diff --git a/PROJECTNAME.PHENOTYPES.txt b/PROJECTNAME.PHENOTYPES.txt new file mode 100644 index 0000000..2de5790 --- /dev/null +++ b/PROJECTNAME.PHENOTYPES.txt @@ -0,0 +1 @@ +PHENOTYPE SAMPLESIZE GWASINPUT diff --git a/SCRIPTS/gsmr/GSMR.plot_effect.R b/SCRIPTS/gsmr/GSMR.plot_effect.R new file mode 100644 index 0000000..2026c29 --- /dev/null +++ b/SCRIPTS/gsmr/GSMR.plot_effect.R @@ -0,0 +1,41 @@ +#!/hpc/local/Rocky8/dhl_ec/software/R-3.6.3/bin/Rscript --vanilla +# SLURM settings +#SBATCH --job-name=gsmr_plotter # Job name +#SBATCH --mail-type=END,FAIL # Mail events (NONE, BEGIN, END, FAIL, ALL) +#SBATCH --mail-user=e.j.a.smulders-2@umcutrecht.nl # Where to send mail +#SBATCH --nodes=1 #run on one node +#SBATCH --ntasks=1 # Run on a single CPU +#SBATCH --mem-per-cpu=10G # Job memory request +#SBATCH --time=1:00:00 # Time limit hrs:min:sec +#SBATCH --output=gsmr_plotter.log # Standard output and error log + +phenotype <- Sys.getenv("PHENOTYPE") +trait <- Sys.getenv("TRAIT") +resultdir <- Sys.getenv("resultdir") +#x_label <- trait # TRAIT corresponds to X_LABEL +#y_label <- phenotype # PHENOTYPE corresponds to Y_LABEL +#color_index <- as.integer(Sys.getenv("COLOR")) +print(phenotype) +print(trait) +# Construct the file path using the variables +file_path1 <- sprintf("%s/gsmr/%s.out.eff_plot.gz", resultdir, trait) +source("/hpc/dhl_ec/esmulders/gsmr/gsmr_plot.r") + + +# Read the data +gsmr_data <- read_gsmr_data(file_path1) +gsmr_summary(gsmr_data) + +file_path2 <- sprintf("%s/gsmr/%s_effect.png", resultdir, trait) + +png(filename = file_path2, width = 480, height = 480) +# Plotting function +plot_gsmr_effect(gsmr_data, trait, phenotype, colors()[630]) + +# Close the device to save the plot +dev.off() + + + + + diff --git a/SCRIPTS/gsmr/_archive/gsmr.analysis (09-07-2024, 15.20).sh b/SCRIPTS/gsmr/_archive/gsmr.analysis (09-07-2024, 15.20).sh new file mode 100644 index 0000000..caafb20 --- /dev/null +++ b/SCRIPTS/gsmr/_archive/gsmr.analysis (09-07-2024, 15.20).sh @@ -0,0 +1,139 @@ +#!/bin/bash + +# SLURM settings +#SBATCH --array=1-50 # create 23 jobs from this +#SBATCH --job-name=gsmr # Job name +#SBATCH --mail-type=FAIL # Mail events (NONE, BEGIN, END, FAIL, ALL) +#SBATCH --mail-user=e.j.a.smulders-2@umcutrecht.nl # Where to send mail +#SBATCH --nodes=1 # Run on one node +#SBATCH --ntasks=1 # Run on a single CPU +#SBATCH --mem=128G # Job memory request +#SBATCH --time=1:00:00 # Time limit hrs:min:sec +#SBATCH --output=gsmr.out # Standard output and error log + +echo "Starting task" $SLURM_ARRAY_TASK_ID + +# Ensure that SLURM_ARRAY_TASK_ID is set +if [ -z "$SLURM_ARRAY_TASK_ID" ]; then + echo "SLURM_ARRAY_TASK_ID is not set." + exit 1 +fi + + +GSMR="/hpc/dhl_ec/esmulders/gsmr" +GCTA="/hpc/local/Rocky8/dhl_ec/software/gcta_v1.94.1" +DIRECTION=0 +REF="${GSMR}/gsmr_ref_data.txt" +#PHENO="META_cox_CHDDEAD_MI_ALL" +#TRAIT_FILE="${GSMR}/gsmr_traits.txt" +PHENO="" +TRAIT_FILE="" +#EXPOSURE_FILE="${GSMR}/gsmr_${trait}_${PHENO}_exposure.txt" + + +# Parsing command-line options +while getopts ":p:t:d:j:r:" opt; do + case ${opt} in + p) PHENO=$OPTARG ;; + t) TRAIT_FILE=$OPTARG ;; + d) DATA=$OPTARG ;; + j) PROJECTNAME=$OPTARG ;; + r) REF=$OPTARG ;; + :) echo "Option -$OPTARG requires an argument." >&2; exit 1 ;; + \?) echo "Invalid option: -$OPTARG" >&2; exit 1 ;; + esac +done + +Total=$(wc -w < "$TRAIT_FILE") # Count total amount of IDs (amount of lines in file) +# Check if the current SLURM_ARRAY_TASK_ID is less than Total +if (( SLURM_ARRAY_TASK_ID < $Total )); then + # Your commands here + echo "Processing task ID $SLURM_ARRAY_TASK_ID" +else + # Cancel the job if SLURM_ARRAY_TASK_ID is not valid + echo "Invalid task ID $SLURM_ARRAY_TASK_ID. Cancelling job." + scancel $SLURM_JOB_ID +fi +echo "Analyzing phenotype ${PHENO}" +echo "${REF}" +#TRAIT_FILE="${GSMR}/gsmr_traits.txt" +#output_dir="${DATA}/${PROJECTNAME}/${PHENO}/gsmr" +#mkdir -p "$output_dir" +#echo "$output_dir" +OUTCOME_FILE="${DATA}/${PROJECTNAME}/${PHENO}/gsmr/gsmr_${PHENO}_outcome.txt" +#echo "$TRAIT_FILE" +#echo "$OUTCOME_FILE" +# Write the outcome to the outcome file +echo "$PHENO ${DATA}/${PROJECTNAME}/${PHENO}/input/${PHENO}.cojo.gz" > "$OUTCOME_FILE" + +# Step 1: Process each trait file for GSMR analysis +echo "Starting GSMR analysis for each trait..." +#mkdir -p "$GSMR/gsmr_$PHENO" + +# while read -r line; do +# # Extract the trait name and file path +# trait=$(echo "$line" | awk '{print $1}') +# file_path=$(echo "$line" | awk '{print $2}') +# EXPOSURE_FILE="${GSMR}/gsmr_${trait}_${PHENO}_exposure.txt" +# # Copy the current trait line to the exposure file +# echo "$line" > "$EXPOSURE_FILE" +# +# +# +# # Define the output file name +# output_file="${GSMR}/gsmr_${PHENO}/gsmr_result_${trait}.out" +# +# # Run GSMR analysis +# echo "Running GSMR analysis for $trait..." +# ${GCTA} --mbfile "$REF" --gsmr-file "$EXPOSURE_FILE" "$OUTCOME_FILE" --gsmr-direction "$DIRECTION" --effect-plot --gsmr2-beta --out "$output_file" +# +# echo "GSMR analysis completed for $trait. Results saved to $output_file." +# done < "$TRAIT_FILE" +# +# # Read the specified line from TRAIT_FILE based on SLURM_ARRAY_TASK_ID +# line=$(sed -n "${SLURM_ARRAY_TASK_ID}p" "$TRAIT_FILE") +# +# # Extract the trait and file path +# trait=$(echo $line | cut -d' ' -f1) +# file_path=$(echo $line | cut -d' ' -f2) + +# Create the output directory if it does not exist + + +# Get the line corresponding to the current SLURM_ARRAY_TASK_ID +line=$(sed -n "${SLURM_ARRAY_TASK_ID}p" "$TRAIT_FILE") + +# Logging +echo "Line extracted: $line" + +# Extract the trait name and file path +trait=$(echo "$line" | awk '{print $1}') +file_path=$(echo "$line" | awk '{print $2}') + + + +# Logging +echo "Trait: $trait" +echo "File Path: $file_path" +EXPOSURE_FILE="${DATA}/${PROJECTNAME}/${PHENO}/gsmr/${trait}_${PHENO}_exposure.txt" + +# Copy the current trait line to the exposure file +echo "$line" > "$EXPOSURE_FILE" + +# Logging +echo "Exposure file content:" +cat "$EXPOSURE_FILE" + +# Define the output file name +#output_file="${GSMR}/gsmr_${PHENO}/gsmr_result_${trait}.out" +output_file="${DATA}/${PROJECTNAME}/${PHENO}/gsmr/${trait}.out" +echo "output file: ${output_file}" +# Run GSMR analysis directly with the trait line +echo "Running GSMR analysis for $trait..." +${GCTA} --mbfile "${REF}" --gsmr-file "${EXPOSURE_FILE}" "${OUTCOME_FILE}" --gsmr-direction "${DIRECTION}" --effect-plot --gsmr2-beta --out "${output_file}" + +echo "GSMR analysis completed for $trait. Results saved to $output_file." + +# Delete the individual exposure file + rm "$EXPOSURE_FILE" +echo "Deleted exposure file: $EXPOSURE_FILE" \ No newline at end of file diff --git a/SCRIPTS/gsmr/gsmr.analysis.sh b/SCRIPTS/gsmr/gsmr.analysis.sh new file mode 100644 index 0000000..3d101f0 --- /dev/null +++ b/SCRIPTS/gsmr/gsmr.analysis.sh @@ -0,0 +1,108 @@ +#!/bin/bash + +# SLURM settings +#SBATCH --array=1-55 # create jobs from this +#SBATCH --job-name=gsmr # Job name +#SBATCH --mail-type=FAIL # Mail events (NONE, BEGIN, END, FAIL, ALL) +#SBATCH --mail-user=e.j.a.smulders-2@umcutrecht.nl # Where to send mail +#SBATCH --nodes=1 # Run on one node +#SBATCH --ntasks=1 # Run on a single CPU +#SBATCH --mem=128G # Job memory request +#SBATCH --time=1:00:00 # Time limit hrs:min:sec +#SBATCH --output=gsmr.out # Standard output and error log + +echo "Starting task" $SLURM_ARRAY_TASK_ID + +# Ensure that SLURM_ARRAY_TASK_ID is set +if [ -z "$SLURM_ARRAY_TASK_ID" ]; then + echo "SLURM_ARRAY_TASK_ID is not set." + exit 1 +fi + + +GSMR="/hpc/dhl_ec/esmulders/MetaGWASToolKit/SCRIPTS/gsmr" +GCTA="/hpc/local/Rocky8/dhl_ec/software/gcta_v1.94.1" +DIRECTION=0 +REF="${GSMR}/gsmr_ref_data.EUR.txt" + +PHENOTYPE="" +TRAIT_FILE="" + + +# Parsing command-line options +while getopts ":p:t:d:j:r:" opt; do + case ${opt} in + p) PHENOTYPE=$OPTARG ;; + t) TRAIT_FILE=$OPTARG ;; + d) DATA=$OPTARG ;; + j) PROJECTNAME=$OPTARG ;; + r) REF=$OPTARG ;; + :) echo "Option -$OPTARG requires an argument." >&2; exit 1 ;; + \?) echo "Invalid option: -$OPTARG" >&2; exit 1 ;; + esac +done + +Total=$(wc -w < "$TRAIT_FILE") # Count total amount of IDs (amount of lines in file) +# Check if the current SLURM_ARRAY_TASK_ID is less than Total +if (( SLURM_ARRAY_TASK_ID < $Total )); then + # Your commands here + echo "Processing task ID $SLURM_ARRAY_TASK_ID" +else + # Cancel the job if SLURM_ARRAY_TASK_ID is not valid + echo "Invalid task ID $SLURM_ARRAY_TASK_ID. Cancelling job." + scancel $SLURM_JOB_ID +fi +echo "Analyzing phenotype ${PHENOTYPE}" +echo "${REF}" + +mkdir -p "${DATA}/gsmr" +OUTCOME_FILE="${DATA}/gsmr/gsmr_${PHENOTYPE}_outcome.txt" +#echo "$TRAIT_FILE" +#echo "$OUTCOME_FILE" +# Write the outcome to the outcome file +echo "$PHENOTYPE ${DATA}/input/${PHENOTYPE}.cojo.gz" > "$OUTCOME_FILE" + +# Step 1: Process each trait file for GSMR analysis +echo "Starting GSMR analysis for each trait..." +#mkdir -p "$GSMR/gsmr_$PHENOTYPE" + +# Get the line corresponding to the current SLURM_ARRAY_TASK_ID +line=$(sed -n "${SLURM_ARRAY_TASK_ID}p" "$TRAIT_FILE") + +# Logging +echo "Line extracted: $line" + +# Extract the trait name and file path +trait=$(echo "$line" | awk '{print $1}') +file_path=$(echo "$line" | awk '{print $2}') + + +# Logging +echo "Trait: $trait" +echo "File Path: $file_path" +EXPOSURE_FILE="${DATA}/gsmr/${trait}_${PHENOTYPE}_exposure.txt" + +# Copy the current trait line to the exposure file +echo "$line" > "$EXPOSURE_FILE" + +# Logging +echo "Exposure file content:" +cat "$EXPOSURE_FILE" + +# Define the output file name +#output_file="${GSMR}/gsmr_${PHENOTYPE}/gsmr_result_${trait}.out" +output_file="${DATA}/gsmr/${trait}.out" +echo "output file: ${output_file}" +# Run GSMR analysis directly with the trait line +echo "Running GSMR analysis for $trait in $PHENOTYPE" +TRAIT="${trait}" +resultdir="${DATA}" +${GCTA} --mbfile "${REF}" --gsmr-file "${EXPOSURE_FILE}" "${OUTCOME_FILE}" --gsmr-direction "${DIRECTION}" --effect-plot --gsmr2-beta --out "${output_file}" +# +echo "GSMR analysis completed for $trait. Results saved to $output_file." + +PHENOTYPE=$PHENOTYPE TRAIT=$trait resultdir=$resultdir Rscript ${GSMR}/GSMR.plot_effect.R + +## Delete the individual exposure file +# rm "$EXPOSURE_FILE" +#echo "Deleted exposure file: $EXPOSURE_FILE" diff --git a/SCRIPTS/gsmr/gsmr_plot.r b/SCRIPTS/gsmr/gsmr_plot.r new file mode 100644 index 0000000..f11a0a1 --- /dev/null +++ b/SCRIPTS/gsmr/gsmr_plot.r @@ -0,0 +1,303 @@ +# ************************************************** # +# Read exposure and outcome # +# ************************************************** # +read_gsmr_trait = function(file_con) { + expo_str = scan(file_con, nlines=1, quiet=TRUE, what=""); + outcome_str = scan(file_con, nlines=1, quiet=TRUE, what=""); + strbuf = scan(file_con, nlines=1, quiet=TRUE, what=""); + return(list(expo_str=expo_str, outcome_str=outcome_str)) +} + +# ************************************************** # +# Read GSMR result # +# ************************************************** # +read_gsmr_result = function(file_con) { + expo_str = outcome_str = bxy = bxy_se = bxy_pval = bxy_m = c() + while(1) { + strbuf = scan(file_con, nlines=1, quiet=TRUE, what=""); + if(strbuf[1] == "#gsmr_end") break; + if(strbuf[1] == "Exposure") next; + expo_str = c(expo_str, strbuf[1]); + outcome_str = c(outcome_str, strbuf[2]); + bxy = c(bxy, as.numeric(strbuf[3])); + bxy_se = c(bxy_se, as.numeric(strbuf[4])); + bxy_pval = c(bxy_pval, as.numeric(strbuf[5])); + bxy_m = c(bxy_m, as.numeric(strbuf[6])); + } + return(cbind(expo_str, outcome_str, bxy, bxy_se, bxy_pval, bxy_m)) +} + +# ************************************************** # +# Read SNP effects # +# ************************************************** # +read_snp_effect = function(file_con) { + snp_effect = c() + while(1) { + strbuf = scan(file_con, nlines=1, quiet=TRUE, what=""); + if(strbuf[1] == "#effect_end") break; + snp_effect = rbind(snp_effect, strbuf); + print(length(strbuf)) + if(length(strbuf)<14) print(strbuf) + } + return(snp_effect) +} + +# ************************************************** # +# Read SNP instruments # +# ************************************************** # +read_snp_instru = function(file_con, snplist, nexpo, noutcome) { + nrow = length(snplist); ncol = nexpo+noutcome + snp_instru = matrix(NA, nrow, ncol) + while(1) { + strbuf = scan(file_con, nlines=1, quiet=TRUE, what=""); + if(strbuf[1] == "#marker_end") break; + expo_indx = as.numeric(strbuf[1]); outcome_indx = as.numeric(strbuf[2]); + forward_flag = TRUE; + if(expo_indx < outcome_indx) { + outcome_indx = outcome_indx - nexpo + } else { + expo_indx = expo_indx - nexpo + forward_flag = FALSE; + } + snpbuf = scan(file_con, nlines=1, quiet=TRUE, what=""); + snp_indx = match(snpbuf, snplist) + posbuf = rep(0, nrow); posbuf[snp_indx] = 1; + indxbuf = expo_indx + if(!forward_flag) indxbuf = indxbuf + nexpo + if(length(which(!is.na(snp_instru[,indxbuf])))==0) { + snp_instru[,indxbuf] = posbuf; + } else { + snp_instru[,indxbuf] = paste(snp_instru[,indxbuf], posbuf, sep="") + } + } + return(snp_instru) +} + +# ************************************************** # +# Read output by GCTA-GSMR for plot # +# ************************************************** # +read_gsmr_data = function(gsmr_effect_file) { + trait_flag = gsmr_flag = marker_flag = effect_flag = FALSE; + file_con = file(gsmr_effect_file, "r") + while(1) { + strbuf = scan(file_con, nlines=1, quiet=TRUE, what=""); + if(strbuf == "#trait_begin") { + # Read the exposures and outcomes + resbuf = read_gsmr_trait(file_con); + expo_str = resbuf$expo_str; + outcome_str = resbuf$outcome_str; + pheno_str = c(expo_str, outcome_str); + nexpo = length(expo_str); noutcome = length(outcome_str) + trait_flag = TRUE; + } else if(strbuf == "#gsmr_begin") { + # Read the GSMR result + bxy_result = read_gsmr_result(file_con); + colnames(bxy_result) = c("Exposure", "Outcome", "bxy", "se", "p", "n_snps") + gsmr_flag = TRUE; + } else if(strbuf == "#effect_begin") { + # Read the summary statistics + snp_effect = read_snp_effect(file_con); + snplist = as.character(snp_effect[,1]) + effect_flag = TRUE; + } else if(strbuf == "#marker_begin") { + # Read the SNPs + snp_instru = read_snp_instru(file_con, snplist, nexpo, noutcome); + snp_effect = cbind(snp_effect[,1], snp_instru, snp_effect[,-1]) + marker_flag = TRUE; + } + if(trait_flag==T & gsmr_flag==T & marker_flag==T & effect_flag==T) break; + } + return(list(pheno=c(nexpo, noutcome, pheno_str), bxy_result=bxy_result, snp_effect = snp_effect)) +} + +# ************************************************** # +# Display summary of the gsmr data # +# ************************************************** # +gsmr_summary = function(gsmr_data) { + message("\n## Exposure and outcome") + pheno_str = gsmr_data$pheno[c(-1,-2)] + # exposure + nexpo = as.numeric(gsmr_data$pheno[1]); + noutcome = as.numeric(gsmr_data$pheno[2]); + logger_m = paste(nexpo, "exposure(s):"); + logger_m = paste(logger_m, gsmr_data$pheno[3]) + if(nexpo > 1) { + for(i in 2 : nexpo) + logger_m = paste(logger_m, gsmr_data$pheno[i+2], sep=", ") + } + message(logger_m) + # outcome + logger_m = paste(noutcome, "outcome(s):"); + logger_m = paste(logger_m, gsmr_data$pheno[3+nexpo]) + if(noutcome > 1) { + for(i in 2 : noutcome) + logger_m = paste(logger_m, gsmr_data$pheno[i+2+nexpo], sep=", ") + } + message(logger_m) + + message("\n## GSMR result") + m_bxy_rst = data.frame(gsmr_data$bxy_result) + print(m_bxy_rst) +} + + +# ************************************************** # +# Retrieve SNP effects # +# ************************************************** # +gsmr_snp_effect = function(gsmr_data, expo_str, outcome_str) { + # index of SNP instruments + pheno_str = as.character(gsmr_data$pheno[c(-1,-2)]) + nexpo = as.numeric(gsmr_data$pheno[1]) + noutcome = as.numeric(gsmr_data$pheno[2]) + expo_indx = match(expo_str, pheno_str) + if(is.na(expo_indx)) stop("\"", expo_str, "\" is not found.") + outcome_indx = match(outcome_str, pheno_str) + if(is.na(outcome_indx)) stop("\"", outcome_str, "\" is not found.") + forward_flag = TRUE; + if(expo_indx > outcome_indx) forward_flag = FALSE; + if(forward_flag) { + outcome_indx = outcome_indx - nexpo; + } else { + expo_indx = expo_indx - nexpo; + } + indxbuf = expo_indx + 1 + if(!forward_flag) indxbuf = indxbuf + nexpo + strbuf = as.character(substr(gsmr_data$snp_effect[,indxbuf], outcome_indx, outcome_indx)) + snpindx = which(strbuf=="1") + if(length(snpindx) < 3) stop("Not enough SNPs retained.") + # bxy + indxbuf = which(gsmr_data$bxy_result[,1]==expo_str & gsmr_data$bxy_result[,2]==outcome_str) + bxy = as.numeric(gsmr_data$bxy_result[indxbuf, 3]) + # SNP effects + if(forward_flag) { + indxbuf1 = 1 + nexpo + noutcome + 3 + (expo_indx-1)*2 + 1 + indxbuf2 = 1 + nexpo + noutcome + 3 + nexpo*2 + (outcome_indx-1)*2 + 1 + } else { + indxbuf1 = 1 + nexpo + noutcome + 3 + nexpo*2 + (expo_indx-1)*2 + 1 + indxbuf2 = 1 + nexpo + noutcome + 3 + (outcome_indx-1)*2 + 1 + } + snpid = as.character(gsmr_data$snp_effect[snpindx,1]) + bzx = as.numeric(gsmr_data$snp_effect[snpindx,indxbuf1]); indxbuf1 = indxbuf1 + 1; + bzx_se = as.numeric(gsmr_data$snp_effect[snpindx,indxbuf1]); + bzx_pval = pchisq((bzx/bzx_se)^2, 1, lower.tail=F); + bzy = as.numeric(gsmr_data$snp_effect[snpindx,indxbuf2]); indxbuf2 = indxbuf2 + 1; + bzy_se = as.numeric(gsmr_data$snp_effect[snpindx,indxbuf2]); + bzy_pval = pchisq((bzy/bzy_se)^2, 1, lower.tail=F); + return(list(snp=snpid, bxy=bxy, bzx=bzx, bzx_se=bzx_se, bzx_pval=bzx_pval, bzy=bzy, bzy_se=bzy_se, bzy_pval=bzy_pval)) +} + +# ************************************************** # +# Plot bzy vs bzx # +# ************************************************** # +plot_snp_effect = function(expo_str, outcome_str, bxy, bzx, bzx_se, bzy, bzy_se, effect_col=colors()[75]) { + vals = c(bzx - bzx_se, bzx + bzx_se) + xmin = min(vals); xmax = max(vals) + vals = c(bzy - bzy_se, bzy + bzy_se) + ymin = min(vals); ymax = max(vals) + + plot(bzx, bzy, pch=20, cex=0.8, bty="n", cex.axis=1.1, cex.lab=1.2, + col=effect_col, xlim=c(xmin, xmax), ylim=c(ymin, ymax), + xlab=substitute(paste(trait, " (", italic(b[zx]), ")", sep=""), list(trait=expo_str)), + ylab="", # Remove ylab from here + main="") + + # Add the y-axis title with adjusted margin + title(ylab=substitute(paste(trait, " (", italic(b[zy]), ")", sep=""), list(trait=outcome_str)), + mgp=c(2, 1, 0)) # Adjust the first value as needed + + if(!is.na(bxy)) abline(0, bxy, lwd=1.5, lty=2, col="dim grey") + + ## Standard errors + nsnps = length(bzx) + for(i in 1:nsnps) { + # x axis + xstart = bzx[i] - bzx_se[i]; xend = bzx[i] + bzx_se[i] + ystart = bzy[i]; yend = bzy[i] + segments(xstart, ystart, xend, yend, lwd=1.5, col=effect_col) + # y axis + xstart = bzx[i]; xend = bzx[i] + ystart = bzy[i] - bzy_se[i]; yend = bzy[i] + bzy_se[i] + segments(xstart, ystart, xend, yend, lwd=1.5, col=effect_col) + } +} + + +# ************************************************** # +# Plot bzy_pval vs bzx_pval # +# ************************************************** # +plot_snp_pval = function(expo_str, outcome_str, bzx_pval, bzy_pval, gwas_thresh, truncation, effect_col) { + eps = 1e-300; truncation = -log10(truncation); + if(truncation > 300) { + warning("The minimal truncated p-value would be 1e-300.") + truncation = 300 + } + bzx_pval = -log10(bzx_pval + eps); + bzy_pval = -log10(bzy_pval + eps); + pval = c(bzx_pval, bzy_pval) + min_val = 0; max_val = max(pval); + max_val = ifelse(max_val > truncation, truncation, max_val) + gwas_thresh = -log10(gwas_thresh); + plot(bzx_pval, bzy_pval, pch=20, cex=0.8, bty="n", cex.axis=1.1, cex.lab=1.2, + col=effect_col, xlim=c(min_val, max_val), ylim=c(min_val, max_val), + xlab=substitute(paste(trait, " (", -log[10], italic(P)[zx], ")", sep=""), list(trait=expo_str)), + ylab=substitute(paste(trait, " (", -log[10], italic(P[zy]), ")", sep=""), list(trait=outcome_str))) + abline(h=gwas_thresh, lty=2, lwd=1.5, col="maroon") +} + +# ************************************************** # +# Plot bxy vs bzx_pval # +# ************************************************** # +plot_snp_bxy = function(expo_str, outcome_str, bxy, bzx_pval, effect_col) { + eps = 1e-300; + bzx_pval = -log10(bzx_pval + eps); + xmin = min(bxy, na.rm=T); xmax = max(bxy, na.rm=T) + ymin = min(bzx_pval); ymax = max(bzx_pval); + plot(bxy, bzx_pval, pch=20, cex=0.8, bty="n", cex.axis=1.1, cex.lab=1.2, + col=effect_col, xlim=c(xmin, xmax), ylim=c(ymin, ymax), + xlab=substitute(paste(italic(hat(b)[xy]), " (", trait1, " -> ", trait2, ")", sep=""), list(trait1=expo_str, trait2=outcome_str)), + ylab=substitute(paste(trait, " (", -log[10], italic(P[zx]), ")", sep=""), list(trait=expo_str))) +} + +# ************************************************** # +# Effect size plot # +# ************************************************** # +# expo_str, exposure +# outcome_str, outcome +# effect_col, plotting colour +plot_gsmr_effect = function(gsmr_data, expo_str, outcome_str, effect_col=colors()[75]) { + resbuf = gsmr_snp_effect(gsmr_data, expo_str, outcome_str); + bxy = resbuf$bxy + bzx = resbuf$bzx; bzx_se = resbuf$bzx_se; + bzy = resbuf$bzy; bzy_se = resbuf$bzy_se; + # plot + plot_snp_effect(expo_str, outcome_str, bxy, bzx, bzx_se, bzy, bzy_se, effect_col) +} + +# ************************************************** # +# P-value plot # +# ************************************************** # +# expo_str, exposure +# outcome_str, outcome +# effect_col, plotting colour +plot_gsmr_pvalue = function(gsmr_data, expo_str, outcome_str, gwas_thresh=5e-8, truncation=1e-50, effect_col=colors()[75]) { + resbuf = gsmr_snp_effect(gsmr_data, expo_str, outcome_str); + bzx_pval = resbuf$bzx_pval; bzy_pval = resbuf$bzy_pval; + # plot + plot_snp_pval(expo_str, outcome_str, bzx_pval, bzy_pval, gwas_thresh, truncation, effect_col) +} + +# ************************************************** # +# bxy distribution plot # +# ************************************************** # + +# expo_str, exposure +# outcome_str, outcome +# effect_col, plotting colour +plot_bxy_distribution = function(gsmr_data, expo_str, outcome_str, effect_col=colors()[75]) { + resbuf = gsmr_snp_effect(gsmr_data, expo_str, outcome_str); + bzx = resbuf$bzx; bzx_pval = resbuf$bzx_pval; + bzy = resbuf$bzy; + bxy = bzy/bzx + # plot + plot_snp_bxy(expo_str, outcome_str, bxy, bzx_pval, effect_col) +} diff --git a/SCRIPTS/gsmr/gsmr_ref_data.AFR.txt b/SCRIPTS/gsmr/gsmr_ref_data.AFR.txt new file mode 100644 index 0000000..827eab9 --- /dev/null +++ b/SCRIPTS/gsmr/gsmr_ref_data.AFR.txt @@ -0,0 +1,22 @@ +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.AFR.chr1 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.AFR.chr2 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.AFR.chr3 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.AFR.chr4 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.AFR.chr5 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.AFR.chr6 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.AFR.chr7 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.AFR.chr8 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.AFR.chr9 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.AFR.chr10 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.AFR.chr11 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.AFR.chr12 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.AFR.chr13 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.AFR.chr14 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.AFR.chr15 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.AFR.chr16 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.AFR.chr17 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.AFR.chr18 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.AFR.chr19 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.AFR.chr20 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.AFR.chr21 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.AFR.chr22 \ No newline at end of file diff --git a/SCRIPTS/gsmr/gsmr_ref_data.EAS.txt b/SCRIPTS/gsmr/gsmr_ref_data.EAS.txt new file mode 100644 index 0000000..b52be47 --- /dev/null +++ b/SCRIPTS/gsmr/gsmr_ref_data.EAS.txt @@ -0,0 +1,22 @@ +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EAS.chr1 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EAS.chr2 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EAS.chr3 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EAS.chr4 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EAS.chr5 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EAS.chr6 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EAS.chr7 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EAS.chr8 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EAS.chr9 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EAS.chr10 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EAS.chr11 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EAS.chr12 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EAS.chr13 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EAS.chr14 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EAS.chr15 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EAS.chr16 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EAS.chr17 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EAS.chr18 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EAS.chr19 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EAS.chr20 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EAS.chr21 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EAS.chr22 \ No newline at end of file diff --git a/SCRIPTS/gsmr/gsmr_ref_data.EUR.txt b/SCRIPTS/gsmr/gsmr_ref_data.EUR.txt new file mode 100644 index 0000000..59db3d4 --- /dev/null +++ b/SCRIPTS/gsmr/gsmr_ref_data.EUR.txt @@ -0,0 +1,22 @@ +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EUR.chr1 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EUR.chr2 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EUR.chr3 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EUR.chr4 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EUR.chr5 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EUR.chr6 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EUR.chr7 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EUR.chr8 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EUR.chr9 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EUR.chr10 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EUR.chr11 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EUR.chr12 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EUR.chr13 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EUR.chr14 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EUR.chr15 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EUR.chr16 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EUR.chr17 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EUR.chr18 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EUR.chr19 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EUR.chr20 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EUR.chr21 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.EUR.chr22 \ No newline at end of file diff --git a/SCRIPTS/gsmr/gsmr_ref_data.SAS.txt b/SCRIPTS/gsmr/gsmr_ref_data.SAS.txt new file mode 100644 index 0000000..b924ee2 --- /dev/null +++ b/SCRIPTS/gsmr/gsmr_ref_data.SAS.txt @@ -0,0 +1,22 @@ +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.SAS.chr1 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.SAS.chr2 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.SAS.chr3 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.SAS.chr4 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.SAS.chr5 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.SAS.chr6 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.SAS.chr7 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.SAS.chr8 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.SAS.chr9 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.SAS.chr10 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.SAS.chr11 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.SAS.chr12 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.SAS.chr13 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.SAS.chr14 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.SAS.chr15 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.SAS.chr16 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.SAS.chr17 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.SAS.chr18 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.SAS.chr19 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.SAS.chr20 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.SAS.chr21 +/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format/1000Gp3v5.20130502.SAS.chr22 \ No newline at end of file diff --git a/SCRIPTS/gsmr/gsmr_traits.txt b/SCRIPTS/gsmr/gsmr_traits.txt new file mode 100644 index 0000000..6cb8ce3 --- /dev/null +++ b/SCRIPTS/gsmr/gsmr_traits.txt @@ -0,0 +1,49 @@ +BMI /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/BMI.cojo.gz +T2D_EU /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/T2D_EU.cojo.gz +IQ /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/IQ.cojo.gz +BIP /hpc/dhl_ec/esmulders/cojo/BIP.cojo.gz +PP /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/PP.cojo.gz +T2DadjBMI /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/T2DadjBMI.cojo.gz +Insomnia /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/Insomnia.cojo.gz +cIMT /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/cIMT.cojo.gz +EA /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/EA.cojo.gz +CAD /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/CAD.cojo.gz +HDL /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/HDL.cojo.gz +TG /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/TG.cojo.gz +AD /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/AD.cojo.gz +SBP /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/SBP.cojo.gz +DBP /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/DBP.cojo.gz +BC /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/BC.cojo.gz +MILLIONHEARTS /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/MILLIONHEARTS.cojo.gz +AS /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/AS.cojo.gz +FmrSmk /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/FmrSmk.cojo.gz +IS /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/IS.cojo.gz +CpD /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/CpD.cojo.gz +EvrSmk /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/EvrSmk.cojo.gz +TG /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/TG.cojo.gz +HF /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/HF.cojo.gz +FNBMD /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/FNBMD.cojo.gz +NICM /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/NICM.cojo.gz +Plaque /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/Plaque.cojo.gz +AF /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/AF.cojo.gz +SVD /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/SVD.cojo.gz +CKD /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/CKD.cojo.gz +IBD /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/IBD.cojo.gz +logOnset /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/logOnset.cojo.gz +DS /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/DS.cojo.gz +CAC_EA /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/CAC_EA.cojo.gz +PD /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/PD.cojo.gz +CES /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/CES.cojo.gz +LAS /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/LAS.cojo.gz +Neuroticism /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/Neuroticism.cojo.gz +Na /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/Na.cojo.gz +TC /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/TC.cojo.gz +FABMD /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/FABMD.cojo.gz +SWB /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/SWB.cojo.gz +K /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/K.cojo.gz +LSBMD /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/LSBMD.cojo.gz +ASD /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/ASD.cojo.gz +LDL /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/LDL.cojo.gz +TAGC /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/TAGC.cojo.gz +CAC /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/CAC.cojo.gz +PrCa /hpc/dhl_ec/data/_gwas_datasets/_cojo/rsid/PrCa.cojo.gz diff --git a/SCRIPTS/gwas.parser.R b/SCRIPTS/gwas.parser.R index 81406af..1f0ee93 100755 --- a/SCRIPTS/gwas.parser.R +++ b/SCRIPTS/gwas.parser.R @@ -285,29 +285,31 @@ if(!is.na(opt$projectdir) & !is.na(opt$datagwas) & !is.na(opt$outputdir)) { ### Selecting the columns we want cat("\n* Selecting required columns, and creating them if not present...") - VectorOfColumnsWeWant <- c("^marker$", "^snp$", "^rsid$", "^snpid$", "^id$", # variant ID + VectorOfColumnsWeWant <- c("^marker$", "^snp$", "^rsid$", "^snpid$", "^id$", # variant ID "^chr$", "^chrom$", "^chromosome$", # chromosome "^position$", "^bp$", "^genpos$","^pos$", # Variant position on chromosomes "^effect[_]allele$", "^minor[_]allele$", "^risk[_]allele$", "^coded[_]allele$", # Effect/Minor allele "^effectallele$", "^minorallele$", "^riskallele$", "^codedallele$", "^allele1$", # Effect/Minor allele "^other[_]allele$", "^major[_]allele$", "^non[_]effect[_]allele$", "^non[_]coded[_]allele$", # Other/Major allele - "^otherallele$", "^majorallele$", "^noneffectallele$", "^noncodedallele$", "^allele0$", # Other/Major allele + "^otherallele$", "^majorallele$", "^noneffectallele$", "^noncodedallele$", "^noncoded_allele$", "^allele0$", # Other/Major allele "^strand$", # chromosomal strand - "^beta$", "^effect[_]size$", "^effectsize$", # effect size estimates, should be beta - "^se.$", "^se$", # Standard error - "^p.value$", "^p$", "^p.val$", "^pvalue$", "^pval$", "^p_bolt_lmm_inf$", # p-value - "^[remc]af$", "^a1freq$", # effect/minor allele frequency + "^beta$", "^effect[_]size$", "^effectsize$", "^beta_fixed$", # effect size estimates, should be beta + "^se.$", "^se$", "^se_fixed$", # Standard error + "^p.value$", "^p$", "^p.val$", "^pvalue$", "^pval$", "^p_bolt_lmm_inf$", "^p_fixed$", # p-value + "^[remc]af$", "^a1freq$", "^coded_allele_freq$", "^eaf$", # effect/minor allele frequency "^hwe.value$", "^hwe$", "^hwe.val$", # Hardy-Weinberg Equilibrium p-value - "^n$", "^samplesize$", # Number of samples analysed - "^n_case.$", "^n_control.$", "^n_cntrl.$", # Number of samples analysed in GWAS in case-control set up + "^n$", "^samplesize$", "^n_eff$", # Number of samples analysed + "^n[_]case.$", "^ncase$", "^n[_]control.$","^ncontrol$", "^n_cntrl.$", # Number of samples analysed in GWAS in case-control set up "^imputed$", # Imputed or genotyped? - "^info$", "^qual[_]score$") # Imputation quality + "^info$", "^qual[_]score$", # Imputation quality + "^df$", #degrees of freedom + "^caveat$") #caveat matchExpression <- paste(VectorOfColumnsWeWant, collapse = "|") - # print(head(GWASDATA_RAW)) - # str(GWASDATA_RAW) - - GWASDATA_RAWSELECTION <- GWASDATA_RAW %>% select(matches(matchExpression, ignore.case = TRUE)) - + # print(head(GWASDATA_RAW)) + str(GWASDATA_RAW) +# + GWASDATA_RAWSELECTION <- GWASDATA_RAW %>% select(matches(matchExpression, ignore.case = TRUE)) +# # print(names(GWASDATA_RAWSELECTION)) cat("\n* Renaming columns where necessary...") @@ -325,6 +327,8 @@ if(!is.na(opt$projectdir) & !is.na(opt$datagwas) & !is.na(opt$outputdir)) { ### - noncoded = noneffect = nonrisk = other -- will be coded as "OtherAllele" ### Set these three accordingly, other wise set these to CAF/coded/other ### +# + ### Rename columns -- strand GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, Strand = matches("^strand$"), everything()) @@ -336,9 +340,12 @@ if(!is.na(opt$projectdir) & !is.na(opt$datagwas) & !is.na(opt$outputdir)) { ### Rename columns -- n cases and controls GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, N_controls = matches("^n_control.$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, N_controls = matches("^ncontrol$"), everything()) GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, N_controls = matches("^n_ctrl.$"), everything()) GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, N_cases = matches("^n_case.$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, N_cases = matches("^ncase$"), everything()) + ### Rename columns -- sample size GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, N = matches("^n$"), everything()) GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, N = matches("^samplesize$"), everything()) diff --git a/SCRIPTS/gwaslab_sumstats.py b/SCRIPTS/gwaslab_sumstats.py new file mode 100644 index 0000000..4a6a8a2 --- /dev/null +++ b/SCRIPTS/gwaslab_sumstats.py @@ -0,0 +1,427 @@ +# Import packages +import os +import glob +import importlib +import sys +from subprocess import check_output +import polars as pl +import gwaslab as gl +import argparse +from datetime import datetime +import pandas as pd +import pyarrow as pa +from scipy import stats +import numpy as np +import cmcrameri as ccm +from cmcrameri import cm +import matplotlib +import matplotlib.pyplot as plt +import seaborn as sns +import liftover +from liftover import get_lifter + +parser = argparse.ArgumentParser(description="Parser commands.") +# parser.add_argument("-i", "--identifier", help="The VariantID identifier to use (" + ", ".join(["VariantID"] + alt_ids) + ").", type=str) +# parser.add_argument("-s", "--subsample", help="Number of rows to use for subsampling, when determining the optimal VariantID (default = 100,000)", type=int) + +requiredNamed = parser.add_argument_group('required named arguments') + +requiredNamed.add_argument("-g", "--gwas", help="The name of the GWAS study.", type=str) +requiredNamed.add_argument("-i", "--input", help="The path and name of the parsed input file.", type=str) +requiredNamed.add_argument("-d", "--directory", help="The path to the results directory.", type=str) +requiredNamed.add_argument("-r", "--reference", help="The path to the references directory.", type=str) +requiredNamed.add_argument("-p", "--population", help="Population analysed.", type=str) +requiredNamed.add_argument("-f", "--figures", help="Make plots or not?(YES or NO).", type=str) +requiredNamed.add_argument("-q", "--qc", help="Perform Quality Control or not?(YES or NO).", type=str) +requiredNamed.add_argument("-n", "--onlyqc", help="Perform ONLY Quality Control or not? pickle file has to exist! (YES or NO).", type=str) +requiredNamed.add_argument("-l", "--leads", help="select lead SNPs and safe in file?(YES or NO).", type=str) +#requiredNamed.add_argument("-o", "--output", help="File name for the output file to store the results.", type=str) +args = parser.parse_args() + +#reference_identifier = args.identifier +#### set some general defaults + +PHENOTYPE = args.gwas +#PHENOTYPE = "cox_DEAD_ALL" # option +SUBSTUDY_PHENO = f"META_{PHENOTYPE}" + +POPULATION = args.population + +#POPULATION = "EUR" # option + +perform_qc = args.qc + +select_leads= args.leads +only_qc= args.onlyqc +# Reference data directory +REF_loc = args.reference +gl.options.set_option("data_directory",f"{REF_loc}") + +#REF_loc = "/hpc/dhl_ec/esmulders/references" +print("Checking contents of the reference directory:") +print(check_output(["ls", os.path.join(REF_loc)]).decode("utf8")) + +# GWAS data directory + +GWAS_RES_loc = args.directory +# print("Checking contents of the GWAS results directory:") +# print(check_output(["ls", os.path.join(GWAS_RES_loc)]).decode("utf8")) +# GWAS_RES_loc = "/hpc/dhl_ec/svanderlaan/projects/consortia/CHARGE_cIMT_Sex/CHARGE_cIMT_EUR/cimt_eur/META/" +# print("Checking contents of the GWAS results directory:") +# print(check_output(["ls", os.path.join(GWAS_RES_loc)]).decode("utf8")) + +# GWAS_DATASETS_loc = "/hpc/dhl_ec/esmulders/GENIUS_CHD/gwaslab/" +# print("Checking contents of the GWAS Datasets directory:") +# print(check_output(["ls", os.path.join(GWAS_DATASETS_loc)]).decode("utf8")) + +# Check if the GWASCatalog directory exists within GWAS_RES_loc +if not os.path.exists(os.path.join(GWAS_RES_loc, "GWASCatalog")): + # If it doesn't exist, create it + os.makedirs(os.path.join(GWAS_RES_loc, "GWASCatalog")) + +# GWAS Catalog directory +GWASCatalog_loc = os.path.join(GWAS_RES_loc, "GWASCatalog/") + +# # List the files in the GWASCatalog directory +# files = os.listdir(GWASCatalog_loc) +# print("Files in GWASCatalog directory:", files) + +print(check_output( + ["ls", os.path.join(GWASCatalog_loc)]).decode("utf8")) + +# general plotting directory + +make_plots=args.figures +# Check if the directory exists +if not (os.path.join(GWAS_RES_loc, "PLOTS")): + # If it doesn't exist, create it + os.makedirs(os.path.join(GWAS_RES_loc, "PLOTS")) +PLOTS_loc = os.path.join(GWAS_RES_loc, "PLOTS/") + +# regional association plots directory +REG_PLOTS_loc = PLOTS_loc + "/Regional_Association_Plots" + +# Check if the directory exists +if not os.path.exists(REG_PLOTS_loc): + # If it doesn't exist, create it + os.makedirs(REG_PLOTS_loc) + +if only_qc=="YES": + gwas_data_sumstats = gl.load_pickle( + os.path.join( + os.path.join(GWASCatalog_loc, f"{PHENOTYPE}.b37.gwaslab.pkl"), + ) +) + gwas_data_sumstats.data +### Load data +if only_qc=="NO": + temp = pl.read_csv( + source=os.path.join( + GWAS_RES_loc + + "/meta.results." + PHENOTYPE + ".1Gp3." + POPULATION + ".summary.txt.gz", + ), + has_header=True, + separator=" ", + ignore_errors=False, + # n_rows=1000, # for debugging + quote_char=None, + # necessary to fix issues with missing values when reading data + null_values=["NA"], + # There is an error at import (from temp to pandas()): + # Could not parse `X` as dtype `i64` at column 'CHR' (column number 2) + # https://stackoverflow.com/questions/75797640/how-to-specify-column-types-in-python-polars-read-csv + # https://stackoverflow.com/questions/71790235/switching-between-dtypes-within-a-dataframe + # https://pola-rs.github.io/polars/user-guide/concepts/data-types/ + dtypes={"CHR": pl.Utf8}, +) + + +# change polars dataframe to pandas dataframe + gwas_data = temp.to_pandas() + del temp + + + if make_plots == "YES": + # CAF plot + plt.figure() + sns.histplot( + data=gwas_data, + x="CAF", + bins=25, + kde=False, + stat="frequency", + color="#1290D9", + ) + plt.title("Histogram of Coded Allele Frequency") + plt.savefig( + os.path.join(PLOTS_loc, f"histogram.EAF.{PHENOTYPE}.png"), + dpi=300, + bbox_inches="tight", + format="png", + ) + plt.close() + + # BETA plot + plt.figure() + sns.histplot( + data=gwas_data, + x="BETA_FIXED", + bins=25, + kde=False, + stat="frequency", + color="#E55738", + ) + plt.title("Histogram of Beta Fixed") + plt.savefig( + os.path.join(PLOTS_loc, f"histogram.effect.{PHENOTYPE}.png"), + dpi=300, + bbox_inches="tight", + format="png", + ) + plt.close() + + +### FIX COLUMNS +# Convert CHR column to string type to handle non-numeric chromosomes + gwas_data["CHR"] = gwas_data["CHR"].astype(str) + +# Optionally map non-numeric chromosomes to integers if required + chromosome_mapping = { + 'X': '23', + 'Y': '24', + 'MT': '25' +} + + gwas_data["CHR"] = gwas_data["CHR"].replace(chromosome_mapping) + +# Now you can convert it to an integer type + gwas_data["CHR"] = gwas_data["CHR"].astype("Int64") + + + gwas_data[["POS"]] = gwas_data[["POS"]].astype("Int64") + +# create new SNPID column based on chromosome, position, and alleles +# down the road we need an SNPID column to merge with the reference data and which does not contain 'ID' because this is not correctly interpreted by GWASLab + gwas_data["SNPID"] = ( + gwas_data["CHR"].astype(str) + + ":" + + gwas_data["POS"].astype(str) + + ":" + + gwas_data["OTHERALLELE"].astype(str) + + ":" + + gwas_data["CODEDALLELE"].astype(str) +) + + gwas_data.rename(columns={"SNP": "VariantID"}, inplace=True) + +# Create CAVEAT column + gwas_data['CAVEAT'].fillna('None', inplace=True) + + + +### GWASLAB - create variable +# Specify the columns: + gwas_data_sumstats = gl.Sumstats( + gwas_data, + snpid="VARIANTID", + # rsid="RSID", # not available + chrom="CHR", + pos="POS", + ea="CODEDALLELE", + nea="OTHERALLELE", + eaf="CAF", + beta="BETA_FIXED", + se="SE_FIXED", + p="P_FIXED", + # direction="Direction", # only for meta-GWAS + n="N_EFF", + # info="Info", # not available + other=[ + "DF", + "DIRECTIONS", + "P_COCHRANS_Q", + "I_SQUARED", + "CAVEAT", + ], + build="19", + verbose=True, +) +# clean up + del gwas_data + + +# Execute `basic_check` function - first we just make sure the data has the expected format, columns, and datatypes. +# full data + gwas_data_sumstats.basic_check(verbose=True) + +# Remove duplicates + gwas_data_sumstats.remove_dup( + mode="md", # remove multi-allelic and duplicate variants + remove=False, # remove NAs + keep_col="P", + keep_ascend=True, + # keep the first variant, with the lowest p-value (sorted by that column) + keep="first", +) + + +# .check_ref(): Check if NEA is aligned with the reference sequence. After checking, the tracking status code will be changed accordingly. + +# full dataset + gwas_data_sumstats.check_ref( + ref_seq=REF_loc + "hg19.fa", + # chr_dict=gl.get_number_to_NC(build="19") +) + +# # full dataset +# gwas_data_sumstats.check_ref( +# ref_seq=REF_loc + "hg19.fa", +# # chr_dict=gl.get_number_to_NC(build="19") +# ) + +# we make sure to flip the alleles based on the status code + gwas_data_sumstats.flip_allele_stats() +# infer strand for palindromic SNPs/align indistinguishable indels + gwas_data_sumstats.infer_strand( + ref_infer= REF_loc + f"{POPULATION}.ALL.split_norm_af.1kgp3v5.hg19.vcf.gz", + ref_alt_freq="AF", + n_cores=8 +) + gwas_data_sumstats.flip_allele_stats() + + +# Assign rsID by matching SNPID with CHR:POS:REF:ALT in the reference + gwas_data_sumstats.assign_rsid( + n_cores=8, + # this works for common variants + ref_rsid_tsv=REF_loc + "/1kg_dbsnp151_hg19_auto.txt.gz", + # ref_rsid_vcf=gl.get_path("dbsnp_v156_hg19"), + chr_dict=gl.get_number_to_NC( + build="19" + ), # this is needed as in the VCF file, the chromosome is in NC format +) + +# Check if SNPIDs are correct + gwas_data_sumstats.fix_id( + fixid=True, + forcefixid=True, + overwrite=True, +) + + gwas_data_sumstats.check_af( + ref_infer=REF_loc + f"{POPULATION}.ALL.split_norm_af.1kgp3v5.hg19.vcf.gz", + ref_alt_freq="AF", + n_cores=8, +) +#plot allele frequency comparison plot against reference + gwas_data_sumstats.plot_daf(threshold=0.12) + + +# select caveats in dataset + temp = gwas_data_sumstats.data["CAVEAT"].value_counts() + + temp.to_csv( + os.path.join( + GWASCatalog_loc + PHENOTYPE + ".b37.counts_caveats.csv", + ) +) + del temp + + gl.dump_pickle( + gwas_data_sumstats, + os.path.join( + GWASCatalog_loc + PHENOTYPE + ".b37.gwaslab.pkl", + ), + overwrite=True, +) + + gwas_data_sumstats.log.show() + + gwas_data_sumstats.log.save( + os.path.join( + GWASCatalog_loc + PHENOTYPE + ".b37.gwaslab.log", + ) +) + + gwas_data_sumstats.to_format( + os.path.join(GWASCatalog_loc + PHENOTYPE + ".b37.gwaslab"), + fmt="ssf", + build="19", +) + + +# manhattan and qq plot +if make_plots == "YES": + gwas_data_sumstats.plot_daf(threshold=0.12) + # Manhattan and QQ plot + gwas_data_sumstats.plot_mqq( + skip=2, + cut=10, + sig_line=True, + sig_level=5e-8, + anno="GENENAME", + anno_style="right", + windowsizekb=500, + arm_offset=2, + repel_force=0.02, # default 0.01 + use_rank=True, + build="19", + stratified=True, + drop_chr_start=True, + title=f"{PHENOTYPE}", + save=os.path.join(PLOTS_loc, f"manhattan.500kb.300dpi.{PHENOTYPE}.png"), + saveargs={"dpi": 300}, + verbose=True, + ) + +# Perform Quality Control if required +if perform_qc == "YES" or only_qc == "YES": + gwas_data_sumstats_qc = gwas_data_sumstats.filter_value( + '(EAF>=0.01 & EAF<0.99 & DF>=1) & (DAF<0.12 & DAF>-0.12) & (CAVEAT=="None")' + ) + gl.dump_pickle( + gwas_data_sumstats_qc, + os.path.join(GWASCatalog_loc, f"{PHENOTYPE}.b37.gwaslab.qc.pkl"), + overwrite=True, + ) + + gwas_data_sumstats_qc.log.show() + + gwas_data_sumstats_qc.log.save( + os.path.join(GWASCatalog_loc, f"{PHENOTYPE}.b37.gwaslab.qc.log") + ) + + gwas_data_sumstats_qc.to_format( + os.path.join(GWASCatalog_loc, f"{PHENOTYPE}.b37.gwaslab.qc"), + fmt="ssf", + build="19", + ) + + # Generate plots if required + if make_plots == "YES": + gwas_data_sumstats_qc.plot_mqq( + skip=2, + cut=10, + sig_line=True, + sig_level=5e-8, + anno="GENENAME", + anno_style="right", + windowsizekb=500, + arm_offset=2, + repel_force=0.02, # default 0.01 + use_rank=True, + build="19", + stratified=True, + drop_chr_start=True, + save=os.path.join(PLOTS_loc, f"manhattan.500kb.300dpi.{PHENOTYPE}.qc.png"), + saveargs={"dpi": 300}, + verbose=True, + ) + +if select_leads=="YES": + gwas_data_sumstats_leads = gwas_data_sumstats.get_lead(anno=True, windowsizekb=0, sig_level=5e-8, verbose=True, gls=True) + gwas_data_sumstats_leads.to_format( + os.path.join(GWASCatalog_loc + PHENOTYPE + ".b37.gwaslab.significant_snps"), + fmt="ssf", + build="19", +) \ No newline at end of file diff --git a/SCRIPTS/ldsc/CHANGELOG b/SCRIPTS/ldsc/CHANGELOG new file mode 100644 index 0000000..dd72373 --- /dev/null +++ b/SCRIPTS/ldsc/CHANGELOG @@ -0,0 +1,51 @@ +02.01.20 Fix KeyError in allele_merge +30.07.19 v1.0.1; update changelog and increment version for edits on this date (see #164) +30.07.19 Fix to display of liability scale gencov, h2 in rg results log (see #162) +30.07.19 Update munge to use pandas option for gzip output (see #142) +30.07.19 Avoid inconsistent failures from singular matrix in unit test (see #163) +10.08.18 Bug fix to quantile results for continuous annotations (commit 89c13a7, 16e6e80) +25.07.18 Add shebang to make_annot.py (see #127) +17.05.18 Update environment.yml with bioconda/pybedtools (see #118) +11.05.18 Add make_annot.py (commit ea188a7) +07.05.18 Improve error message from type-checking columns in munge (see #59) +07.05.18 Fix for bug in LD score calculation when subsetting individuals (see #115) +07.05.18 Move to conda environment versioning, ensure unit tests passing (see #113) +02.05.17 Update this changelog to record changes between 2.8.15 and 24.4.17 (see #81) +21.04.17 Fix README formatting issues from changes to Github markdown (commit a673b7c; see #80) +08.03.17 Update pandas version requirement, add --daner-n option (pull #75; commit 4da1199) +28.02.17 Fix ValueError in --h2 regarding Series labels from pandas 0.19 (pull #73; commit fa28cca) +27.02.17 Add --thin-annot and --ldsc-cts options (commit 94b5143) +12.02.17 Fix empty annotations outputting total LD scores; fix munge_sumstats outputting wrong + sign on Z scores when sumstats aren't floats; fix error from floats used as indices with + some versions of pandas (pull #69; commit c42e055) +09.11.16 Add support for continuous annotations in partitioned h2 (pull #62; commit f7c3316) +27.10.16 Fix possibly incorrect log of number of MAF exclusions from munge_sumstats (pull #44; + commit d853e57); fix typos in LD hub information (commit 285eb69, 091c1fc) +24.10.16 Add LD Hub information to readme (pull #60; bb0bb7d) +07.06.16 Update Anaconda recommendation for Broad users (commit 2a4af67) +05.04.16 Fix multiple crash errors from pandas 0.17 (pull #49; commit 23a94fc) +29.02.16 Add --print-cov to print covariance matrix of regression coefficients (commit d382392) +01.02.16 Move precomputed LD scores download location to + data.broadinstitute.org/alkesgroup/LDSCORE (commit 2bedf46) +23.12.15 Remove nonfunctional --all flag (commit f8e8342) +06.10.15 Clarify error message if --rg encounters mismatched alleles (commit 78874c7) +05.10.15 Fix failing nosetests from pandas updates (commit a4a29af) +03.08.15 Add mention of heritability tutorial to readme (commit 34394e8) +16.07.15 Update Anaconda recommendation for Broad users (commit af14f3b) +09.07.15 Fix non-ascii character in regressions.py (commit 0401c28) +07.07.15 Fix typo in log for ratio estimates < 0 (commit c72a219) +02.07.15 Modify rg out of bounds error message when --no-intercept (or --intercept-h2 and + --intercept-gencov) flags are set +25.05.15 Fix ValueError in partitioned h2 (possibly caused by changes in pandas in 0.16.1?) +18.04.15 Fix bug where --no-intercept + partitioned LD Scores gave wrong answers. +18.04.15 Fix bug in munge_sumstats.py where using --a1-inc with a file with a signed summary + statistic would when printing summary statistic metadata. +13.03.15 Fix bug where munge_sumstats.py did not correcly report number of variants with bad + alleles removed +03.03.15 Raise sensible exception with pandas < 0.15 +02.03.15 Fix bug where munge sumstats said many SNPs removed for invalid p-values with + --merge-alleles +20.02.15 Fix --a1-inc flag in munge_sumstats.py +17.02.15 Fix bug where ~ was not expanded in comma-separated lists of files +12.02.15 v1.0.0 released + diff --git a/SCRIPTS/ldsc/ContinuousAnnotations/quantile_M.pl b/SCRIPTS/ldsc/ContinuousAnnotations/quantile_M.pl new file mode 100644 index 0000000..3de238b --- /dev/null +++ b/SCRIPTS/ldsc/ContinuousAnnotations/quantile_M.pl @@ -0,0 +1,241 @@ +#!/usr/bin/env perl + +use strict; +use warnings; +use Getopt::Long; +use IO::Uncompress::Gunzip qw($GunzipError); + +sub print_header +{ + print "\n"; + print "\@------------------------------------------------------------------@\n"; + print "| Compute sum of each annotation |\n"; + print "| by quantile of continuous annotations |\n"; + print "| sgazal\@hsph.harvard.edu |\n"; + print "| last modified: 08/10/18 |\n"; + print "\@------------------------------------------------------------------@\n"; + print "\n"; +} + +sub print_help +{ + print "This script allows computing the sum of each annotation by quantile\n"; + print "of a continuous annotation\n\n"; + print "usage: quantile_M.pl [--help]\n"; + print " [--frqfile-chr FRQFILE_CHR] [--ref-annot-chr REF_ANNOT_CHR]\n"; + print " [--annot-header ANNOT_HEADER] [--nb-quantile NB_QUANTILE]\n"; + print " [--maf MAF_THRESHOLD] [--out OUTFILE] [--exclude0]\n\n"; + print "optional arguments:\n"; + print "--help\n"; + print " Print this help message and exit\n"; + print "--frqfile-chr FRQFILE_CHR\n"; + print " Prefix for allele frequency files split over chromosome.\n"; + print " These files should be in the format FRQFILE_CHRchr.frq or\n"; + print " FRQFILE_CHRchr.frq.gz\n"; + print "--ref-annot-chr REF_ANNOT_CHR\n"; + print " Prefix for annotation files split over chromosome. These\n"; + print " files should be in the format REF_LD_CHRchr.annot.gz.\n"; + print " Several annotation files can be combined when seperated\n"; + print " by a comma. The corresponding files need to have the same\n"; + print " SNPs in the same order.\n"; + print "--annot-header ANNOT_HEADER\n"; + print " Specify the header of your annotation of interest in the\n"; + print " REF_ANNOT_CHR files.\n"; + print "--nb-quantile NB_QUANTILE\n"; + print " Specify the number of quantiles to generate (5 by default).\n"; + print "--maf MAF_THRESHOLD\n"; + print " Specify the MAF threshold to use to include reference SNPs\n"; + print " (0.05 by default)\n"; + print "--out OUTFILE\n"; + print " Specify the output filename.\n"; + print "--exclude0\n"; + print " Do not consider continuous value equals to 0 (in special case\n"; + print " where the annotation is quantile normalized and 0 tags missing\n"; + print " value).\n"; + exit; +} + +my($frqfile_chr)=""; my($ref_annot_chr)=""; my($annot)=""; my($nb_quantile)=5; my($maf_threshold)=0.05; my($out_file)=""; my($i); my($j); my($k); my($printhelp); my($exclude0); +GetOptions( + "help" => \$printhelp, + "frqfile-chr=s" => \$frqfile_chr, + "ref-annot-chr=s" => \$ref_annot_chr, + "annot-header=s" => \$annot, + "nb-quantile=s" => \$nb_quantile, + "maf=s" => \$maf_threshold, + "out=s" => \$out_file, + "exclude0" => \$exclude0 +); + +print_header(); +if (defined($printhelp)){ print_help() } +if (($frqfile_chr eq "") || ($ref_annot_chr eq "") || ($annot eq "") || ($out_file eq "")) {die "\nERROR! --freqfile-chr, --ref-annot-chr, --annot-header and --out options are mandatory.\n"} + +#Step 0: find annot in ref-ld +my($annot_file)=""; +my($annot_colm)=""; +my($total_nb_annotation)=0; +my(@list_ref_annot)=split(/,/, $ref_annot_chr); +my($nb_ref_annot) =$#list_ref_annot+1; +print "ref-ld-chr : $nb_ref_annot file(s)\n"; +for ($i=0; $i<=$#list_ref_annot ; $i++) { + print " $list_ref_annot[$i]\n"; + my $IN = IO::Uncompress::Gunzip->new( " $list_ref_annot[$i]22.annot.gz" ) or die "IO::Uncompress::Gunzip failed: $GunzipError\n"; + my(@line)=split ' ', <$IN>; + for ($j=0; $j<=$#line ; $j++) { + if($line[$j] eq $annot){ + $annot_file=$list_ref_annot[$i]; + $annot_colm=$j+1; + } + } + close $IN; + $total_nb_annotation=$total_nb_annotation+$#line-3; +} +print " $total_nb_annotation total annotations are present in annotation files.\n"; +print "annot-header : $annot\n"; +if($annot_file eq "") {die "\nERROR! Annotation $annot has not been find in any annotation files.\n"} +print " present in column $annot_colm of annotation files $annot_file\n"; +print "frqfile-chr : $frqfile_chr\n"; +print "nb-quantile : $nb_quantile\n"; +print "maf : $maf_threshold\n"; +if ($maf_threshold<0 || $maf_threshold>0.5) {die "ERROR! MAF sould be between O and 0.5\n\n"} +print "exclude0 : "; +if (defined($exclude0)) {print "yes\n"} else {print "no\n"} +print "out : $out_file\n"; +print "\n"; + +#Step 1: Stock MAF from $frqfile_chr files +print "Step 1: Stock MAF from $frqfile_chr files\n"; +my(@MAF)=(); +my($chr); +my($nbSNPs)=0; +my($nbcommonSNPs)=0; +for ($chr=1; $chr<=22 ; $chr++) { + if (-e "$frqfile_chr$chr.frq") { + open(IN,"$frqfile_chr$chr.frq") or die "\nERROR! Cannot open $frqfile_chr$chr.frq.\n"; + while () { + chomp $_; my(@line)=split; + if($line[4] ne "MAF"){ + if ($line[4]>=$maf_threshold && $line[4]<=(1-$maf_threshold)) { push(@MAF,1); $nbcommonSNPs++} else { push(@MAF,0) }; + $nbSNPs++; + } + } + close IN; + } elsif (-e "$frqfile_chr$chr.frq.gz") { + my $IN = IO::Uncompress::Gunzip->new( "$frqfile_chr$chr.frq.gz" ) or die "IO::Uncompress::Gunzip failed: $GunzipError\n"; + while (<$IN>) { + chomp $_; my(@line)=split; + if($line[4] ne "MAF"){ + if ($line[4]>=$maf_threshold && $line[4]<=(1-$maf_threshold)) { push(@MAF,1); $nbcommonSNPs++} else { push(@MAF,0) }; + $nbSNPs++; + } + } + close $IN; + } else { + die "\nERROR! Cannot open $frqfile_chr$chr.frq(.gz).\n"; + } +} +print " $nbcommonSNPs/$nbSNPs SNPs with MAF>=$maf_threshold\n\n"; + +#Step 2: Stock continuous annotations from $annot_file files +print "Step 2: Stock continuous annotations from $annot_file files\n"; +print " and compute quantiles\n"; +my($Q); +my(@annot_value)=(); +my(@annot_value_all)=(); +my($cpt)=0; +$j=$annot_colm-1; +for ($chr=1; $chr<=22 ; $chr++) { + my $IN = IO::Uncompress::Gunzip->new( " $annot_file$chr.annot.gz" ) or die "IO::Uncompress::Gunzip failed: $GunzipError\n"; + while (<$IN>) { + chomp $_; my(@line)=split; + if($line[$j] ne $annot){ + if ($MAF[$cpt]==1){ + if (!defined($exclude0) || $line[$j]!=0) { + push(@annot_value,$line[$j]) + } + } + push(@annot_value_all,$line[$j]); + $cpt++; + } + } + close IN; +} +my($nb_annot_value)=$#annot_value+1; +print " $nb_annot_value $annot values with MAF>=$maf_threshold\n\n"; + +@annot_value = sort {$a <=> $b} @annot_value; + +my(@Qvect)=(); +for ($i=0; $i<=$nb_quantile ; $i++) { + $Q=$annot_value[sprintf("%.0f",($i*($#annot_value)/$nb_quantile))]; + print "Q$i = $Q\n"; + push(@Qvect,$Q); +} + +#substract epsilon to $Qvect[0] +$Qvect[0]=$Qvect[0]-0.0000000001; + +#Step 3: Compute sum of each annotation in each quantile +print "\nStep 3: Compute sum of each annotation in each quantile\n"; +#Initialize matrix +my @matrix; +my @thismatrix; +my @nb_per_Q; +my @list_annot; +# +my($cptQ); +for ($k=0; $k<=$#list_ref_annot ; $k++) { + print " Reading $list_ref_annot[$k]\n"; + $cpt=0; + @thismatrix=(); + @nb_per_Q=(); + for ($chr=1; $chr<=22 ; $chr++) { + my($cpt0)=0; + my $IN = IO::Uncompress::Gunzip->new( " $list_ref_annot[$k]$chr.annot.gz" ) or die "IO::Uncompress::Gunzip failed: $GunzipError\n"; + while (<$IN>) { + chomp $_; my(@line)=split; + if($cpt0>0){ + if ($MAF[$cpt]==1){ + if (!defined($exclude0) || $annot_value_all[$cpt]!=0) { + $cptQ=-1; + while ($annot_value_all[$cpt]>$Qvect[$cptQ+1]) {$cptQ++} + for ($i=0; $i<($#line-3) ; $i++) { + $thismatrix[$i][$cptQ]=$thismatrix[$i][$cptQ]+$line[$i+4]; + } + $nb_per_Q[$cptQ]=$nb_per_Q[$cptQ]+1; + } + } + $cpt++; + } + if($cpt==0) { + #initialize thismatrix and nb_per_Q + update list_annot + for ($i=0; $i<($#line-3) ; $i++) { + for ($j=0; $j<$nb_quantile ; $j++) { + $thismatrix[$i][$j]=0; + } + push(@list_annot,$line[$i+4]); + } + @nb_per_Q=(0) x $nb_quantile; + } + $cpt0=1; + } + close $IN; + } + @matrix = (@matrix,@thismatrix); +} + +open(OUT,">$out_file") || die "\nCannot create $out_file file.\n"; +printf OUT "N"; +for ($j=0; $j<$nb_quantile ; $j++) { +printf OUT " $nb_per_Q[$j]"; +} +printf OUT "\n"; +for ($i=0; $i<$total_nb_annotation ; $i++) { + printf OUT "$list_annot[$i]"; + for ($j=0; $j<$nb_quantile ; $j++) { + printf OUT " $matrix[$i][$j]"; + } + printf OUT "\n"; +} +close OUT; diff --git a/SCRIPTS/ldsc/ContinuousAnnotations/quantile_h2g.r b/SCRIPTS/ldsc/ContinuousAnnotations/quantile_h2g.r new file mode 100644 index 0000000..2a0cfd9 --- /dev/null +++ b/SCRIPTS/ldsc/ContinuousAnnotations/quantile_h2g.r @@ -0,0 +1,76 @@ +#8/10/18: fixed a bug for the enr_se column + +options(echo=FALSE); +param <- commandArgs(trailingOnly=T) +annotfile = eval(paste(text=param[1])) +resultfile = eval(paste(text=param[2])) +outfile = eval(paste(text=param[3])) + +if (length(param)!=3) { + cat("\nError: three arguments must be supplied.\n") + cat("ARG1 : the file containing the sum of each annotation\n") + cat(" by quantile of the continuous annotation\n") + cat("ARG2 : Specify the prefix of the ldsc result outputs\n") + cat(" (.results and .part_delete files)\n") + cat("ARG3 : Specify the output filename.\n\n") + stop("three arguments must be supplied.\n", call.=FALSE) +} + +compute_h2 <- function(baseline_chunk,tau) { + apply(tau*t(baseline_chunk$baseline_chunk),2,sum) +} + +se_jacknife <- function(theta,theta_j){ + theta_hatJ=sum(theta-theta_j)+sum(theta_j/200) + tau=200*theta-199*theta_j + sqrt((1/200)*sum((tau-theta_hatJ)**2/(199))) +} + +#1) Read annotfile +data=read.table(annotfile,h=F) +M = NULL +M$chunk_size = as.numeric(data[1,-1]) +M$baseline_chunk = t(data[-1,-1]) +rm(data) + +#2) Read results file and delete file +data=read.table(paste(resultfile,".results",sep=""),h=T) +mytau=as.numeric(data$Coefficient) +rm(data) +jacknife = read.table(paste(resultfile,".part_delete",sep=""),h=F) + +#3) Compute h2g per quantile +h2g = compute_h2(M,mytau) +h2g_prop = h2g/sum(h2g) +#h2g_enr_z test whether the enrichment of a quintile is significantly different +#from the union of all the others (see Finucane et al. 2015 Nat Genet +h2g_enr_z = (h2g/M$chunk_size) - ((sum(h2g)-h2g)/(sum(M$chunk_size)-M$chunk_size)) + +#4) Compute h2g for each Jacknife estimates +h2jacknife = NULL; +h2jacknife_prop = NULL; +h2jacknife_enr_z = NULL; +for (i in 1:nrow(jacknife)){ + tau_i=NULL; for (j in 1:ncol(jacknife)) {tau_i=c(tau_i,jacknife[i,j])} + thish2g = compute_h2(M,tau_i) + h2jacknife = rbind(h2jacknife , thish2g ); + h2jacknife_prop = rbind(h2jacknife_prop , thish2g/sum(thish2g) ); + h2jacknife_enr_z = rbind(h2jacknife_enr_z , (thish2g/M$chunk_size) - ((sum(thish2g)-thish2g)/(sum(M$chunk_size)-M$chunk_size)) ); +} + +#5) Compute h2g std err per quantile +h2g_sd = NULL; +h2g_prop_sd = NULL; +h2g_enr_z_sd = NULL; +for (i in 1:length(h2g)) { + h2g_sd = c(h2g_sd ,se_jacknife(h2g[i] ,h2jacknife[,i])) + h2g_prop_sd = c(h2g_prop_sd ,se_jacknife(h2g_prop[i] ,h2jacknife_prop[,i])) + h2g_enr_z_sd = c(h2g_enr_z_sd,se_jacknife(h2g_enr_z[i] ,h2jacknife_enr_z[,i])) +} + +#6) write outputs +#out = cbind(h2g,h2g_sd,h2g_prop,h2g_prop_sd,(h2g/sum(h2g))/(M$chunk_size/sum(M$chunk_size)),(h2g_sd/sum(h2g))/(M$chunk_size/sum(M$chunk_size)),2*pnorm(-abs(h2g_enr_z/h2g_enr_z_sd))) +out = cbind(h2g,h2g_sd,h2g_prop,h2g_prop_sd,h2g_prop/(M$chunk_size/sum(M$chunk_size)),h2g_prop_sd/(M$chunk_size/sum(M$chunk_size)),2*pnorm(-abs(h2g_enr_z/h2g_enr_z_sd))) +colnames(out)=c("h2g","h2g_se","prop_h2g","prop_h2g_se","enr","enr_se","enr_pval") + +write.table(file=outfile,out,quote=F,col.names=T,row.names=F,sep="\t") diff --git a/SCRIPTS/ldsc/LICENSE b/SCRIPTS/ldsc/LICENSE new file mode 100644 index 0000000..733c072 --- /dev/null +++ b/SCRIPTS/ldsc/LICENSE @@ -0,0 +1,675 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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But first, please read +. + diff --git a/SCRIPTS/ldsc/README.md b/SCRIPTS/ldsc/README.md new file mode 100644 index 0000000..5795612 --- /dev/null +++ b/SCRIPTS/ldsc/README.md @@ -0,0 +1,119 @@ + +# LDSC (LD SCore) `v1.0.1` + +`ldsc` is a command line tool for estimating heritability and genetic correlation from GWAS summary statistics. `ldsc` also computes LD Scores. + +## Getting Started + + + +In order to download `ldsc`, you should clone this repository via the commands +``` +git clone https://github.com/bulik/ldsc.git +cd ldsc +``` + +In order to install the Python dependencies, you will need the [Anaconda](https://store.continuum.io/cshop/anaconda/) Python distribution and package manager. After installing Anaconda, run the following commands to create an environment with LDSC's dependencies: + +``` +conda env create --file environment.yml +source activate ldsc +``` + +Once the above has completed, you can run: + +``` +./ldsc.py -h +./munge_sumstats.py -h +``` +to print a list of all command-line options. If these commands fail with an error, then something as gone wrong during the installation process. + +Short tutorials describing the four basic functions of `ldsc` (estimating LD Scores, h2 and partitioned h2, genetic correlation, the LD Score regression intercept) can be found in the wiki. If you would like to run the tests, please see the wiki. + +## Updating LDSC + +You can update to the newest version of `ldsc` using `git`. First, navigate to your `ldsc/` directory (e.g., `cd ldsc`), then run +``` +git pull +``` +If `ldsc` is up to date, you will see +``` +Already up-to-date. +``` +otherwise, you will see `git` output similar to +``` +remote: Counting objects: 3, done. +remote: Compressing objects: 100% (3/3), done. +remote: Total 3 (delta 0), reused 0 (delta 0), pack-reused 0 +Unpacking objects: 100% (3/3), done. +From https://github.com/bulik/ldsc + 95f4db3..a6a6b18 master -> origin/master +Updating 95f4db3..a6a6b18 +Fast-forward + README.md | 15 +++++++++++++++ + 1 file changed, 15 insertions(+) + ``` +which tells you which files were changed. If you have modified the `ldsc` source code, `git pull` may fail with an error such as `error: Your local changes to the following files would be overwritten by merge:`. + +In case the Python dependencies have changed, you can update the LDSC environment with + +``` +conda env update --file environment.yml +``` + +## Where Can I Get LD Scores? + +You can download [European](https://data.broadinstitute.org/alkesgroup/LDSCORE/eur_w_ld_chr.tar.bz2) and [East Asian LD Scores](https://data.broadinstitute.org/alkesgroup/LDSCORE/eas_ldscores.tar.bz2) from 1000 Genomes [here](https://data.broadinstitute.org/alkesgroup/LDSCORE/). These LD Scores are suitable for basic LD Score analyses (the LD Score regression intercept, heritability, genetic correlation, cross-sex genetic correlation). You can download partitioned LD Scores for partitioned heritability estimation [here](http://data.broadinstitute.org/alkesgroup/LDSCORE/). + + +## Support + +Before contacting us, please try the following: + +1. The [wiki](https://github.com/bulik/ldsc/wiki) has tutorials on [estimating LD Score](https://github.com/bulik/ldsc/wiki/LD-Score-Estimation-Tutorial), [heritability, genetic correlation and the LD Score regression intercept](https://github.com/bulik/ldsc/wiki/Heritability-and-Genetic-Correlation) and [partitioned heritability](https://github.com/bulik/ldsc/wiki/Partitioned-Heritability). +2. Common issues are described in the [FAQ](https://github.com/bulik/ldsc/wiki/FAQ) +2. The methods are described in the papers (citations below) + +If that doesn't work, you can get in touch with us via the [google group](https://groups.google.com/forum/?hl=en#!forum/ldsc_users). + +Issues with LD Hub? Email ld-hub@bristol.ac.uk + + +## Citation + +If you use the software or the LD Score regression intercept, please cite + +[Bulik-Sullivan, et al. LD Score Regression Distinguishes Confounding from Polygenicity in Genome-Wide Association Studies. +Nature Genetics, 2015.](http://www.nature.com/ng/journal/vaop/ncurrent/full/ng.3211.html) + +For genetic correlation, please also cite + +[Bulik-Sullivan, B., et al. An Atlas of Genetic Correlations across Human Diseases and Traits. Nature Genetics, 2015.](https://www.nature.com/articles/ng.3406) Preprint available on bioRxiv doi: http://dx.doi.org/10.1101/014498 + +For partitioned heritability, please also cite + +[Finucane, HK, et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nature Genetics, 2015.](https://www.nature.com/articles/ng.3404) Preprint available on bioRxiv doi: http://dx.doi.org/10.1101/014241 + +For stratified heritability using continuous annotation, please also cite + +[Gazal, S, et al. Linkage disequilibrium–dependent architecture of human complex traits shows action of negative selection. Nature Genetics, 2017.](https://www.nature.com/articles/ng.3954) + +If you find the fact that LD Score regression approximates HE regression to be conceptually useful, please cite + +Bulik-Sullivan, Brendan. Relationship between LD Score and Haseman-Elston, bioRxiv doi: http://dx.doi.org/10.1101/018283 + +For LD Hub, please cite + +[Zheng, et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics (2016)](https://doi.org/10.1093/bioinformatics/btw613) + + +## License + +This project is licensed under GNU GPL v3. + + +## Authors + +Brendan Bulik-Sullivan (Broad Institute of MIT and Harvard) + +Hilary Finucane (MIT Department of Mathematics) diff --git a/SCRIPTS/ldsc/_archive/ldsc.heritability.genius.sh b/SCRIPTS/ldsc/_archive/ldsc.heritability.genius.sh new file mode 100644 index 0000000..4ed27e1 --- /dev/null +++ b/SCRIPTS/ldsc/_archive/ldsc.heritability.genius.sh @@ -0,0 +1,99 @@ +#!/bin/bash +#ldsc.heritability.sh +###Download ldsc by GitHub instructions +###In environment.yml change pandas=0.20 to simply pandas +###set up environment in miniconda3 bin (not anaconda) + +# activate ldsc conda environment + source /hpc/local/Rocky8/dhl_ec/software/mambaforge3/bin/activate ldsc +# set up paths +# path to ldsc scripts +#ROOT="/Users/esmulde2" + +#data folder +#DATA="/Volumes/MacExtern/UMC/Werk/GENIUS-CHD/ldsc" +DATA="" +# +## path to map with ld +#REF_LD="${LDSC}/eur_w_ld_chr/" +## path to merge allele snplist +#SNPLIST="${LDSC}/w_hm3.snplist" + +# Default values +PHENOTYPE="" +TRAIT="" +REF_LD="" +#REF_LD="${LDSC}/eur_w_ld_chr/" + +# Function to display usage information +usage() { + echo "Usage: $0 [-p ] [Options]" 1>&2 + echo "Options:" 1>&2 + echo " -p : Specify the phenotype." 1>&2 + echo " -p : Specify the trait." 1>&2 + echo " -d : Specify the map for data" 1>&2 + echo " -l : Specify the map with REF_LD info." 1>&2 + exit 1 +} +# Parsing command-line options +while getopts ":p:t:d:j:l:" opt; do + case ${opt} in + p) PHENOTYPE=$OPTARG ;; + t) TRAIT=$OPTARG ;; + d) DATA=$OPTARG ;; + j) PROJECTNAME=$OPTARG ;; + l) REF_LD=$OPTARG ;; + :) echo "Option -$OPTARG requires an argument." >&2; exit 1 ;; + \?) echo "Invalid option: -$OPTARG" >&2; exit 1 ;; + esac +done + +LDSC="${DATA}/ldsc" +cd ${LDSC} + +# Ensure either PHENOTYPE or TRAIT is provided +if [ -z "$PHENOTYPE" ] && [ -z "$TRAIT" ]; then + echo "Error: Either phenotype or trait must be specified." >&2 + usage +fi + +# Perform actions based on PHENOTYPE +if [ -n "$PHENOTYPE" ]; then + echo "PHENOTYPE: ${PHENOTYPE}" + H2_INPUT="${DATA}/${PROJECTNAME}/${PHENOTYPE}/ldsc/sumstats/${PHENOTYPE}.sumstats.gz" + mkdir -p "${DATA}/${PROJECTNAME}/${PHENOTYPE}/ldsc/heritability/" + H2_OUTPUT="${DATA}/${PROJECTNAME}/${PHENOTYPE}/ldsc/heritability/${PHENOTYPE}_h2" + + # Ensure input file exists + if [ ! -f "$H2_INPUT" ]; then + echo "Error: Input file ${H2_INPUT} not found." >&2 + exit 1 + fi + + ### h2 heritability + ./ldsc.py \ + --h2 ${H2_INPUT} \ + --ref-ld-chr ${REF_LD} \ + --w-ld-chr ${REF_LD} \ + --out ${H2_OUTPUT} +fi + +# Perform actions based on TRAIT +if [ -n "$TRAIT" ]; then + echo "TRAIT: ${TRAIT}" + H2_INPUT="${DATA}/traits/sumstats/${TRAIT}.sumstats.gz" + mkdir -p "${DATA}/traits/heritability" + H2_OUTPUT="${DATA}/traits/heritability/${TRAIT}_h2" + # Ensure input file exists + if [ ! -f "$H2_INPUT" ]; then + echo "Error: Input file ${H2_INPUT} not found." >&2 + exit 1 + fi + + ### h2 heritability + ./ldsc.py \ + --h2 ${H2_INPUT} \ + --ref-ld-chr ${REF_LD} \ + --w-ld-chr ${REF_LD} \ + --out ${H2_OUTPUT} +fi diff --git a/SCRIPTS/ldsc/_archive/ldsc.rg.genius.sh b/SCRIPTS/ldsc/_archive/ldsc.rg.genius.sh new file mode 100644 index 0000000..7824244 --- /dev/null +++ b/SCRIPTS/ldsc/_archive/ldsc.rg.genius.sh @@ -0,0 +1,75 @@ +#!/bin/bash + +###Download ldsc by GitHub instructions +###In environment.yml change pandas=0.20 to simply pandas +###set up environment in miniconda3 bin (not anaconda) + +# activate ldsc conda environment +source /hpc/local/Rocky8/dhl_ec/software/mambaforge3/bin/activate ldsc + +# set up paths +# path to ldsc scripts +#ROOT="/Users/esmulde2" + +#data folder +DATA="" +#DATA="/Volumes/MacExtern/UMC/Werk/GENIUS-CHD" + +# Default values +PHENOTYPE="" +TRAIT="" +REF_LD="" + +# Function to display usage information +usage() { + echo "Usage: $0 [-p ] [-t ] [-i ] [Options]" 1>&2 + echo "Options:" 1>&2 + echo " -p : Specify the phenotype." 1>&2 + echo " -t : Specify the trait." 1>&2 + echo " -d : Specify the map for data" 1>&2 + echo " -l : Specify the map with eur_ld info." 1>&2 + exit 1 +} +# Parsing command-line options +while getopts ":p:t:d:j:l:" opt; do + case ${opt} in + p) PHENOTYPE=$OPTARG ;; + t) TRAIT=$OPTARG ;; + d) DATA=$OPTARG ;; + j) PROJECTNAME=$OPTARG ;; + l) REF_LD=$OPTARG ;; + :) echo "Option -$OPTARG requires an argument." >&2; exit 1 ;; + \?) echo "Invalid option: -$OPTARG" >&2; exit 1 ;; + esac +done + +#echo "$DATA" +#echo "$REF_LD" +LDSC="${DATA}/ldsc" +cd ${LDSC} +# Check if either phenotype or trait is provided +if [[ -z "${PHENOTYPE}" || -z "${TRAIT}" ]]; then + echo "Error: Please specify a phenotype and a trait." 1>&2 + exit 1 +fi + + + + +## path to GENIUS_CHD phenotypes input file +PHENO_INPUT="${DATA}/${PROJECTNAME}/${PHENOTYPE}/ldsc/sumstats/${PHENOTYPE}.sumstats.gz" + +# mkdir -p "${DATA}/ldsc/heritability/${PHENOTYPE}" + +# path to input file for heritability +TRAIT_INPUT="${DATA}/traits/sumstats/${TRAIT}.sumstats.gz" + +# path to output file for heritability +PHENOTYPE_TRAIT="${DATA}/${PROJECTNAME}/${PHENOTYPE}/ldsc/heritability/${PHENOTYPE}_${TRAIT}" + +##phenotype comparison with trait +./ldsc.py \ +--rg ${PHENO_INPUT},${TRAIT_INPUT} \ +--ref-ld-chr ${REF_LD} \ +--w-ld-chr ${REF_LD} \ +--out ${PHENOTYPE_TRAIT} diff --git a/SCRIPTS/ldsc/_archive/ldsc.run.genius.sh b/SCRIPTS/ldsc/_archive/ldsc.run.genius.sh new file mode 100644 index 0000000..c428d49 --- /dev/null +++ b/SCRIPTS/ldsc/_archive/ldsc.run.genius.sh @@ -0,0 +1,194 @@ +#!/bin/bash + +# SLURM settings +#SBATCH --job-name=ldsc # Job name +#SBATCH --mail-type=FAIL # Mail events (NONE, BEGIN, END, FAIL, ALL) +#SBATCH --mail-user=e.j.a.smulders-2@umcutrecht.nl # Where to send mail +#SBATCH --nodes=1 # Run on one node +#SBATCH --ntasks=1 # Run on a single CPU +#SBATCH --mem=32G # Job memory request +#SBATCH --time=1:00:00 # Time limit hrs:min:sec +#SBATCH --output=ldsc.out # Standard output and error log + +###Download ldsc by GitHub instructions +###In environment.yml change pandas=0.20 to simply pandas +###set up environment in miniconda3 bin (not anaconda) + +# set up paths +# path to ldsc scripts +#ROOT="/Users/esmulde2" + + +#data folder +#DATA="/Volumes/MacExtern/UMC/Werk/GENIUS-CHD/ldsc" +#DATA="/Volumes/MacExtern/UMC/Werk/GENIUS-CHD" +DATA="" +PROJECTNAME="" +PHENOTYPE="" +TRAIT_FILE="" +#DATA="/Volumes/MacExtern/UMC/Werk/GENIUS-CHD" +# path to map with ld +REF_LD="" +REF="" +#REF_LD="/Volumes/MacExtern/UMC/Werk/references/eur_w_ld_chr/" +# path to merge allele snplist +SNPLIST="${LDSC}/w_hm3.snplist" +CHUNKSIZE= + +path="" +r="" +n="" +a="" +b="" +p="" +f="" + +#PHENOTYPES=("META_cox_DEAD_ALL" "META_logistic_DEAD_ALL") +#TRAITS=("AD" "Na" "K") +data_analysed="" +# Function to display usage information +usage() { + echo "Usage: $0 [-p ] [Options]" 1>&2 + echo "Options:" 1>&2 + echo " -p : Specify the phenotypes." 1>&2 + echo " -t : Specify the traits." 1>&2 + echo " -w : Specify trait or phenotype analysis." 1>&2 + echo " -d : Specify the map for data" 1>&2 + echo " -l : Specify the map with ref_ld info." 1>&2 + echo " -h : Specify the snplist file." 1>&2 + exit 1 +} +# Parsing command-line options +while getopts ":p:t:w:d:j:i:l:h:r:n:a:b:v:f:c:" opt; do + case ${opt} in + p) PHENOTYPE=$OPTARG ;; + t) TRAIT_FILE=$OPTARG ;; + w) data_analysed=$OPTARG ;; + d) DATA=$OPTARG ;; + j) PROJECTNAME=$OPTARG ;; + i) path=$OPTARG ;; + l) REF_LD=$OPTARG ;; + h) SNPLIST=$OPTARG ;; + r) r=$OPTARG ;; + n) n=$OPTARG ;; + a) a=$OPTARG ;; + b) b=$OPTARG ;; + v) v=$OPTARG ;; + f) f=$OPTARG ;; + c) CHUNKSIZE=$OPTARG ;; + :) echo "Option -$OPTARG requires an argument." >&2; exit 1 ;; + \?) echo "Invalid option: -$OPTARG" >&2; exit 1 ;; + esac +done + +LDSC="${DATA}/ldsc" + +#echo "${path}" + +# Validate input for data_analysed +if [ "$data_analysed" != "PHENOTYPE" ] && [ "$data_analysed" != "TRAIT" ]; then + echo "Invalid value for -w option. Please specify either 'PHENOTYPE' or 'TRAIT'." + exit 1 +fi + +# Check if phenotype file is provided and read the contents +# if [ "$data_analysed" == "PHENOTYPE" ]; then +# if [ -n "$PHENOTYPE_FILE" ]; then +# PHENOTYPES=() +# SAMPLESIZES=() +# while IFS=',' read -r phenotype sample_size; do +# PHENOTYPES+=("$phenotype") +# SAMPLESIZES+=("$sample_size") +# done < "$PHENOTYPE_FILE" +# if [ ${#PHENOTYPES[@]} -eq 0 ]; then +# echo "Phenotype file is empty or not properly formatted." +# exit 1 +# fi +# else +# echo "Phenotype file (-p) is required." +# exit 1 +# fi + +# # Also read the trait file for genetic correlation analysis +# if [ -n "$TRAIT_FILE" ]; then +# TRAITS=() +# while read -r line; do +# trait=$(echo $line | awk '{print $1}') +# TRAITS+=("$trait") +# done < "$TRAIT_FILE" +# if [ ${#TRAITS[@]} -eq 0 ]; then +# echo "Trait file is empty or not properly formatted." +# exit 1 +# fi +# else +# echo "Trait file (-t) is required for genetic correlation analysis." +# exit 1 +# fi +# fi + + +# # Check if trait file is provided and read the contents if data_analysed is TRAIT +# if [ "$data_analysed" == "TRAIT" ] && [ -n "$TRAIT_FILE" ]; then +# TRAITS=() +# while read -r line; do +# trait=$(echo $line | awk '{print $1}') +# TRAITS+=("$trait") +# done < "$TRAIT_FILE" +# if [ ${#TRAITS[@]} -eq 0 ]; then +# echo "Trait file is empty or not properly formatted." +# exit 1 +# fi +# fi + + + +# Check if phenotype file is provided and read the contents +if [ "$data_analysed" == "PHENOTYPE" ]; then + if [ -z "$PHENOTYPE" ]; then + echo "Phenotype (-p) is required for phenotype analysis." + exit 1 + fi + mkdir -p ${DATA}/${PROJECTNAME}/${PHENOTYPE}/ldsc + mkdir -p ${DATA}/${PROJECTNAME}/${PHENOTYPE}/ldsc/sumstats + mkdir -p ${DATA}/${PROJECTNAME}/${PHENOTYPE}/ldsc/heritability + # Also read the trait file for genetic correlation analysis + if [ -n "$TRAIT_FILE" ]; then + TRAITS=() + while read -r line; do + trait=$(echo $line | awk '{print $1}') + TRAITS+=("$trait") + done < "$TRAIT_FILE" + if [ ${#TRAITS[@]} -eq 0 ]; then + echo "Trait file is empty or not properly formatted." + exit 1 + fi + else + echo "Trait file (-t) is required for genetic correlation analysis." + exit 1 + fi + + echo "Analyzing PHENOTYPE: ${PHENOTYPE}" + echo "Making sumstats file..." + bash ${LDSC}/ldsc.sumstats.sh -d ${DATA} -j "${PROJECTNAME}" -h ${SNPLIST} -p ${PHENOTYPE} -i ${DATA}/${PROJECTNAME}/${PHENOTYPE}/input/${PHENOTYPE}.cojo.gz -r ${r} -n ${n} -a ${a} -b ${b} -v ${v} -f ${f} -c ${CHUNKSIZE} + echo "Running heritability analysis..." + bash ${LDSC}/ldsc.heritability.sh -d ${DATA} -j "${PROJECTNAME}" -l ${REF_LD} -p ${PHENOTYPE} + for TRAIT in "${TRAITS[@]}"; do + echo "Comparing ${PHENOTYPE} with ${TRAIT} ..." + bash ${LDSC}/ldsc.rg.sh -d ${DATA} -j "${PROJECTNAME}" -l ${REF_LD} -p ${PHENOTYPE} -t ${TRAIT} + done +fi + +if [ "$data_analysed" == "TRAIT" ]; then + if [ -z "$TRAIT_FILE" ]; then + echo "Trait file (-t) is required for trait analysis." + exit 1 + fi + mkdir -p ${DATA}/traits + mkdir -p ${DATA}/traits/sumstats + mkdir -p ${DATA}/traits/heritability + echo "Analyzing TRAIT: ${TRAIT_FILE}" + echo "Making sumstats file..." + bash ${LDSC}/ldsc.sumstats.sh -d ${DATA} -j "${PROJECTNAME}" -h ${SNPLIST} -t ${TRAIT_FILE} -i ${path} -r ${r} -n ${n} -a ${a} -b ${b} -v ${v} -f ${f} -c ${CHUNKSIZE} + echo "Running heritability analysis..." + bash ${LDSC}/ldsc.heritability.sh -d ${DATA} -j "${PROJECTNAME}" -l ${REF_LD} -t ${TRAIT_FILE} +fi \ No newline at end of file diff --git a/SCRIPTS/ldsc/_archive/ldsc.sumstats.genius.sh b/SCRIPTS/ldsc/_archive/ldsc.sumstats.genius.sh new file mode 100644 index 0000000..697f53b --- /dev/null +++ b/SCRIPTS/ldsc/_archive/ldsc.sumstats.genius.sh @@ -0,0 +1,126 @@ +#!/bin/bash +#ldsc.sumstats.sh +###Download ldsc by GitHub instructions +###In environment.yml change pandas=0.20 to simply pandas +###set up environment in miniconda3 bin (not anaconda) + +# activate ldsc conda environment +source /hpc/local/Rocky8/dhl_ec/software/mambaforge3/bin/activate ldsc +# set up paths +# path to ldsc scripts + + + +# Default values +#DATA="/Volumes/MacExtern/UMC/Werk/GENIUS-CHD" +DATA="" +PHENOTYPE="" +TRAIT="" +SNPLIST="" +CHUNKSIZE= +r="" +N="" +a1="" +a2="" +p="" +frq="" + + +# Function to display usage information +usage() { + echo "Usage: $0 [-p ] [-i ] [Options]" 1>&2 + echo "Options:" 1>&2 + echo " -p : Specify the phenotype." 1>&2 + echo " -t : Specify the trait." 1>&2 + echo " -d : Specify the map for data" 1>&2 + echo " -h : Specify the snplist file." 1>&2 + echo " -i : Specify the input file for sumstats conversion." 1>&2 + echo " -r : Specify the column name for SNP IDs." 1>&2 + echo " -n : Specify the column name for sample size (N)." 1>&2 + echo " -a : Specify the column name for effect allele (a1)." 1>&2 + echo " -b : Specify the column name for other allele (a2)." 1>&2 + echo " -v : Specify the column name for P-value." 1>&2 + echo " -f : Specify the column name for effect allele frequency." 1>&2 + exit 1 +} + + +# Parsing command-line options +while getopts ":p:t:d:j:h:i:r:n:a:b:v:f:c:" opt; do + case ${opt} in + p) PHENOTYPE=$OPTARG ;; + t) TRAIT=$OPTARG ;; + d) DATA=$OPTARG ;; + j) PROJECTNAME=$OPTARG ;; + h) SNPLIST=$OPTARG ;; + i) INPUT=$OPTARG ;; + r) snp=$OPTARG ;; + n) N=$OPTARG ;; + a) a1=$OPTARG ;; + b) a2=$OPTARG ;; + v) p=$OPTARG ;; + f) frq=$OPTARG ;; + c) CHUNKSIZE=$OPTARG ;; + :) echo "Option -$OPTARG requires an argument." >&2; exit 1 ;; + \?) echo "Invalid option: -$OPTARG" >&2; exit 1 ;; + esac +done + + +# Debugging: Print parsed options +LDSC="${DATA}/ldsc" +cd ${LDSC} + + + +# Ensure either PHENOTYPE or TRAIT is provided +if [ -z "$PHENOTYPE" ] && [ -z "$TRAIT" ]; then + echo "Error: Either phenotype or trait must be specified." >&2 + usage +fi + + +## path to phenotypes input file for sumstats conversion + +# echo "PHENOTYPE: ${PHENOTYPE}" +# echo "TRAIT: ${TRAIT}" +# echo "DATA: ${DATA}" +# echo "INPUT: ${INPUT}" +# echo "snp: ${snp}" +# echo "N: ${N}" +# echo "a1: ${a1}" +# echo "a2: ${a2}" +# echo "p: ${p}" +# echo "frq: ${frq}" +# SUMSTATS_INPUT="${DATA}/input/${PHENOTYPE}.b37.gwaslab.ssf.tsv.gz" + + +# Determine output file based on PHENOTYPE or TRAIT +if [ -n "$PHENOTYPE" ]; then + SUMSTATS_INPUT="${INPUT}" +elif [ -n "$TRAIT" ]; then + SUMSTATS_INPUT="${INPUT}" +fi + +# Determine output file based on PHENOTYPE or TRAIT +if [ -n "$PHENOTYPE" ]; then + mkdir -p ${DATA}/${PROJECTNAME}/${PHENOTYPE}/ldsc/sumstats + SUMSTATS_OUTPUT="${DATA}/${PROJECTNAME}/${PHENOTYPE}/ldsc/sumstats/${PHENOTYPE}" +elif [ -n "$TRAIT" ]; then + mkdir -p ${DATA}/traits/sumstats + SUMSTATS_OUTPUT="${DATA}/traits/sumstats/${TRAIT}" +fi + +### sumstats conversion for later testing +./munge_sumstats.py \ +--sumstats ${SUMSTATS_INPUT} \ +--snp ${snp} \ +--N-col ${N} \ +--a1 ${a1} \ +--a2 ${a2} \ +--p ${p} \ +--frq ${frq} \ +--out ${SUMSTATS_OUTPUT} \ +--merge-alleles ${SNPLIST} \ +--chunksize ${CHUNKSIZE} + diff --git a/SCRIPTS/ldsc/environment.yml b/SCRIPTS/ldsc/environment.yml new file mode 100644 index 0000000..a0c1966 --- /dev/null +++ b/SCRIPTS/ldsc/environment.yml @@ -0,0 +1,13 @@ +name: ldsc +channels: +- bioconda +dependencies: +- python=2.7 +- bitarray=0.8 +- nose=1.3 +- pybedtools=0.7 +- pip +- pip: + - scipy==0.18 + - pandas + - numpy==1.16 diff --git a/SCRIPTS/ldsc/ldsc.conf b/SCRIPTS/ldsc/ldsc.conf new file mode 100644 index 0000000..45553db --- /dev/null +++ b/SCRIPTS/ldsc/ldsc.conf @@ -0,0 +1,19 @@ +ROOT="/Users/esmulde2" +LDSC="${ROOT}/ldsc" +PHENOTYPE="META_cox_DEAD_ALL" +TRAIT="cIMT" +# path to map with ld +EUR_LD="${LDSC}/eur_w_ld_chr/" +# path to merge allele snplist +SNPLIST="${LDSC}/w_hm3.snplist" + +snp="rsid" # SNP ID needs to be rs format +N="n" #sample size (N) +a1="effect_allele" #effect allele +a2="other_alle" # other allele +p="p_value" #P-value +frq="effect_allele_frequency" #EAF + +DATA="/Volumes/MacExtern/UMC/Werk/GENIUS-CHD/ldsc" + +INPUT="${DATA}/input/${PHENOTYPE}.b37.gwaslab.ssf.tsv.gz" \ No newline at end of file diff --git a/SCRIPTS/ldsc/ldsc.heritability.sh b/SCRIPTS/ldsc/ldsc.heritability.sh new file mode 100644 index 0000000..17e297d --- /dev/null +++ b/SCRIPTS/ldsc/ldsc.heritability.sh @@ -0,0 +1,103 @@ +#!/bin/bash +#ldsc.heritability.sh +###Download ldsc by GitHub instructions +###In environment.yml change pandas=0.20 to simply pandas +###set up environment in miniconda3 bin (not anaconda) + +# activate ldsc conda environment +source /hpc/local/Rocky8/dhl_ec/software/mambaforge3/bin/activate ldsc +# set up paths +# path to ldsc scripts +#ROOT="/Users/esmulde2" + +#data folder +#DATA="/Volumes/MacExtern/UMC/Werk/GENIUS-CHD/ldsc" +DATA="" +# +## path to map with ld +#REF_LD="${LDSC}/eur_w_ld_chr/" +## path to merge allele snplist +#SNPLIST="${LDSC}/w_hm3.snplist" + +# Default values +PHENOTYPE="" +TRAIT="" +REF_LD="" +#REF_LD="${LDSC}/eur_w_ld_chr/" + +# Function to display usage information +usage() { + echo "Usage: $0 [-p ] [Options]" 1>&2 + echo "Options:" 1>&2 + echo " -p : Specify the phenotype." 1>&2 + echo " -p : Specify the trait." 1>&2 + echo " -d : Specify the map for data" 1>&2 + echo " -l : Specify the map with REF_LD info." 1>&2 + exit 1 +} +# Parsing command-line options +while getopts ":p:t:d:j:l:" opt; do + case ${opt} in + p) PHENOTYPE=$OPTARG ;; + t) TRAIT=$OPTARG ;; + d) DATA=$OPTARG ;; + j) PROJECTNAME=$OPTARG ;; + l) REF_LD=$OPTARG ;; + :) echo "Option -$OPTARG requires an argument." >&2; exit 1 ;; + \?) echo "Invalid option: -$OPTARG" >&2; exit 1 ;; + esac +done + +#LDSC="${DATA}/ldsc" +LDSC="/hpc/dhl_ec/esmulders/ldsc" +cd ${LDSC} + +# Ensure either PHENOTYPE or TRAIT is provided +if [ -z "$PHENOTYPE" ] && [ -z "$TRAIT" ]; then + echo "Error: Either phenotype or trait must be specified." >&2 + usage +fi + +# Perform actions based on PHENOTYPE +if [ -n "$PHENOTYPE" ]; then + echo "PHENOTYPE: ${PHENOTYPE}" + #H2_INPUT="${DATA}/${PROJECTNAME}/${PHENOTYPE}/ldsc/sumstats/${PHENOTYPE}.sumstats.gz" + H2_INPUT="${DATA}/ldsc/sumstats/${PHENOTYPE}.sumstats.gz" + #mkdir -p "${DATA}/${PROJECTNAME}/${PHENOTYPE}/ldsc/heritability/" + mkdir -p "${DATA}/ldsc/heritability/" + #H2_OUTPUT="${DATA}/${PROJECTNAME}/${PHENOTYPE}/ldsc/heritability/${PHENOTYPE}_h2" + H2_OUTPUT="${DATA}/ldsc/heritability/${PHENOTYPE}_h2" + + # Ensure input file exists + if [ ! -f "$H2_INPUT" ]; then + echo "Error: Input file ${H2_INPUT} not found." >&2 + exit 1 + fi + + ### h2 heritability + ./ldsc.py \ + --h2 ${H2_INPUT} \ + --ref-ld-chr ${REF_LD} \ + --w-ld-chr ${REF_LD} \ + --out ${H2_OUTPUT} +fi + +# Perform actions based on TRAIT +if [ -n "$TRAIT" ]; then + echo "TRAIT: ${TRAIT}" + H2_INPUT="${DATA}/ldsc/traits/sumstats/${TRAIT}.sumstats.gz" + mkdir -p "${DATA}/traits/heritability" + H2_OUTPUT="${DATA}/traits/heritability/${TRAIT}_h2" + # Ensure input file exists + if [ ! -f "$H2_INPUT" ]; then + echo "Error: Input file ${H2_INPUT} not found." >&2 + exit 1 + fi + + ### h2 heritability + ./ldsc.py \ + --h2 ${H2_INPUT} \ + --ref-ld-chr ${REF_LD} \ + --w-ld-chr ${REF_LD} \ + --out ${H2_OUTPUT} +fi diff --git a/SCRIPTS/ldsc/ldsc.py b/SCRIPTS/ldsc/ldsc.py new file mode 100644 index 0000000..aa81340 --- /dev/null +++ b/SCRIPTS/ldsc/ldsc.py @@ -0,0 +1,660 @@ +#!/usr/bin/env python +''' +(c) 2014 Brendan Bulik-Sullivan and Hilary Finucane + +LDSC is a command line tool for estimating + 1. LD Score + 2. heritability / partitioned heritability + 3. genetic covariance / correlation + +''' +from __future__ import division +import ldscore.ldscore as ld +import ldscore.parse as ps +import ldscore.sumstats as sumstats +import ldscore.regressions as reg +import numpy as np +import pandas as pd +from subprocess import call +from itertools import product +import time, sys, traceback, argparse + + +try: + x = pd.DataFrame({'A': [1, 2, 3]}) + x.sort_values(by='A') +except AttributeError: + raise ImportError('LDSC requires pandas version >= 0.17.0') + +__version__ = '1.0.1' +MASTHEAD = "*********************************************************************\n" +MASTHEAD += "* LD Score Regression (LDSC)\n" +MASTHEAD += "* Version {V}\n".format(V=__version__) +MASTHEAD += "* (C) 2014-2019 Brendan Bulik-Sullivan and Hilary Finucane\n" +MASTHEAD += "* Broad Institute of MIT and Harvard / MIT Department of Mathematics\n" +MASTHEAD += "* GNU General Public License v3\n" +MASTHEAD += "*********************************************************************\n" +pd.set_option('display.max_rows', 500) +pd.set_option('display.max_columns', 500) +pd.set_option('display.width', 1000) +pd.set_option('precision', 4) +pd.set_option('max_colwidth',1000) +np.set_printoptions(linewidth=1000) +np.set_printoptions(precision=4) + + +def sec_to_str(t): + '''Convert seconds to days:hours:minutes:seconds''' + [d, h, m, s, n] = reduce(lambda ll, b : divmod(ll[0], b) + ll[1:], [(t, 1), 60, 60, 24]) + f = '' + if d > 0: + f += '{D}d:'.format(D=d) + if h > 0: + f += '{H}h:'.format(H=h) + if m > 0: + f += '{M}m:'.format(M=m) + + f += '{S}s'.format(S=s) + return f + + +def _remove_dtype(x): + '''Removes dtype: float64 and dtype: int64 from pandas printouts''' + x = str(x) + x = x.replace('\ndtype: int64', '') + x = x.replace('\ndtype: float64', '') + return x + + +class Logger(object): + ''' + Lightweight logging. + TODO: replace with logging module + + ''' + def __init__(self, fh): + self.log_fh = open(fh, 'wb') + + def log(self, msg): + ''' + Print to log file and stdout with a single command. + + ''' + print >>self.log_fh, msg + print msg + + +def __filter__(fname, noun, verb, merge_obj): + merged_list = None + if fname: + f = lambda x,n: x.format(noun=noun, verb=verb, fname=fname, num=n) + x = ps.FilterFile(fname) + c = 'Read list of {num} {noun} to {verb} from {fname}' + print f(c, len(x.IDList)) + merged_list = merge_obj.loj(x.IDList) + len_merged_list = len(merged_list) + if len_merged_list > 0: + c = 'After merging, {num} {noun} remain' + print f(c, len_merged_list) + else: + error_msg = 'No {noun} retained for analysis' + raise ValueError(f(error_msg, 0)) + + return merged_list + +def annot_sort_key(s): + '''For use with --cts-bin. Fixes weird pandas crosstab column order.''' + if type(s) == tuple: + s = [x.split('_')[0] for x in s] + s = map(lambda x: float(x) if x != 'min' else -float('inf'), s) + else: # type(s) = str: + s = s.split('_')[0] + if s == 'min': + s = float('-inf') + else: + s = float(s) + + return s + +def ldscore(args, log): + ''' + Wrapper function for estimating l1, l1^2, l2 and l4 (+ optionally standard errors) from + reference panel genotypes. + + Annot format is + chr snp bp cm + + ''' + + if args.bfile: + snp_file, snp_obj = args.bfile+'.bim', ps.PlinkBIMFile + ind_file, ind_obj = args.bfile+'.fam', ps.PlinkFAMFile + array_file, array_obj = args.bfile+'.bed', ld.PlinkBEDFile + + # read bim/snp + array_snps = snp_obj(snp_file) + m = len(array_snps.IDList) + log.log('Read list of {m} SNPs from {f}'.format(m=m, f=snp_file)) + if args.annot is not None: # read --annot + try: + if args.thin_annot: # annot file has only annotations + annot = ps.ThinAnnotFile(args.annot) + n_annot, ma = len(annot.df.columns), len(annot.df) + log.log("Read {A} annotations for {M} SNPs from {f}".format(f=args.annot, + A=n_annot, M=ma)) + annot_matrix = annot.df.values + annot_colnames = annot.df.columns + keep_snps = None + else: + annot = ps.AnnotFile(args.annot) + n_annot, ma = len(annot.df.columns) - 4, len(annot.df) + log.log("Read {A} annotations for {M} SNPs from {f}".format(f=args.annot, + A=n_annot, M=ma)) + annot_matrix = np.array(annot.df.iloc[:,4:]) + annot_colnames = annot.df.columns[4:] + keep_snps = None + if np.any(annot.df.SNP.values != array_snps.df.SNP.values): + raise ValueError('The .annot file must contain the same SNPs in the same'+\ + ' order as the .bim file.') + except Exception: + log.log('Error parsing .annot file') + raise + + elif args.extract is not None: # --extract + keep_snps = __filter__(args.extract, 'SNPs', 'include', array_snps) + annot_matrix, annot_colnames, n_annot = None, None, 1 + + + elif args.cts_bin is not None and args.cts_breaks is not None: # --cts-bin + cts_fnames = sumstats._splitp(args.cts_bin) # read filenames + args.cts_breaks = args.cts_breaks.replace('N','-') # replace N with negative sign + try: # split on x + breaks = [[float(x) for x in y.split(',')] for y in args.cts_breaks.split('x')] + except ValueError as e: + raise ValueError('--cts-breaks must be a comma-separated list of numbers: ' + +str(e.args)) + + if len(breaks) != len(cts_fnames): + raise ValueError('Need to specify one set of breaks for each file in --cts-bin.') + + if args.cts_names: + cts_colnames = [str(x) for x in args.cts_names.split(',')] + if len(cts_colnames) != len(cts_fnames): + msg = 'Must specify either no --cts-names or one value for each file in --cts-bin.' + raise ValueError(msg) + + else: + cts_colnames = ['ANNOT'+str(i) for i in xrange(len(cts_fnames))] + + log.log('Reading numbers with which to bin SNPs from {F}'.format(F=args.cts_bin)) + + cts_levs = [] + full_labs = [] + for i,fh in enumerate(cts_fnames): + vec = ps.read_cts(cts_fnames[i], array_snps.df.SNP.values) + + max_cts = np.max(vec) + min_cts = np.min(vec) + cut_breaks = list(breaks[i]) + name_breaks = list(cut_breaks) + if np.all(cut_breaks >= max_cts) or np.all(cut_breaks <= min_cts): + raise ValueError('All breaks lie outside the range of the cts variable.') + + if np.all(cut_breaks <= max_cts): + name_breaks.append(max_cts) + cut_breaks.append(max_cts+1) + + if np.all(cut_breaks >= min_cts): + name_breaks.append(min_cts) + cut_breaks.append(min_cts-1) + + name_breaks.sort() + cut_breaks.sort() + n_breaks = len(cut_breaks) + # so that col names are consistent across chromosomes with different max vals + name_breaks[0] = 'min' + name_breaks[-1] = 'max' + name_breaks = [str(x) for x in name_breaks] + labs = [name_breaks[i]+'_'+name_breaks[i+1] for i in xrange(n_breaks-1)] + cut_vec = pd.Series(pd.cut(vec, bins=cut_breaks, labels=labs)) + cts_levs.append(cut_vec) + full_labs.append(labs) + + annot_matrix = pd.concat(cts_levs, axis=1) + annot_matrix.columns = cts_colnames + # crosstab -- for now we keep empty columns + annot_matrix = pd.crosstab(annot_matrix.index, + [annot_matrix[i] for i in annot_matrix.columns], dropna=False, + colnames=annot_matrix.columns) + + # add missing columns + if len(cts_colnames) > 1: + for x in product(*full_labs): + if x not in annot_matrix.columns: + annot_matrix[x] = 0 + else: + for x in full_labs[0]: + if x not in annot_matrix.columns: + annot_matrix[x] = 0 + + annot_matrix = annot_matrix[sorted(annot_matrix.columns, key=annot_sort_key)] + if len(cts_colnames) > 1: + # flatten multi-index + annot_colnames = ['_'.join([cts_colnames[i]+'_'+b for i,b in enumerate(c)]) + for c in annot_matrix.columns] + else: + annot_colnames = [cts_colnames[0]+'_'+b for b in annot_matrix.columns] + + annot_matrix = np.matrix(annot_matrix) + keep_snps = None + n_annot = len(annot_colnames) + if np.any(np.sum(annot_matrix, axis=1) == 0): + # This exception should never be raised. For debugging only. + raise ValueError('Some SNPs have no annotation in --cts-bin. This is a bug!') + + else: + annot_matrix, annot_colnames, keep_snps = None, None, None, + n_annot = 1 + + # read fam + array_indivs = ind_obj(ind_file) + n = len(array_indivs.IDList) + log.log('Read list of {n} individuals from {f}'.format(n=n, f=ind_file)) + # read keep_indivs + if args.keep: + keep_indivs = __filter__(args.keep, 'individuals', 'include', array_indivs) + else: + keep_indivs = None + + # read genotype array + log.log('Reading genotypes from {fname}'.format(fname=array_file)) + geno_array = array_obj(array_file, n, array_snps, keep_snps=keep_snps, + keep_indivs=keep_indivs, mafMin=args.maf) + + # filter annot_matrix down to only SNPs passing MAF cutoffs + if annot_matrix is not None: + annot_keep = geno_array.kept_snps + annot_matrix = annot_matrix[annot_keep,:] + + # determine block widths + x = np.array((args.ld_wind_snps, args.ld_wind_kb, args.ld_wind_cm), dtype=bool) + if np.sum(x) != 1: + raise ValueError('Must specify exactly one --ld-wind option') + + if args.ld_wind_snps: + max_dist = args.ld_wind_snps + coords = np.array(xrange(geno_array.m)) + elif args.ld_wind_kb: + max_dist = args.ld_wind_kb*1000 + coords = np.array(array_snps.df['BP'])[geno_array.kept_snps] + elif args.ld_wind_cm: + max_dist = args.ld_wind_cm + coords = np.array(array_snps.df['CM'])[geno_array.kept_snps] + + block_left = ld.getBlockLefts(coords, max_dist) + if block_left[len(block_left)-1] == 0 and not args.yes_really: + error_msg = 'Do you really want to compute whole-chomosome LD Score? If so, set the ' + error_msg += '--yes-really flag (warning: it will use a lot of time / memory)' + raise ValueError(error_msg) + + scale_suffix = '' + if args.pq_exp is not None: + log.log('Computing LD with pq ^ {S}.'.format(S=args.pq_exp)) + msg = 'Note that LD Scores with pq raised to a nonzero power are' + msg += 'not directly comparable to normal LD Scores.' + log.log(msg) + scale_suffix = '_S{S}'.format(S=args.pq_exp) + pq = np.matrix(geno_array.maf*(1-geno_array.maf)).reshape((geno_array.m, 1)) + pq = np.power(pq, args.pq_exp) + + if annot_matrix is not None: + annot_matrix = np.multiply(annot_matrix, pq) + else: + annot_matrix = pq + + log.log("Estimating LD Score.") + lN = geno_array.ldScoreVarBlocks(block_left, args.chunk_size, annot=annot_matrix) + col_prefix = "L2"; file_suffix = "l2" + + if n_annot == 1: + ldscore_colnames = [col_prefix+scale_suffix] + else: + ldscore_colnames = [y+col_prefix+scale_suffix for y in annot_colnames] + + # print .ldscore. Output columns: CHR, BP, RS, [LD Scores] + out_fname = args.out + '.' + file_suffix + '.ldscore' + new_colnames = geno_array.colnames + ldscore_colnames + df = pd.DataFrame.from_records(np.c_[geno_array.df, lN]) + df.columns = new_colnames + if args.print_snps: + if args.print_snps.endswith('gz'): + print_snps = pd.read_csv(args.print_snps, header=None, compression='gzip') + elif args.print_snps.endswith('bz2'): + print_snps = pd.read_csv(args.print_snps, header=None, compression='bz2') + else: + print_snps = pd.read_csv(args.print_snps, header=None) + if len(print_snps.columns) > 1: + raise ValueError('--print-snps must refer to a file with a one column of SNP IDs.') + log.log('Reading list of {N} SNPs for which to print LD Scores from {F}'.format(\ + F=args.print_snps, N=len(print_snps))) + + print_snps.columns=['SNP'] + df = df.ix[df.SNP.isin(print_snps.SNP),:] + if len(df) == 0: + raise ValueError('After merging with --print-snps, no SNPs remain.') + else: + msg = 'After merging with --print-snps, LD Scores for {N} SNPs will be printed.' + log.log(msg.format(N=len(df))) + + l2_suffix = '.gz' + log.log("Writing LD Scores for {N} SNPs to {f}.gz".format(f=out_fname, N=len(df))) + df.drop(['CM','MAF'], axis=1).to_csv(out_fname, sep="\t", header=True, index=False, + float_format='%.3f') + call(['gzip', '-f', out_fname]) + if annot_matrix is not None: + M = np.atleast_1d(np.squeeze(np.asarray(np.sum(annot_matrix, axis=0)))) + ii = geno_array.maf > 0.05 + M_5_50 = np.atleast_1d(np.squeeze(np.asarray(np.sum(annot_matrix[ii,:], axis=0)))) + else: + M = [geno_array.m] + M_5_50 = [np.sum(geno_array.maf > 0.05)] + + # print .M + fout_M = open(args.out + '.'+ file_suffix +'.M','wb') + print >>fout_M, '\t'.join(map(str,M)) + fout_M.close() + + # print .M_5_50 + fout_M_5_50 = open(args.out + '.'+ file_suffix +'.M_5_50','wb') + print >>fout_M_5_50, '\t'.join(map(str,M_5_50)) + fout_M_5_50.close() + + # print annot matrix + if (args.cts_bin is not None) and not args.no_print_annot: + out_fname_annot = args.out + '.annot' + new_colnames = geno_array.colnames + ldscore_colnames + annot_df = pd.DataFrame(np.c_[geno_array.df, annot_matrix]) + annot_df.columns = new_colnames + del annot_df['MAF'] + log.log("Writing annot matrix produced by --cts-bin to {F}".format(F=out_fname+'.gz')) + annot_df.to_csv(out_fname_annot, sep="\t", header=True, index=False) + call(['gzip', '-f', out_fname_annot]) + + # print LD Score summary + pd.set_option('display.max_rows', 200) + log.log('\nSummary of LD Scores in {F}'.format(F=out_fname+l2_suffix)) + t = df.ix[:,4:].describe() + log.log( t.ix[1:,:] ) + + np.seterr(divide='ignore', invalid='ignore') # print NaN instead of weird errors + # print correlation matrix including all LD Scores and sample MAF + log.log('') + log.log('MAF/LD Score Correlation Matrix') + log.log( df.ix[:,4:].corr() ) + + # print condition number + if n_annot > 1: # condition number of a column vector w/ nonzero var is trivially one + log.log('\nLD Score Matrix Condition Number') + cond_num = np.linalg.cond(df.ix[:,5:]) + log.log( reg.remove_brackets(str(np.matrix(cond_num))) ) + if cond_num > 10000: + log.log('WARNING: ill-conditioned LD Score Matrix!') + + # summarize annot matrix if there is one + if annot_matrix is not None: + # covariance matrix + x = pd.DataFrame(annot_matrix, columns=annot_colnames) + log.log('\nAnnotation Correlation Matrix') + log.log( x.corr() ) + + # column sums + log.log('\nAnnotation Matrix Column Sums') + log.log(_remove_dtype(x.sum(axis=0))) + + # row sums + log.log('\nSummary of Annotation Matrix Row Sums') + row_sums = x.sum(axis=1).describe() + log.log(_remove_dtype(row_sums)) + + np.seterr(divide='raise', invalid='raise') + + +parser = argparse.ArgumentParser() +parser.add_argument('--out', default='ldsc', type=str, + help='Output filename prefix. If --out is not set, LDSC will use ldsc as the ' + 'defualt output filename prefix.') +# Basic LD Score Estimation Flags' +parser.add_argument('--bfile', default=None, type=str, + help='Prefix for Plink .bed/.bim/.fam file') +parser.add_argument('--l2', default=False, action='store_true', + help='Estimate l2. Compatible with both jackknife and non-jackknife.') +# Filtering / Data Management for LD Score +parser.add_argument('--extract', default=None, type=str, + help='File with SNPs to include in LD Score estimation. ' + 'The file should contain one SNP ID per row.') +parser.add_argument('--keep', default=None, type=str, + help='File with individuals to include in LD Score estimation. ' + 'The file should contain one individual ID per row.') +parser.add_argument('--ld-wind-snps', default=None, type=int, + help='Specify the window size to be used for estimating LD Scores in units of ' + '# of SNPs. You can only specify one --ld-wind-* option.') +parser.add_argument('--ld-wind-kb', default=None, type=float, + help='Specify the window size to be used for estimating LD Scores in units of ' + 'kilobase-pairs (kb). You can only specify one --ld-wind-* option.') +parser.add_argument('--ld-wind-cm', default=None, type=float, + help='Specify the window size to be used for estimating LD Scores in units of ' + 'centiMorgans (cM). You can only specify one --ld-wind-* option.') +parser.add_argument('--print-snps', default=None, type=str, + help='This flag tells LDSC to only print LD Scores for the SNPs listed ' + '(one ID per row) in PRINT_SNPS. The sum r^2 will still include SNPs not in ' + 'PRINT_SNPs. This is useful for reducing the number of LD Scores that have to be ' + 'read into memory when estimating h2 or rg.' ) +# Fancy LD Score Estimation Flags +parser.add_argument('--annot', default=None, type=str, + help='Filename prefix for annotation file for partitioned LD Score estimation. ' + 'LDSC will automatically append .annot or .annot.gz to the filename prefix. ' + 'See docs/file_formats_ld for a definition of the .annot format.') +parser.add_argument('--thin-annot', action='store_true', default=False, + help='This flag says your annot files have only annotations, with no SNP, CM, CHR, BP columns.') +parser.add_argument('--cts-bin', default=None, type=str, + help='This flag tells LDSC to compute partitioned LD Scores, where the partition ' + 'is defined by cutting one or several continuous variable[s] into bins. ' + 'The argument to this flag should be the name of a single file or a comma-separated ' + 'list of files. The file format is two columns, with SNP IDs in the first column ' + 'and the continuous variable in the second column. ') +parser.add_argument('--cts-breaks', default=None, type=str, + help='Use this flag to specify names for the continuous variables cut into bins ' + 'with --cts-bin. For each continuous variable, specify breaks as a comma-separated ' + 'list of breakpoints, and separate the breakpoints for each variable with an x. ' + 'For example, if binning on MAF and distance to gene (in kb), ' + 'you might set --cts-breaks 0.1,0.25,0.4x10,100,1000 ') +parser.add_argument('--cts-names', default=None, type=str, + help='Use this flag to specify names for the continuous variables cut into bins ' + 'with --cts-bin. The argument to this flag should be a comma-separated list of ' + 'names. For example, if binning on DAF and distance to gene, you might set ' + '--cts-bin DAF,DIST_TO_GENE ') +parser.add_argument('--per-allele', default=False, action='store_true', + help='Setting this flag causes LDSC to compute per-allele LD Scores, ' + 'i.e., \ell_j := \sum_k p_k(1-p_k)r^2_{jk}, where p_k denotes the MAF ' + 'of SNP j. ') +parser.add_argument('--pq-exp', default=None, type=float, + help='Setting this flag causes LDSC to compute LD Scores with the given scale factor, ' + 'i.e., \ell_j := \sum_k (p_k(1-p_k))^a r^2_{jk}, where p_k denotes the MAF ' + 'of SNP j and a is the argument to --pq-exp. ') +parser.add_argument('--no-print-annot', default=False, action='store_true', + help='By defualt, seting --cts-bin or --cts-bin-add causes LDSC to print ' + 'the resulting annot matrix. Setting --no-print-annot tells LDSC not ' + 'to print the annot matrix. ') +parser.add_argument('--maf', default=None, type=float, + help='Minor allele frequency lower bound. Default is MAF > 0.') +# Basic Flags for Working with Variance Components +parser.add_argument('--h2', default=None, type=str, + help='Filename for a .sumstats[.gz] file for one-phenotype LD Score regression. ' + '--h2 requires at minimum also setting the --ref-ld and --w-ld flags.') +parser.add_argument('--h2-cts', default=None, type=str, + help='Filename for a .sumstats[.gz] file for cell-type-specific analysis. ' + '--h2-cts requires the --ref-ld-chr, --w-ld, and --ref-ld-chr-cts flags.') +parser.add_argument('--rg', default=None, type=str, + help='Comma-separated list of prefixes of .chisq filed for genetic correlation estimation.') +parser.add_argument('--ref-ld', default=None, type=str, + help='Use --ref-ld to tell LDSC which LD Scores to use as the predictors in the LD ' + 'Score regression. ' + 'LDSC will automatically append .l2.ldscore/.l2.ldscore.gz to the filename prefix.') +parser.add_argument('--ref-ld-chr', default=None, type=str, + help='Same as --ref-ld, but will automatically concatenate .l2.ldscore files split ' + 'across 22 chromosomes. LDSC will automatically append .l2.ldscore/.l2.ldscore.gz ' + 'to the filename prefix. If the filename prefix contains the symbol @, LDSC will ' + 'replace the @ symbol with chromosome numbers. Otherwise, LDSC will append chromosome ' + 'numbers to the end of the filename prefix.' + 'Example 1: --ref-ld-chr ld/ will read ld/1.l2.ldscore.gz ... ld/22.l2.ldscore.gz' + 'Example 2: --ref-ld-chr ld/@_kg will read ld/1_kg.l2.ldscore.gz ... ld/22_kg.l2.ldscore.gz') +parser.add_argument('--w-ld', default=None, type=str, + help='Filename prefix for file with LD Scores with sum r^2 taken over SNPs included ' + 'in the regression. LDSC will automatically append .l2.ldscore/.l2.ldscore.gz.') +parser.add_argument('--w-ld-chr', default=None, type=str, + help='Same as --w-ld, but will read files split into 22 chromosomes in the same ' + 'manner as --ref-ld-chr.') +parser.add_argument('--overlap-annot', default=False, action='store_true', + help='This flag informs LDSC that the partitioned LD Scores were generates using an ' + 'annot matrix with overlapping categories (i.e., not all row sums equal 1), ' + 'and prevents LDSC from displaying output that is meaningless with overlapping categories.') +parser.add_argument('--print-coefficients',default=False,action='store_true', + help='when categories are overlapping, print coefficients as well as heritabilities.') +parser.add_argument('--frqfile', type=str, + help='For use with --overlap-annot. Provides allele frequencies to prune to common ' + 'snps if --not-M-5-50 is not set.') +parser.add_argument('--frqfile-chr', type=str, + help='Prefix for --frqfile files split over chromosome.') +parser.add_argument('--no-intercept', action='store_true', + help = 'If used with --h2, this constrains the LD Score regression intercept to equal ' + '1. If used with --rg, this constrains the LD Score regression intercepts for the h2 ' + 'estimates to be one and the intercept for the genetic covariance estimate to be zero.') +parser.add_argument('--intercept-h2', action='store', default=None, + help = 'Intercepts for constrained-intercept single-trait LD Score regression.') +parser.add_argument('--intercept-gencov', action='store', default=None, + help = 'Intercepts for constrained-intercept cross-trait LD Score regression.' + ' Must have same length as --rg. The first entry is ignored.') +parser.add_argument('--M', default=None, type=str, + help='# of SNPs (if you don\'t want to use the .l2.M files that came with your .l2.ldscore.gz files)') +parser.add_argument('--two-step', default=None, type=float, + help='Test statistic bound for use with the two-step estimator. Not compatible with --no-intercept and --constrain-intercept.') +parser.add_argument('--chisq-max', default=None, type=float, + help='Max chi^2.') +parser.add_argument('--ref-ld-chr-cts', default=None, type=str, + help='Name of a file that has a list of file name prefixes for cell-type-specific analysis.') +parser.add_argument('--print-all-cts', action='store_true', default=False) + +# Flags for both LD Score estimation and h2/gencor estimation +parser.add_argument('--print-cov', default=False, action='store_true', + help='For use with --h2/--rg. This flag tells LDSC to print the ' + 'covaraince matrix of the estimates.') +parser.add_argument('--print-delete-vals', default=False, action='store_true', + help='If this flag is set, LDSC will print the block jackknife delete-values (' + 'i.e., the regression coefficeints estimated from the data with a block removed). ' + 'The delete-values are formatted as a matrix with (# of jackknife blocks) rows and ' + '(# of LD Scores) columns.') +# Flags you should almost never use +parser.add_argument('--chunk-size', default=50, type=int, + help='Chunk size for LD Score calculation. Use the default.') +parser.add_argument('--pickle', default=False, action='store_true', + help='Store .l2.ldscore files as pickles instead of gzipped tab-delimited text.') +parser.add_argument('--yes-really', default=False, action='store_true', + help='Yes, I really want to compute whole-chromosome LD Score.') +parser.add_argument('--invert-anyway', default=False, action='store_true', + help="Force LDSC to attempt to invert ill-conditioned matrices.") +parser.add_argument('--n-blocks', default=200, type=int, + help='Number of block jackknife blocks.') +parser.add_argument('--not-M-5-50', default=False, action='store_true', + help='This flag tells LDSC to use the .l2.M file instead of the .l2.M_5_50 file.') +parser.add_argument('--return-silly-things', default=False, action='store_true', + help='Force ldsc to return silly genetic correlation estimates.') +parser.add_argument('--no-check-alleles', default=False, action='store_true', + help='For rg estimation, skip checking whether the alleles match. This check is ' + 'redundant for pairs of chisq files generated using munge_sumstats.py and the ' + 'same argument to the --merge-alleles flag.') +# transform to liability scale +parser.add_argument('--samp-prev',default=None, + help='Sample prevalence of binary phenotype (for conversion to liability scale).') +parser.add_argument('--pop-prev',default=None, + help='Population prevalence of binary phenotype (for conversion to liability scale).') + +if __name__ == '__main__': + + args = parser.parse_args() + if args.out is None: + raise ValueError('--out is required.') + + log = Logger(args.out+'.log') + try: + defaults = vars(parser.parse_args('')) + opts = vars(args) + non_defaults = [x for x in opts.keys() if opts[x] != defaults[x]] + header = MASTHEAD + header += "Call: \n" + header += './ldsc.py \\\n' + options = ['--'+x.replace('_','-')+' '+str(opts[x])+' \\' for x in non_defaults] + header += '\n'.join(options).replace('True','').replace('False','') + header = header[0:-1]+'\n' + log.log(header) + log.log('Beginning analysis at {T}'.format(T=time.ctime())) + start_time = time.time() + if args.n_blocks <= 1: + raise ValueError('--n-blocks must be an integer > 1.') + if args.bfile is not None: + if args.l2 is None: + raise ValueError('Must specify --l2 with --bfile.') + if args.annot is not None and args.extract is not None: + raise ValueError('--annot and --extract are currently incompatible.') + if args.cts_bin is not None and args.extract is not None: + raise ValueError('--cts-bin and --extract are currently incompatible.') + if args.annot is not None and args.cts_bin is not None: + raise ValueError('--annot and --cts-bin are currently incompatible.') + if (args.cts_bin is not None) != (args.cts_breaks is not None): + raise ValueError('Must set both or neither of --cts-bin and --cts-breaks.') + if args.per_allele and args.pq_exp is not None: + raise ValueError('Cannot set both --per-allele and --pq-exp (--per-allele is equivalent to --pq-exp 1).') + if args.per_allele: + args.pq_exp = 1 + + + ldscore(args, log) + # summary statistics + elif (args.h2 or args.rg or args.h2_cts) and (args.ref_ld or args.ref_ld_chr) and (args.w_ld or args.w_ld_chr): + if args.h2 is not None and args.rg is not None: + raise ValueError('Cannot set both --h2 and --rg.') + if args.ref_ld and args.ref_ld_chr: + raise ValueError('Cannot set both --ref-ld and --ref-ld-chr.') + if args.w_ld and args.w_ld_chr: + raise ValueError('Cannot set both --w-ld and --w-ld-chr.') + if (args.samp_prev is not None) != (args.pop_prev is not None): + raise ValueError('Must set both or neither of --samp-prev and --pop-prev.') + + if not args.overlap_annot or args.not_M_5_50: + if args.frqfile is not None or args.frqfile_chr is not None: + log.log('The frequency file is unnecessary and is being ignored.') + args.frqfile = None + args.frqfile_chr = None + if args.overlap_annot and not args.not_M_5_50: + if not ((args.frqfile and args.ref_ld) or (args.frqfile_chr and args.ref_ld_chr)): + raise ValueError('Must set either --frqfile and --ref-ld or --frqfile-chr and --ref-ld-chr') + + if args.rg: + sumstats.estimate_rg(args, log) + elif args.h2: + sumstats.estimate_h2(args, log) + elif args.h2_cts: + sumstats.cell_type_specific(args, log) + + # bad flags + else: + print header + print 'Error: no analysis selected.' + print 'ldsc.py -h describes options.' + except Exception: + ex_type, ex, tb = sys.exc_info() + log.log( traceback.format_exc(ex) ) + raise + finally: + log.log('Analysis finished at {T}'.format(T=time.ctime()) ) + time_elapsed = round(time.time()-start_time,2) + log.log('Total time elapsed: {T}'.format(T=sec_to_str(time_elapsed))) diff --git a/SCRIPTS/ldsc/ldsc.pyc b/SCRIPTS/ldsc/ldsc.pyc new file mode 100644 index 0000000..00c97e8 Binary files /dev/null and b/SCRIPTS/ldsc/ldsc.pyc differ diff --git a/SCRIPTS/ldsc/ldsc.rg.sh b/SCRIPTS/ldsc/ldsc.rg.sh new file mode 100644 index 0000000..d71edb6 --- /dev/null +++ b/SCRIPTS/ldsc/ldsc.rg.sh @@ -0,0 +1,82 @@ +#!/bin/bash + +###Download ldsc by GitHub instructions +###In environment.yml change pandas=0.20 to simply pandas +###set up environment in miniconda3 bin (not anaconda) + +# activate ldsc conda environment +source /hpc/local/Rocky8/dhl_ec/software/mambaforge3/bin/activate ldsc + +# set up paths +# path to ldsc scripts +#ROOT="/Users/esmulde2" + +#data folder +DATA="" +#DATA="/Volumes/MacExtern/UMC/Werk/GENIUS-CHD" + +# Default values +PHENOTYPE="" +TRAIT="" +REF_LD="" + +# Function to display usage information +usage() { + echo "Usage: $0 [-p ] [-t ] [-i ] [Options]" 1>&2 + echo "Options:" 1>&2 + echo " -p : Specify the phenotype." 1>&2 + echo " -t : Specify the trait." 1>&2 + echo " -d : Specify the map for data" 1>&2 + echo " -l : Specify the map with eur_ld info." 1>&2 + exit 1 +} +# Parsing command-line options +while getopts ":p:t:d:j:l:" opt; do + case ${opt} in + p) PHENOTYPE=$OPTARG ;; + t) TRAIT=$OPTARG ;; + d) DATA=$OPTARG ;; + j) PROJECTNAME=$OPTARG ;; + l) REF_LD=$OPTARG ;; + :) echo "Option -$OPTARG requires an argument." >&2; exit 1 ;; + \?) echo "Invalid option: -$OPTARG" >&2; exit 1 ;; + esac +done + +#echo "$DATA" +#echo "$REF_LD" +#LDSC="${DATA}/ldsc" +LDSC="/hpc/dhl_ec/esmulders/ldsc" + +cd ${LDSC} +# Check if either phenotype or trait is provided +if [[ -z "${PHENOTYPE}" || -z "${TRAIT}" ]]; then + echo "Error: Please specify a phenotype and a trait." 1>&2 + exit 1 +fi + + + + +## path to GENIUS_CHD phenotypes input file +PHENO_INPUT="${DATA}/ldsc/sumstats/${PHENOTYPE}.sumstats.gz" + +# mkdir -p "${DATA}/ldsc/heritability/${PHENOTYPE}" + +# path to input file for heritability +TRAIT_INPUT="${DATA}/ldsc/traits/sumstats/${TRAIT}.sumstats.gz" +#TRAIT_INPUT="/hpc/dhl_ec/esmulders/MetaGWASToolKit/RESOURCES/traits/sumstats/${TRAIT}.sumstats.gz" + +# path to output file for heritability +PHENOTYPE_TRAIT="${DATA}/ldsc/heritability/${PHENOTYPE}_${TRAIT}" + +##phenotype comparison with trait +./ldsc.py \ +--rg ${PHENO_INPUT},${TRAIT_INPUT} \ +--ref-ld-chr ${REF_LD} \ +--w-ld-chr ${REF_LD} \ +--out ${PHENOTYPE_TRAIT} + + +#rm ${DATA}/ldsc/sumstats/${PHENOTYPE}.sumstats.gz +#rm /hpc/dhl_ec/esmulders/MetaGWASToolKit/RESOURCES/traits/sumstats/${TRAIT}.sumstats.gz \ No newline at end of file diff --git a/SCRIPTS/ldsc/ldsc.run.sh b/SCRIPTS/ldsc/ldsc.run.sh new file mode 100644 index 0000000..ec6ee08 --- /dev/null +++ b/SCRIPTS/ldsc/ldsc.run.sh @@ -0,0 +1,193 @@ +#!/bin/bash + +# SLURM settings +#SBATCH --job-name=ldsc # Job name +#SBATCH --mail-type=FAIL # Mail events (NONE, BEGIN, END, FAIL, ALL) +#SBATCH --mail-user=e.j.a.smulders-2@umcutrecht.nl # Where to send mail +#SBATCH --nodes=1 # Run on one node +#SBATCH --ntasks=1 # Run on a single CPU +#SBATCH --mem=32G # Job memory request +#SBATCH --time=1:00:00 # Time limit hrs:min:sec +#SBATCH --output=ldsc.out # Standard output and error log + +###Download ldsc by GitHub instructions +###In environment.yml change pandas=0.20 to simply pandas +###set up environment in miniconda3 bin (not anaconda) + +# set up paths +# path to ldsc scripts +#ROOT="/Users/esmulde2" + + +#data folder +#DATA="/Volumes/MacExtern/UMC/Werk/GENIUS-CHD/ldsc" +#DATA="/Volumes/MacExtern/UMC/Werk/GENIUS-CHD" +DATA="" +PROJECTNAME="" +PHENOTYPE="" +TRAIT_FILE="" +#DATA="/Volumes/MacExtern/UMC/Werk/GENIUS-CHD" +# path to map with ld +REF_LD="" +REF="" +#REF_LD="/Volumes/MacExtern/UMC/Werk/references/eur_w_ld_chr/" +# path to merge allele snplist +SNPLIST="${LDSC}/w_hm3.snplist" +CHUNKSIZE= + +path="" +r="" +n="" +a="" +b="" +p="" +f="" + +#PHENOTYPES=("META_cox_DEAD_ALL" "META_logistic_DEAD_ALL") +#TRAITS=("AD" "Na" "K") +data_analysed="" +# Function to display usage information +usage() { + echo "Usage: $0 [-p ] [Options]" 1>&2 + echo "Options:" 1>&2 + echo " -p : Specify the phenotypes." 1>&2 + echo " -t : Specify the traits." 1>&2 + echo " -w : Specify trait or phenotype analysis." 1>&2 + echo " -d : Specify the map for data" 1>&2 + echo " -l : Specify the map with ref_ld info." 1>&2 + echo " -h : Specify the snplist file." 1>&2 + exit 1 +} +# Parsing command-line options +while getopts ":p:t:w:d:j:i:l:h:r:n:a:b:v:f:c:" opt; do + case ${opt} in + p) PHENOTYPE=$OPTARG ;; + t) TRAIT_FILE=$OPTARG ;; + w) data_analysed=$OPTARG ;; + d) DATA=$OPTARG ;; + j) PROJECTNAME=$OPTARG ;; + i) path=$OPTARG ;; + l) REF_LD=$OPTARG ;; + h) SNPLIST=$OPTARG ;; + r) r=$OPTARG ;; + n) n=$OPTARG ;; + a) a=$OPTARG ;; + b) b=$OPTARG ;; + v) v=$OPTARG ;; + f) f=$OPTARG ;; + c) CHUNKSIZE=$OPTARG ;; + :) echo "Option -$OPTARG requires an argument." >&2; exit 1 ;; + \?) echo "Invalid option: -$OPTARG" >&2; exit 1 ;; + esac +done +LDSC="/hpc/dhl_ec/esmulders/MetaGWASToolKit/SCRIPTS/ldsc" +#LDSC="${DATA}/ldsc" + +#echo "${path}" + +# Validate input for data_analysed +if [ "$data_analysed" != "PHENOTYPE" ] && [ "$data_analysed" != "TRAIT" ]; then + echo "Invalid value for -w option. Please specify either 'PHENOTYPE' or 'TRAIT'." + exit 1 +fi + +# Check if phenotype file is provided and read the contents +# if [ "$data_analysed" == "PHENOTYPE" ]; then +# if [ -n "$PHENOTYPE_FILE" ]; then +# PHENOTYPES=() +# SAMPLESIZES=() +# while IFS=',' read -r phenotype sample_size; do +# PHENOTYPES+=("$phenotype") +# SAMPLESIZES+=("$sample_size") +# done < "$PHENOTYPE_FILE" +# if [ ${#PHENOTYPES[@]} -eq 0 ]; then +# echo "Phenotype file is empty or not properly formatted." +# exit 1 +# fi +# else +# echo "Phenotype file (-p) is required." +# exit 1 +# fi + +# # Also read the trait file for genetic correlation analysis +# if [ -n "$TRAIT_FILE" ]; then +# TRAITS=() +# while read -r line; do +# trait=$(echo $line | awk '{print $1}') +# TRAITS+=("$trait") +# done < "$TRAIT_FILE" +# if [ ${#TRAITS[@]} -eq 0 ]; then +# echo "Trait file is empty or not properly formatted." +# exit 1 +# fi +# else +# echo "Trait file (-t) is required for genetic correlation analysis." +# exit 1 +# fi +# fi + + +# # Check if trait file is provided and read the contents if data_analysed is TRAIT +# if [ "$data_analysed" == "TRAIT" ] && [ -n "$TRAIT_FILE" ]; then +# TRAITS=() +# while read -r line; do +# trait=$(echo $line | awk '{print $1}') +# TRAITS+=("$trait") +# done < "$TRAIT_FILE" +# if [ ${#TRAITS[@]} -eq 0 ]; then +# echo "Trait file is empty or not properly formatted." +# exit 1 +# fi +# fi + + + +# Check if phenotype file is provided and read the contents +if [ "$data_analysed" == "PHENOTYPE" ]; then + if [ -z "$PHENOTYPE" ]; then + echo "Phenotype (-p) is required for phenotype analysis." + exit 1 + fi + mkdir -p ${DATA}/ldsc + mkdir -p ${DATA}/ldsc/sumstats + mkdir -p ${DATA}/ldsc/heritability + # Also read the trait file for genetic correlation analysis + if [ -n "$TRAIT_FILE" ]; then + TRAITS=() + while read -r line; do + trait=$(echo $line | awk '{print $1}') + TRAITS+=("$trait") + done < "$TRAIT_FILE" + if [ ${#TRAITS[@]} -eq 0 ]; then + echo "Trait file is empty or not properly formatted." + exit 1 + fi + else + echo "Trait file (-t) is required for genetic correlation analysis." + exit 1 + fi + echo "Analyzing PHENOTYPE: ${PHENOTYPE}" + echo "Making sumstats file..." + bash ${LDSC}/ldsc.sumstats.sh -d ${DATA} -h ${SNPLIST} -p ${PHENOTYPE} -i ${DATA}/input/${PHENOTYPE}.cojo.gz -r ${r} -n ${n} -a ${a} -b ${b} -v ${v} -f ${f} -c ${CHUNKSIZE} + echo "Running heritability analysis..." + bash ${LDSC}/ldsc.heritability.sh -d ${DATA} -j "${PROJECTNAME}" -l ${REF_LD} -p ${PHENOTYPE} + for TRAIT in "${TRAITS[@]}"; do + echo "Comparing ${PHENOTYPE} with ${TRAIT} ..." + bash ${LDSC}/ldsc.rg.sh -d ${DATA} -j "${PROJECTNAME}" -l ${REF_LD} -p ${PHENOTYPE} -t ${TRAIT} + done +fi + +if [ "$data_analysed" == "TRAIT" ]; then + if [ -z "$TRAIT_FILE" ]; then + echo "Trait file (-t) is required for trait analysis." + exit 1 + fi + mkdir -p ${DATA}/traits + mkdir -p ${DATA}/traits/sumstats + mkdir -p ${DATA}/traits/heritability + echo "Analyzing TRAIT: ${TRAIT_FILE}" + echo "Making sumstats file..." + bash ${LDSC}/ldsc.sumstats.sh -d ${DATA} -j "${PROJECTNAME}" -h ${SNPLIST} -t ${TRAIT_FILE} -i ${path} -r ${r} -n ${n} -a ${a} -b ${b} -v ${v} -f ${f} -c ${CHUNKSIZE} + echo "Running heritability analysis..." + bash ${LDSC}/ldsc.heritability.sh -d ${DATA} -j "${PROJECTNAME}" -l ${REF_LD} -t ${TRAIT_FILE} +fi \ No newline at end of file diff --git a/SCRIPTS/ldsc/ldsc.sumstats.sh b/SCRIPTS/ldsc/ldsc.sumstats.sh new file mode 100644 index 0000000..bb0e0f0 --- /dev/null +++ b/SCRIPTS/ldsc/ldsc.sumstats.sh @@ -0,0 +1,131 @@ +#!/bin/bash +#ldsc.sumstats.sh +###Download ldsc by GitHub instructions +###In environment.yml change pandas=0.20 to simply pandas +###set up environment in miniconda3 bin (not anaconda) + +# activate ldsc conda environment +source /hpc/local/Rocky8/dhl_ec/software/mambaforge3/bin/activate ldsc +# set up paths +# path to ldsc scripts + + + +# Default values +#DATA="/Volumes/MacExtern/UMC/Werk/GENIUS-CHD" +DATA="" +PHENOTYPE="" +TRAIT="" +SNPLIST="" +CHUNKSIZE= +r="" +N="" +a1="" +a2="" +p="" +frq="" + + +# Function to display usage information +usage() { + echo "Usage: $0 [-p ] [-i ] [Options]" 1>&2 + echo "Options:" 1>&2 + echo " -p : Specify the phenotype." 1>&2 + echo " -t : Specify the trait." 1>&2 + echo " -d : Specify the map for data" 1>&2 + echo " -h : Specify the snplist file." 1>&2 + echo " -i : Specify the input file for sumstats conversion." 1>&2 + echo " -r : Specify the column name for SNP IDs." 1>&2 + echo " -n : Specify the column name for sample size (N)." 1>&2 + echo " -a : Specify the column name for effect allele (a1)." 1>&2 + echo " -b : Specify the column name for other allele (a2)." 1>&2 + echo " -v : Specify the column name for P-value." 1>&2 + echo " -f : Specify the column name for effect allele frequency." 1>&2 + exit 1 +} + + +# Parsing command-line options +while getopts ":p:t:d:j:h:i:r:n:a:b:v:f:c:" opt; do + case ${opt} in + p) PHENOTYPE=$OPTARG ;; + t) TRAIT=$OPTARG ;; + d) DATA=$OPTARG ;; + j) PROJECTNAME=$OPTARG ;; + h) SNPLIST=$OPTARG ;; + i) INPUT=$OPTARG ;; + r) snp=$OPTARG ;; + n) N=$OPTARG ;; + a) a1=$OPTARG ;; + b) a2=$OPTARG ;; + v) p=$OPTARG ;; + f) frq=$OPTARG ;; + c) CHUNKSIZE=$OPTARG ;; + :) echo "Option -$OPTARG requires an argument." >&2; exit 1 ;; + \?) echo "Invalid option: -$OPTARG" >&2; exit 1 ;; + esac +done + + +# Debugging: Print parsed options +#LDSC="${DATA}/ldsc" +#software +LDSC="/hpc/dhl_ec/esmulders/ldsc" +cd ${LDSC} + + + +# Ensure either PHENOTYPE or TRAIT is provided +if [ -z "$PHENOTYPE" ] && [ -z "$TRAIT" ]; then + echo "Error: Either phenotype or trait must be specified." >&2 + usage +fi + + +## path to phenotypes input file for sumstats conversion + +# echo "PHENOTYPE: ${PHENOTYPE}" +# echo "TRAIT: ${TRAIT}" +# echo "DATA: ${DATA}" +# echo "INPUT: ${INPUT}" +# echo "snp: ${snp}" +# echo "N: ${N}" +# echo "a1: ${a1}" +# echo "a2: ${a2}" +# echo "p: ${p}" +# echo "frq: ${frq}" +# SUMSTATS_INPUT="${DATA}/input/${PHENOTYPE}.b37.gwaslab.ssf.tsv.gz" + + +# Determine output file based on PHENOTYPE or TRAIT +if [ -n "$PHENOTYPE" ]; then + SUMSTATS_INPUT="${INPUT}" +elif [ -n "$TRAIT" ]; then + SUMSTATS_INPUT="${INPUT}" +fi + +# Determine output file based on PHENOTYPE or TRAIT +if [ -n "$PHENOTYPE" ]; then + #mkdir -p ${DATA}/${PROJECTNAME}/${PHENOTYPE}/ldsc/sumstats + mkdir -p ${DATA}/ldsc/sumstats + #SUMSTATS_OUTPUT="${DATA}/${PROJECTNAME}/${PHENOTYPE}/ldsc/sumstats/${PHENOTYPE}" + SUMSTATS_OUTPUT="${DATA}/ldsc/sumstats/${PHENOTYPE}" +elif [ -n "$TRAIT" ]; then + mkdir -p ${DATA}/ldsc/traits/sumstats + SUMSTATS_OUTPUT="${DATA}/ldsc/traits/sumstats/${TRAIT}" + #SUMSTATS_OUTPUT="/hpc/dhl_ec/esmulders/MetaGWASToolKit/RESOURCES/traits/sumstats/${TRAIT}" +fi + +### sumstats conversion for later testing +./munge_sumstats.py \ +--sumstats ${SUMSTATS_INPUT} \ +--snp ${snp} \ +--N-col ${N} \ +--a1 ${a1} \ +--a2 ${a2} \ +--p ${p} \ +--frq ${frq} \ +--out ${SUMSTATS_OUTPUT} \ +--merge-alleles ${SNPLIST} \ +--chunksize ${CHUNKSIZE} + diff --git a/SCRIPTS/ldsc/ldsc_script.sh b/SCRIPTS/ldsc/ldsc_script.sh new file mode 100644 index 0000000..84c3080 --- /dev/null +++ b/SCRIPTS/ldsc/ldsc_script.sh @@ -0,0 +1,79 @@ +#!/bin/bash + +###Download ldsc by GitHub instructions +###In environment.yml change pandas=0.20 to simply pandas +###set up environment in miniconda3 bin (not anaconda) + +# activate ldsc conda environment +source /Users/esmulde2/miniconda3/bin/activate ldsc + +# set up paths +# path to ldsc scripts +ROOT="/Users/esmulde2" +LDSC="${ROOT}/ldsc" +cd ${LDSC} +#data folder +DATA="/Volumes/MacExtern/UMC/Werk/GENIUS-CHD/ldsc" + +# path to map with ld +EUR_LD="${LDSC}/eur_w_ld_chr/" +# path to merge allele snplist +SNPLIST="${LDSC}/w_hm3.snplist" + +PHENOTYPE="META_cox_DEAD_ALL" + + + +## path to GENIUS_CHD phenotypes input file for sumstats conversion +SUMSTATS_INPUT="${DATA}/input/${PHENOTYPE}.b37.gwaslab.ssf.tsv.gz" +## path to output file for sumstats conversion +SUMSTATS_OUTPUT="${DATA}/sumstats/${PHENOTYPE}" +## path to input file for heritability +H2_INPUT="${DATA}/sumstats/${PHENOTYPE}.sumstats.gz" +## path to output file for heritability +H2_OUTPUT="${DATA}/heritability/${PHENOTYPE}_h2" + +##CAD +#SUMSTATS_INPUT="${DATA}/input/MILLIONHEARTS.b37.gwaslab.ssf.tsv.gz" +# path to output file for sumstats conversion +#SUMSTATS_OUTPUT="${DATA}/sumstats/MILLIONHEARTS" +# path to input file for heritability +CAD_INPUT="${DATA}/sumstats/MILLIONHEARTS.sumstats.gz" +# path to output file for heritability +PHENOTYPE_CAD="${DATA}/heritability/${PHENOTYPE}_MILLIONHEARTS" + + + +## column names +snp="rsid" # SNP ID needs to be rs format +N="n" #sample size (N) +a1="effect_allele" #effect allele +a2="other_alle" # other allele +p="p_value" #P-value +frq="effect_allele_frequency" #EAF + +### sumstats conversion for later testing +./munge_sumstats.py \ +--sumstats ${SUMSTATS_INPUT} \ +--snp ${snp} \ +--N-col ${N} \ +--a1 ${a1} \ +--a2 ${a2} \ +--p ${p} \ +--frq ${frq} \ +--out ${SUMSTATS_OUTPUT} \ +--merge-alleles ${SNPLIST} + +### h2 heritability +./ldsc.py \ +--h2 ${H2_INPUT} \ +--ref-ld-chr ${EUR_LD} \ +--w-ld-chr ${EUR_LD} \ +--out ${H2_OUTPUT} + +##phenotype comparison with CAD +./ldsc.py \ +--rg ${H2_INPUT},${CAD_INPUT} \ +--ref-ld-chr ${EUR_LD} \ +--w-ld-chr ${EUR_LD} \ +--out ${PHENOTYPE_CAD} diff --git a/SCRIPTS/ldsc/ldscore/__init__.py b/SCRIPTS/ldsc/ldscore/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/SCRIPTS/ldsc/ldscore/__init__.pyc b/SCRIPTS/ldsc/ldscore/__init__.pyc new file mode 100644 index 0000000..1cb4c44 Binary files /dev/null and b/SCRIPTS/ldsc/ldscore/__init__.pyc differ diff --git a/SCRIPTS/ldsc/ldscore/irwls.py b/SCRIPTS/ldsc/ldscore/irwls.py new file mode 100644 index 0000000..d635a2c --- /dev/null +++ b/SCRIPTS/ldsc/ldscore/irwls.py @@ -0,0 +1,196 @@ +''' +(c) 2015 Brendan Bulik-Sullivan and Hilary Finucane + +Iterativey re-weighted least squares. + +''' +from __future__ import division +import numpy as np +import jackknife as jk + + +class IRWLS(object): + + ''' + Iteratively re-weighted least squares (FLWS). + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + update_func : function + Transforms output of np.linalg.lstsq to new weights. + n_blocks : int + Number of jackknife blocks (for estimating SE via block jackknife). + w : np.matrix with shape (n, 1) + Initial regression weights (default is the identity matrix). These should be on the + inverse CVF scale. + slow : bool + Use slow block jackknife? (Mostly for testing) + + Attributes + ---------- + est : np.matrix with shape (1, p) + IRWLS estimate. + jknife_est : np.matrix with shape (1, p) + Jackknifed estimate. + jknife_var : np.matrix with shape (1, p) + Variance of jackknifed estimate. + jknife_se : np.matrix with shape (1, p) + Standard error of jackknifed estimate, equal to sqrt(jknife_var). + jknife_cov : np.matrix with shape (p, p) + Covariance matrix of jackknifed estimate. + delete_values : np.matrix with shape (n_blocks, p) + Jackknife delete values. + + Methods + ------- + wls(x, y, w) : + Weighted Least Squares. + _weight(x, w) : + Weight x by w. + + ''' + + def __init__(self, x, y, update_func, n_blocks, w=None, slow=False, separators=None): + n, p = jk._check_shape(x, y) + if w is None: + w = np.ones_like(y) + if w.shape != (n, 1): + raise ValueError( + 'w has shape {S}. w must have shape ({N}, 1).'.format(S=w.shape, N=n)) + + jknife = self.irwls( + x, y, update_func, n_blocks, w, slow=slow, separators=separators) + self.est = jknife.est + self.jknife_se = jknife.jknife_se + self.jknife_est = jknife.jknife_est + self.jknife_var = jknife.jknife_var + self.jknife_cov = jknife.jknife_cov + self.delete_values = jknife.delete_values + self.separators = jknife.separators + + @classmethod + def irwls(cls, x, y, update_func, n_blocks, w, slow=False, separators=None): + ''' + Iteratively re-weighted least squares (IRWLS). + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + update_func: function + Transforms output of np.linalg.lstsq to new weights. + n_blocks : int + Number of jackknife blocks (for estimating SE via block jackknife). + w : np.matrix with shape (n, 1) + Initial regression weights. + slow : bool + Use slow block jackknife? (Mostly for testing) + separators : list or None + Block jackknife block boundaries (optional). + + Returns + ------- + jknife : jk.LstsqJackknifeFast + Block jackknife regression with the final IRWLS weights. + + ''' + (n, p) = x.shape + if y.shape != (n, 1): + raise ValueError( + 'y has shape {S}. y must have shape ({N}, 1).'.format(S=y.shape, N=n)) + if w.shape != (n, 1): + raise ValueError( + 'w has shape {S}. w must have shape ({N}, 1).'.format(S=w.shape, N=n)) + + w = np.sqrt(w) + for i in xrange(2): # update this later + new_w = np.sqrt(update_func(cls.wls(x, y, w))) + if new_w.shape != w.shape: + print 'IRWLS update:', new_w.shape, w.shape + raise ValueError('New weights must have same shape.') + else: + w = new_w + + x = cls._weight(x, w) + y = cls._weight(y, w) + if slow: + jknife = jk.LstsqJackknifeSlow( + x, y, n_blocks, separators=separators) + else: + jknife = jk.LstsqJackknifeFast( + x, y, n_blocks, separators=separators) + + return jknife + + @classmethod + def wls(cls, x, y, w): + ''' + Weighted least squares. + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + w : np.matrix with shape (n, 1) + Regression weights (1/CVF scale). + + Returns + ------- + coef : list with four elements (coefficients, residuals, rank, singular values) + Output of np.linalg.lstsq + + ''' + (n, p) = x.shape + if y.shape != (n, 1): + raise ValueError( + 'y has shape {S}. y must have shape ({N}, 1).'.format(S=y.shape, N=n)) + if w.shape != (n, 1): + raise ValueError( + 'w has shape {S}. w must have shape ({N}, 1).'.format(S=w.shape, N=n)) + + x = cls._weight(x, w) + y = cls._weight(y, w) + coef = np.linalg.lstsq(x, y) + return coef + + @classmethod + def _weight(cls, x, w): + ''' + Weight x by w. + + Parameters + ---------- + x : np.matrix with shape (n, p) + Rows are observations. + w : np.matrix with shape (n, 1) + Regression weights (1 / sqrt(CVF) scale). + + Returns + ------- + x_new : np.matrix with shape (n, p) + x_new[i,j] = x[i,j] * w'[i], where w' is w normalized to have sum 1. + + Raises + ------ + ValueError : + If any element of w is <= 0 (negative weights are not meaningful in WLS). + + ''' + if np.any(w <= 0): + raise ValueError('Weights must be > 0') + (n, p) = x.shape + if w.shape != (n, 1): + raise ValueError( + 'w has shape {S}. w must have shape (n, 1).'.format(S=w.shape)) + + w = w / float(np.sum(w)) + x_new = np.multiply(x, w) + return x_new diff --git a/SCRIPTS/ldsc/ldscore/irwls.pyc b/SCRIPTS/ldsc/ldscore/irwls.pyc new file mode 100644 index 0000000..718337a Binary files /dev/null and b/SCRIPTS/ldsc/ldscore/irwls.pyc differ diff --git a/SCRIPTS/ldsc/ldscore/jackknife.py b/SCRIPTS/ldsc/ldscore/jackknife.py new file mode 100644 index 0000000..d9d0cba --- /dev/null +++ b/SCRIPTS/ldsc/ldscore/jackknife.py @@ -0,0 +1,514 @@ +''' +(c) 2014 Brendan Bulik-Sullivan and Hilary Finucane + +Fast block jackknives. + +Everything in this module deals with 2D numpy arrays. 1D data are represented as arrays +with dimension (N, 1) or (1, N), to avoid bugs arising from numpy treating (N, ) as +a fundamentally different shape from (N, 1). The convention in this module is for the +first dimension to represent # of data points (or # of blocks in a block jackknife, since +a block is like a datapoint), and for the second dimension to represent the dimensionality +of the data. + +''' + +from __future__ import division +import numpy as np +from scipy.optimize import nnls +np.seterr(divide='raise', invalid='raise') + + +def _check_shape(x, y): + '''Check that x and y have the correct shapes (for regression jackknives).''' + if len(x.shape) != 2 or len(y.shape) != 2: + raise ValueError('x and y must be 2D arrays.') + if x.shape[0] != y.shape[0]: + raise ValueError( + 'Number of datapoints in x != number of datapoints in y.') + if y.shape[1] != 1: + raise ValueError('y must have shape (n_snp, 1)') + n, p = x.shape + if p > n: + raise ValueError('More dimensions than datapoints.') + + return (n, p) + + +def _check_shape_block(xty_block_values, xtx_block_values): + '''Check that xty_block_values and xtx_block_values have correct shapes.''' + if xtx_block_values.shape[0:2] != xty_block_values.shape: + raise ValueError( + 'Shape of xty_block_values must equal shape of first two dimensions of xty_block_values.') + if len(xtx_block_values.shape) < 3: + raise ValueError('xtx_block_values must be a 3D array.') + if xtx_block_values.shape[1] != xtx_block_values.shape[2]: + raise ValueError( + 'Last two axes of xtx_block_values must have same dimension.') + + return xtx_block_values.shape[0:2] + + +class Jackknife(object): + + ''' + Base class for jackknife objects. Input involves x,y, so this base class is tailored + for statistics computed from independent and dependent variables (e.g., regressions). + The __delete_vals_to_pseudovalues__ and __jknife__ methods will still be useful for other + sorts of statistics, but the __init__ method will need to be overriden. + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + n_blocks : int + Number of jackknife blocks + *args, **kwargs : + Arguments for inheriting jackknives. + + Attributes + ---------- + n_blocks : int + Number of jackknife blocks + p : int + Dimensionality of the independent varianble + N : int + Number of datapoints (equal to x.shape[0]) + + Methods + ------- + jknife(pseudovalues): + Computes jackknife estimate and variance from the jackknife pseudovalues. + delete_vals_to_pseudovalues(delete_vals, est): + Converts delete values and the whole-data estimate to pseudovalues. + get_separators(): + Returns (approximately) evenly-spaced jackknife block boundaries. + ''' + + def __init__(self, x, y, n_blocks=None, separators=None): + self.N, self.p = _check_shape(x, y) + if separators is not None: + if max(separators) != self.N: + raise ValueError( + 'Max(separators) must be equal to number of data points.') + if min(separators) != 0: + raise ValueError('Max(separators) must be equal to 0.') + self.separators = sorted(separators) + self.n_blocks = len(separators) - 1 + elif n_blocks is not None: + self.n_blocks = n_blocks + self.separators = self.get_separators(self.N, self.n_blocks) + else: + raise ValueError('Must specify either n_blocks are separators.') + + if self.n_blocks > self.N: + raise ValueError('More blocks than data points.') + + @classmethod + def jknife(cls, pseudovalues): + ''' + Converts pseudovalues to jackknife estimate and variance. + + Parameters + ---------- + pseudovalues : np.matrix pf floats with shape (n_blocks, p) + + Returns + ------- + jknife_est : np.matrix with shape (1, p) + Jackknifed estimate. + jknife_var : np.matrix with shape (1, p) + Variance of jackknifed estimate. + jknife_se : np.matrix with shape (1, p) + Standard error of jackknifed estimate, equal to sqrt(jknife_var). + jknife_cov : np.matrix with shape (p, p) + Covariance matrix of jackknifed estimate. + + ''' + n_blocks = pseudovalues.shape[0] + jknife_cov = np.atleast_2d(np.cov(pseudovalues.T, ddof=1) / n_blocks) + jknife_var = np.atleast_2d(np.diag(jknife_cov)) + jknife_se = np.atleast_2d(np.sqrt(jknife_var)) + jknife_est = np.atleast_2d(np.mean(pseudovalues, axis=0)) + return (jknife_est, jknife_var, jknife_se, jknife_cov) + + @classmethod + def delete_values_to_pseudovalues(cls, delete_values, est): + ''' + Converts whole-data estimate and delete values to pseudovalues. + + Parameters + ---------- + delete_values : np.matrix with shape (n_blocks, p) + Delete values. + est : np.matrix with shape (1, p): + Whole-data estimate. + + Returns + ------- + pseudovalues : np.matrix with shape (n_blocks, p) + Psuedovalues. + + Raises + ------ + ValueError : + If est.shape != (1, delete_values.shape[1]) + + ''' + n_blocks, p = delete_values.shape + if est.shape != (1, p): + raise ValueError( + 'Different number of parameters in delete_values than in est.') + + return n_blocks * est - (n_blocks - 1) * delete_values + + @classmethod + def get_separators(cls, N, n_blocks): + '''Define evenly-spaced block boundaries.''' + return np.floor(np.linspace(0, N, n_blocks + 1)).astype(int) + + +class LstsqJackknifeSlow(Jackknife): + + ''' + Slow linear-regression block jackknife. This class computes delete values directly, + rather than forming delete values from block values. Useful for testing and for + non-negative least squares (which as far as I am aware does not admit a fast block + jackknife algorithm). + + Inherits from Jackknife class. + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + n_blocks : int + Number of jackknife blocks + nn: bool + Non-negative least-squares? + + Attributes + ---------- + est : np.matrix with shape (1, p) + FWLS estimate. + jknife_est : np.matrix with shape (1, p) + Jackknifed estimate. + jknife_var : np.matrix with shape (1, p) + Variance of jackknifed estimate. + jknife_se : np.matrix with shape (1, p) + Standard error of jackknifed estimate, equal to sqrt(jknife_var). + jknife_cov : np.matrix with shape (p, p) + Covariance matrix of jackknifed estimate. + delete_vals : np.matrix with shape (n_blocks, p) + Jackknife delete values. + + Methods + ------- + delete_values(x, y, func, s): + Compute delete values of func(x, y) the slow way, with blocks defined by s. + + ''' + + def __init__(self, x, y, n_blocks=None, nn=False, separators=None): + Jackknife.__init__(self, x, y, n_blocks, separators) + if nn: # non-negative least squares + func = lambda x, y: np.atleast_2d(nnls(x, np.array(y).T[0])[0]) + else: + func = lambda x, y: np.atleast_2d( + np.linalg.lstsq(x, np.array(y).T[0])[0]) + + self.est = func(x, y) + self.delete_values = self.delete_values(x, y, func, self.separators) + self.pseudovalues = self.delete_values_to_pseudovalues( + self.delete_values, self.est) + (self.jknife_est, self.jknife_var, self.jknife_se, self.jknife_cov) =\ + self.jknife(self.pseudovalues) + + @classmethod + def delete_values(cls, x, y, func, s): + ''' + Compute delete values by deleting one block at a time. + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + func : function (n, p) , (n, 1) --> (1, p) + Function of x and y to be jackknived. + s : list of ints + Block separators. + + Returns + ------- + delete_values : np.matrix with shape (n_blocks, p) + Delete block values (with n_blocks blocks defined by parameter s). + + Raises + ------ + ValueError : + If x.shape[0] does not equal y.shape[0] or x and y are not 2D. + + ''' + _check_shape(x, y) + d = [func(np.vstack([x[0:s[i], ...], x[s[i + 1]:, ...]]), np.vstack([y[0:s[i], ...], y[s[i + 1]:, ...]])) + for i in xrange(len(s) - 1)] + + return np.concatenate(d, axis=0) + + +class LstsqJackknifeFast(Jackknife): + + ''' + Fast block jackknife for linear regression. + + Inherits from Jackknife class. + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + n_blocks : int + Number of jackknife blocks + + Attributes + ---------- + est : np.matrix with shape (1, p) + FWLS estimate. + jknife_est : np.matrix with shape (1, p) + Jackknifed estimate. + jknife_var : np.matrix with shape (1, p) + Variance of jackknifed estimate. + jknife_se : np.matrix with shape (1, p) + Standard error of jackknifed estimate, equal to sqrt(jknife_var). + jknife_cov : np.matrix with shape (p, p) + Covariance matrix of jackknifed estimate. + delete_vals : np.matrix with shape (n_blocks, p) + Jackknife delete values. + + Methods + ------- + block_values(x, y, n_blocks) : + Computes block values for the regression y~x. + block_values_to_est(block_values) : + Computes whole-data estimate from block values. + block_values_to_pseudovalues(block_values, est) : + Computes pseudovalues and delete values in a single pass over the block values. + + ''' + + def __init__(self, x, y, n_blocks=None, separators=None): + Jackknife.__init__(self, x, y, n_blocks, separators) + xty, xtx = self.block_values(x, y, self.separators) + self.est = self.block_values_to_est(xty, xtx) + self.delete_values = self.block_values_to_delete_values(xty, xtx) + self.pseudovalues = self.delete_values_to_pseudovalues( + self.delete_values, self.est) + (self.jknife_est, self.jknife_var, self.jknife_se, self.jknife_cov) =\ + self.jknife(self.pseudovalues) + + @classmethod + def block_values(cls, x, y, s): + ''' + Compute block values. + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + n_blocks : int + Number of jackknife blocks + s : list of ints + Block separators. + + Returns + ------- + xty_block_values : np.matrix with shape (n_blocks, p) + Block values of X^T Y. + xtx_block_values : 3d np array with shape (n_blocks, p, p) + Block values of X^T X. + + Raises + ------ + ValueError : + If x.shape[0] does not equal y.shape[0] or x and y are not 2D. + + ''' + n, p = _check_shape(x, y) + n_blocks = len(s) - 1 + xtx_block_values = np.zeros((n_blocks, p, p)) + xty_block_values = np.zeros((n_blocks, p)) + for i in xrange(n_blocks): + xty_block_values[i, ...] = np.dot( + x[s[i]:s[i + 1], ...].T, y[s[i]:s[i + 1], ...]).reshape((1, p)) + xtx_block_values[i, ...] = np.dot( + x[s[i]:s[i + 1], ...].T, x[s[i]:s[i + 1], ...]) + + return (xty_block_values, xtx_block_values) + + @classmethod + def block_values_to_est(cls, xty_block_values, xtx_block_values): + ''' + Converts block values to the whole-data linear regression estimate. + + Parameters + ---------- + xty_block_values : np.matrix with shape (n_blocks, p) + Block values of X^T Y. + xtx_block_values : 3D np.array with shape (n_blocks, p, p) + Block values of X^T X + + Returns + ------- + est : np.matrix with shape (1, p) + Whole data estimate. + + Raises + ------ + LinAlgError : + If design matrix is singular. + ValueError : + If the last two dimensions of xtx_block_values are not equal or if the first two + dimensions of xtx_block_values do not equal the shape of xty_block_values. + + ''' + n_blocks, p = _check_shape_block(xty_block_values, xtx_block_values) + xty = np.sum(xty_block_values, axis=0) + xtx = np.sum(xtx_block_values, axis=0) + return np.linalg.solve(xtx, xty).reshape((1, p)) + + @classmethod + def block_values_to_delete_values(cls, xty_block_values, xtx_block_values): + ''' + Converts block values to delete values. + + Parameters + ---------- + xty_block_values : np.matrix with shape (n_blocks, p) + Block values of X^T Y. + xtx_block_values : 3D np.array with shape (n_blocks, p, p) + Block values of X^T X + est : np.matrix with shape (1, p) + Whole data estimate + + Returns + ------- + delete_values : np.matrix with shape (n_blocks, p) + Delete Values. + + Raises + ------ + LinAlgError : + If delete design matrix is singular. + ValueError : + If the last two dimensions of xtx_block_values are not equal or if the first two + dimensions of xtx_block_values do not equal the shape of xty_block_values. + + ''' + n_blocks, p = _check_shape_block(xty_block_values, xtx_block_values) + delete_values = np.zeros((n_blocks, p)) + xty_tot = np.sum(xty_block_values, axis=0) + xtx_tot = np.sum(xtx_block_values, axis=0) + for j in xrange(n_blocks): + delete_xty = xty_tot - xty_block_values[j] + delete_xtx = xtx_tot - xtx_block_values[j] + delete_values[j, ...] = np.linalg.solve( + delete_xtx, delete_xty).reshape((1, p)) + + return delete_values + + +class RatioJackknife(Jackknife): + + ''' + Block jackknife ratio estimate. + + Jackknife. + + Parameters + ---------- + est : float or np.array with shape (1, p) + Whole data ratio estimate + numer_delete_values : np.matrix with shape (n_blocks, p) + Delete values for the numerator. + denom_delete_values: np.matrix with shape (n_blocks, p) + Delete values for the denominator. + + Methods + ------- + delete_vals_to_pseudovalues(est, denom, num): + Converts denominator/ numerator delete values and the whole-data estimate to + pseudovalues. + + Raises + ------ + FloatingPointError : + If any entry of denom_delete_values is zero. + + Note that it is possible for the denominator to cross zero (i.e., be both positive + and negative) and still have a finite ratio estimate and SE, for example if the + numerator is fixed to 0 and the denominator is either -1 or 1. If the denominator + is noisily close to zero, then it is unlikely that the denominator will yield zero + exactly (and therefore yield an inf or nan), but delete values will be of the form + (numerator / close to zero) and -(numerator / close to zero), i.e., (big) and -(big), + and so the jackknife will (correctly) yield huge SE. + + ''' + + def __init__(self, est, numer_delete_values, denom_delete_values): + if numer_delete_values.shape != denom_delete_values.shape: + raise ValueError( + 'numer_delete_values.shape != denom_delete_values.shape.') + if len(numer_delete_values.shape) != 2: + raise ValueError('Delete values must be matrices.') + if len(est.shape) != 2 or est.shape[0] != 1 or est.shape[1] != numer_delete_values.shape[1]: + raise ValueError( + 'Shape of est does not match shape of delete values.') + + self.n_blocks = numer_delete_values.shape[0] + self.est = est + self.pseudovalues = self.delete_values_to_pseudovalues(self.est, + denom_delete_values, numer_delete_values) + (self.jknife_est, self.jknife_var, self.jknife_se, self.jknife_cov) =\ + self.jknife(self.pseudovalues) + + @classmethod + def delete_values_to_pseudovalues(cls, est, denom, numer): + ''' + Converts delete values to pseudovalues. + + Parameters + ---------- + est : np.matrix with shape (1, p) + Whole-data ratio estimate. + denom : np.matrix with shape (n_blocks, p) + Denominator delete values. + numer : np.matrix with shape (n_blocks, p) + Numerator delete values. + + Returns + ------- + pseudovalues : + Ratio Jackknife Pseudovalues. + + Raises + ------ + ValueError : + If numer.shape != denom.shape. + + ''' + n_blocks, p = denom.shape + pseudovalues = np.zeros((n_blocks, p)) + for j in xrange(0, n_blocks): + pseudovalues[j, ...] = n_blocks * est - \ + (n_blocks - 1) * numer[j, ...] / denom[j, ...] + + return pseudovalues diff --git a/SCRIPTS/ldsc/ldscore/jackknife.pyc b/SCRIPTS/ldsc/ldscore/jackknife.pyc new file mode 100644 index 0000000..0c26cf9 Binary files /dev/null and b/SCRIPTS/ldsc/ldscore/jackknife.pyc differ diff --git a/SCRIPTS/ldsc/ldscore/ldscore.py b/SCRIPTS/ldsc/ldscore/ldscore.py new file mode 100644 index 0000000..f06ca6c --- /dev/null +++ b/SCRIPTS/ldsc/ldscore/ldscore.py @@ -0,0 +1,415 @@ +from __future__ import division +import numpy as np +import bitarray as ba + + +def getBlockLefts(coords, max_dist): + ''' + Converts coordinates + max block length to the a list of coordinates of the leftmost + SNPs to be included in blocks. + + Parameters + ---------- + coords : array + Array of coordinates. Must be sorted. + max_dist : float + Maximum distance between SNPs included in the same window. + + Returns + ------- + block_left : 1D np.ndarray with same length as block_left + block_left[j] := min{k | dist(j, k) < max_dist}. + + ''' + M = len(coords) + j = 0 + block_left = np.zeros(M) + for i in xrange(M): + while j < M and abs(coords[j] - coords[i]) > max_dist: + j += 1 + + block_left[i] = j + + return block_left + + +def block_left_to_right(block_left): + ''' + Converts block lefts to block rights. + + Parameters + ---------- + block_left : array + Array of block lefts. + + Returns + ------- + block_right : 1D np.ndarray with same length as block_left + block_right[j] := max {k | block_left[k] <= j} + + ''' + M = len(block_left) + j = 0 + block_right = np.zeros(M) + for i in xrange(M): + while j < M and block_left[j] <= i: + j += 1 + + block_right[i] = j + + return block_right + + +class __GenotypeArrayInMemory__(object): + ''' + Parent class for various classes containing interfaces for files with genotype + matrices, e.g., plink .bed files, etc + ''' + def __init__(self, fname, n, snp_list, keep_snps=None, keep_indivs=None, mafMin=None): + self.m = len(snp_list.IDList) + self.n = n + self.keep_snps = keep_snps + self.keep_indivs = keep_indivs + self.df = np.array(snp_list.df[['CHR', 'SNP', 'BP', 'CM']]) + self.colnames = ['CHR', 'SNP', 'BP', 'CM'] + self.mafMin = mafMin if mafMin is not None else 0 + self._currentSNP = 0 + (self.nru, self.geno) = self.__read__(fname, self.m, n) + # filter individuals + if keep_indivs is not None: + keep_indivs = np.array(keep_indivs, dtype='int') + if np.any(keep_indivs > self.n): + raise ValueError('keep_indivs indices out of bounds') + + (self.geno, self.m, self.n) = self.__filter_indivs__(self.geno, keep_indivs, self.m, + self.n) + + if self.n > 0: + print 'After filtering, {n} individuals remain'.format(n=self.n) + else: + raise ValueError('After filtering, no individuals remain') + + # filter SNPs + if keep_snps is not None: + keep_snps = np.array(keep_snps, dtype='int') + if np.any(keep_snps > self.m): # if keep_snps is None, this returns False + raise ValueError('keep_snps indices out of bounds') + + (self.geno, self.m, self.n, self.kept_snps, self.freq) = self.__filter_snps_maf__( + self.geno, self.m, self.n, self.mafMin, keep_snps) + + if self.m > 0: + print 'After filtering, {m} SNPs remain'.format(m=self.m) + else: + raise ValueError('After filtering, no SNPs remain') + + self.df = self.df[self.kept_snps, :] + self.maf = np.minimum(self.freq, np.ones(self.m)-self.freq) + self.sqrtpq = np.sqrt(self.freq*(np.ones(self.m)-self.freq)) + self.df = np.c_[self.df, self.maf] + self.colnames.append('MAF') + + def __read__(self, fname, m, n): + raise NotImplementedError + + def __filter_indivs__(geno, keep_indivs, m, n): + raise NotImplementedError + + def __filter_maf_(geno, m, n, maf): + raise NotImplementedError + + def ldScoreVarBlocks(self, block_left, c, annot=None): + '''Computes an unbiased estimate of L2(j) for j=1,..,M.''' + func = lambda x: self.__l2_unbiased__(x, self.n) + snp_getter = self.nextSNPs + return self.__corSumVarBlocks__(block_left, c, func, snp_getter, annot) + + def ldScoreBlockJackknife(self, block_left, c, annot=None, jN=10): + func = lambda x: np.square(x) + snp_getter = self.nextSNPs + return self.__corSumBlockJackknife__(block_left, c, func, snp_getter, annot, jN) + + def __l2_unbiased__(self, x, n): + denom = n-2 if n > 2 else n # allow n<2 for testing purposes + sq = np.square(x) + return sq - (1-sq) / denom + + # general methods for calculating sums of Pearson correlation coefficients + def __corSumVarBlocks__(self, block_left, c, func, snp_getter, annot=None): + ''' + Parameters + ---------- + block_left : np.ndarray with shape (M, ) + block_left[i] = index of leftmost SNP included in LD Score of SNP i. + if c > 1, then only entries that are multiples of c are examined, and it is + assumed that block_left[a*c+i] = block_left[a*c], except at + the beginning of the chromosome where the 0th SNP is included in the window. + + c : int + Chunk size. + func : function + Function to be applied to the genotype correlation matrix. Before dotting with + annot. Examples: for biased L2, np.square. For biased L4, + lambda x: np.square(np.square(x)). For L1, lambda x: x. + snp_getter : function(int) + The method to be used to get the next SNPs (normalized genotypes? Normalized + genotypes with the minor allele as reference allele? etc) + annot: numpy array with shape (m,n_a) + SNP annotations. + + Returns + ------- + cor_sum : np.ndarray with shape (M, num_annots) + Estimates. + + ''' + m, n = self.m, self.n + block_sizes = np.array(np.arange(m) - block_left) + block_sizes = np.ceil(block_sizes / c)*c + if annot is None: + annot = np.ones((m, 1)) + else: + annot_m = annot.shape[0] + if annot_m != self.m: + raise ValueError('Incorrect number of SNPs in annot') + + n_a = annot.shape[1] # number of annotations + cor_sum = np.zeros((m, n_a)) + # b = index of first SNP for which SNP 0 is not included in LD Score + b = np.nonzero(block_left > 0) + if np.any(b): + b = b[0][0] + else: + b = m + b = int(np.ceil(b/c)*c) # round up to a multiple of c + if b > m: + c = 1 + b = m + l_A = 0 # l_A := index of leftmost SNP in matrix A + A = snp_getter(b) + rfuncAB = np.zeros((b, c)) + rfuncBB = np.zeros((c, c)) + # chunk inside of block + for l_B in xrange(0, b, c): # l_B := index of leftmost SNP in matrix B + B = A[:, l_B:l_B+c] + np.dot(A.T, B / n, out=rfuncAB) + rfuncAB = func(rfuncAB) + cor_sum[l_A:l_A+b, :] += np.dot(rfuncAB, annot[l_B:l_B+c, :]) + # chunk to right of block + b0 = b + md = int(c*np.floor(m/c)) + end = md + 1 if md != m else md + for l_B in xrange(b0, end, c): + # check if the annot matrix is all zeros for this block + chunk + # this happens w/ sparse categories (i.e., pathways) + # update the block + old_b = b + b = int(block_sizes[l_B]) + if l_B > b0 and b > 0: + # block_size can't increase more than c + # block_size can't be less than c unless it is zero + # both of these things make sense + A = np.hstack((A[:, old_b-b+c:old_b], B)) + l_A += old_b-b+c + elif l_B == b0 and b > 0: + A = A[:, b0-b:b0] + l_A = b0-b + elif b == 0: # no SNPs to left in window, e.g., after a sequence gap + A = np.array(()).reshape((n, 0)) + l_A = l_B + if l_B == md: + c = m - md + rfuncAB = np.zeros((b, c)) + rfuncBB = np.zeros((c, c)) + if b != old_b: + rfuncAB = np.zeros((b, c)) + + B = snp_getter(c) + p1 = np.all(annot[l_A:l_A+b, :] == 0) + p2 = np.all(annot[l_B:l_B+c, :] == 0) + if p1 and p2: + continue + + np.dot(A.T, B / n, out=rfuncAB) + rfuncAB = func(rfuncAB) + cor_sum[l_A:l_A+b, :] += np.dot(rfuncAB, annot[l_B:l_B+c, :]) + cor_sum[l_B:l_B+c, :] += np.dot(annot[l_A:l_A+b, :].T, rfuncAB).T + np.dot(B.T, B / n, out=rfuncBB) + rfuncBB = func(rfuncBB) + cor_sum[l_B:l_B+c, :] += np.dot(rfuncBB, annot[l_B:l_B+c, :]) + + return cor_sum + + +class PlinkBEDFile(__GenotypeArrayInMemory__): + ''' + Interface for Plink .bed format + ''' + def __init__(self, fname, n, snp_list, keep_snps=None, keep_indivs=None, mafMin=None): + self._bedcode = { + 2: ba.bitarray('11'), + 9: ba.bitarray('10'), + 1: ba.bitarray('01'), + 0: ba.bitarray('00') + } + + __GenotypeArrayInMemory__.__init__(self, fname, n, snp_list, keep_snps=keep_snps, + keep_indivs=keep_indivs, mafMin=mafMin) + + def __read__(self, fname, m, n): + if not fname.endswith('.bed'): + raise ValueError('.bed filename must end in .bed') + + fh = open(fname, 'rb') + magicNumber = ba.bitarray(endian="little") + magicNumber.fromfile(fh, 2) + bedMode = ba.bitarray(endian="little") + bedMode.fromfile(fh, 1) + e = (4 - n % 4) if n % 4 != 0 else 0 + nru = n + e + self.nru = nru + # check magic number + if magicNumber != ba.bitarray('0011011011011000'): + raise IOError("Magic number from Plink .bed file not recognized") + + if bedMode != ba.bitarray('10000000'): + raise IOError("Plink .bed file must be in default SNP-major mode") + + # check file length + self.geno = ba.bitarray(endian="little") + self.geno.fromfile(fh) + self.__test_length__(self.geno, self.m, self.nru) + return (self.nru, self.geno) + + def __test_length__(self, geno, m, nru): + exp_len = 2*m*nru + real_len = len(geno) + if real_len != exp_len: + s = "Plink .bed file has {n1} bits, expected {n2}" + raise IOError(s.format(n1=real_len, n2=exp_len)) + + def __filter_indivs__(self, geno, keep_indivs, m, n): + n_new = len(keep_indivs) + e = (4 - n_new % 4) if n_new % 4 != 0 else 0 + nru_new = n_new + e + nru = self.nru + z = ba.bitarray(m*2*nru_new, endian="little") + z.setall(0) + for e, i in enumerate(keep_indivs): + z[2*e::2*nru_new] = geno[2*i::2*nru] + z[2*e+1::2*nru_new] = geno[2*i+1::2*nru] + + self.nru = nru_new + return (z, m, n_new) + + def __filter_snps_maf__(self, geno, m, n, mafMin, keep_snps): + ''' + Credit to Chris Chang and the Plink2 developers for this algorithm + Modified from plink_filter.c + https://github.com/chrchang/plink-ng/blob/master/plink_filter.c + + Genotypes are read forwards (since we are cheating and using endian="little") + + A := (genotype) & 1010... + B := (genotype) & 0101... + C := (A >> 1) & B + + Then + + a := A.count() = missing ct + hom major ct + b := B.count() = het ct + hom major ct + c := C.count() = hom major ct + + Which implies that + + missing ct = a - c + # of indivs with nonmissing genotype = n - a + c + major allele ct = b + c + major allele frequency = (b+c)/(2*(n-a+c)) + het ct + missing ct = a + b - 2*c + + Why does bitarray not have >> ???? + + ''' + nru = self.nru + m_poly = 0 + y = ba.bitarray() + if keep_snps is None: + keep_snps = xrange(m) + kept_snps = [] + freq = [] + for e, j in enumerate(keep_snps): + z = geno[2*nru*j:2*nru*(j+1)] + A = z[0::2] + a = A.count() + B = z[1::2] + b = B.count() + c = (A & B).count() + major_ct = b + c # number of copies of the major allele + n_nomiss = n - a + c # number of individuals with nonmissing genotypes + f = major_ct / (2*n_nomiss) if n_nomiss > 0 else 0 + het_miss_ct = a+b-2*c # remove SNPs that are only either het or missing + if np.minimum(f, 1-f) > mafMin and het_miss_ct < n: + freq.append(f) + y += z + m_poly += 1 + kept_snps.append(j) + + return (y, m_poly, n, kept_snps, freq) + + def nextSNPs(self, b, minorRef=None): + ''' + Unpacks the binary array of genotypes and returns an n x b matrix of floats of + normalized genotypes for the next b SNPs, where n := number of samples. + + Parameters + ---------- + b : int + Number of SNPs to return. + minorRef: bool, default None + Should we flip reference alleles so that the minor allele is the reference? + (This is useful for computing l1 w.r.t. minor allele). + + Returns + ------- + X : np.array with dtype float64 with shape (n, b), where n := number of samples + Matrix of genotypes normalized to mean zero and variance one. If minorRef is + not None, then the minor allele will be the positive allele (i.e., two copies + of the minor allele --> a positive number). + + ''' + + try: + b = int(b) + if b <= 0: + raise ValueError("b must be > 0") + except TypeError: + raise TypeError("b must be an integer") + + if self._currentSNP + b > self.m: + s = '{b} SNPs requested, {k} SNPs remain' + raise ValueError(s.format(b=b, k=(self.m-self._currentSNP))) + + c = self._currentSNP + n = self.n + nru = self.nru + slice = self.geno[2*c*nru:2*(c+b)*nru] + X = np.array(slice.decode(self._bedcode), dtype="float64").reshape((b, nru)).T + X = X[0:n, :] + Y = np.zeros(X.shape) + for j in xrange(0, b): + newsnp = X[:, j] + ii = newsnp != 9 + avg = np.mean(newsnp[ii]) + newsnp[np.logical_not(ii)] = avg + denom = np.std(newsnp) + if denom == 0: + denom = 1 + + if minorRef is not None and self.freq[self._currentSNP + j] > 0.5: + denom = denom*-1 + + Y[:, j] = (newsnp - avg) / denom + + self._currentSNP += b + return Y diff --git a/SCRIPTS/ldsc/ldscore/ldscore.pyc b/SCRIPTS/ldsc/ldscore/ldscore.pyc new file mode 100644 index 0000000..c0c4d8d Binary files /dev/null and b/SCRIPTS/ldsc/ldscore/ldscore.pyc differ diff --git a/SCRIPTS/ldsc/ldscore/parse.py b/SCRIPTS/ldsc/ldscore/parse.py new file mode 100644 index 0000000..18fe7c9 --- /dev/null +++ b/SCRIPTS/ldsc/ldscore/parse.py @@ -0,0 +1,292 @@ +''' +(c) 2014 Brendan Bulik-Sullivan and Hilary Finucane + +This module contains functions for parsing various ldsc-defined file formats. + +''' + +from __future__ import division +import numpy as np +import pandas as pd +import os +import glob + + +def series_eq(x, y): + '''Compare series, return False if lengths not equal.''' + return len(x) == len(y) and (x == y).all() + + +def read_csv(fh, **kwargs): + return pd.read_csv(fh, delim_whitespace=True, na_values='.', **kwargs) + + +def sub_chr(s, chrom): + '''Substitute chr for @, else append chr to the end of str.''' + if '@' not in s: + s += '@' + + return s.replace('@', str(chrom)) + + +def get_present_chrs(fh, num): + '''Checks which chromosomes exist, assuming that the file base will be appended by a dot in any suffix.''' + chrs = [] + for chrom in xrange(1,num): + if glob.glob(sub_chr(fh, chrom) + '.*'): + chrs.append(chrom) + return chrs + + +def which_compression(fh): + '''Given a file prefix, figure out what sort of compression to use.''' + if os.access(fh + '.bz2', 4): + suffix = '.bz2' + compression = 'bz2' + elif os.access(fh + '.gz', 4): + suffix = '.gz' + compression = 'gzip' + elif os.access(fh, 4): + suffix = '' + compression = None + else: + raise IOError('Could not open {F}[./gz/bz2]'.format(F=fh)) + + return suffix, compression + + +def get_compression(fh): + '''Which sort of compression should we use with read_csv?''' + if fh.endswith('gz'): + compression = 'gzip' + elif fh.endswith('bz2'): + compression = 'bz2' + else: + compression = None + + return compression + + +def read_cts(fh, match_snps): + '''Reads files for --cts-bin.''' + compression = get_compression(fh) + cts = read_csv(fh, compression=compression, header=None, names=['SNP', 'ANNOT']) + if not series_eq(cts.SNP, match_snps): + raise ValueError('--cts-bin and the .bim file must have identical SNP columns.') + + return cts.ANNOT.values + + +def sumstats(fh, alleles=False, dropna=True): + '''Parses .sumstats files. See docs/file_formats_sumstats.txt.''' + dtype_dict = {'SNP': str, 'Z': float, 'N': float, 'A1': str, 'A2': str} + compression = get_compression(fh) + usecols = ['SNP', 'Z', 'N'] + if alleles: + usecols += ['A1', 'A2'] + + try: + x = read_csv(fh, usecols=usecols, dtype=dtype_dict, compression=compression) + except (AttributeError, ValueError) as e: + raise ValueError('Improperly formatted sumstats file: ' + str(e.args)) + + if dropna: + x = x.dropna(how='any') + + return x + + +def ldscore_fromlist(flist, num=None): + '''Sideways concatenation of a list of LD Score files.''' + ldscore_array = [] + for i, fh in enumerate(flist): + y = ldscore(fh, num) + if i > 0: + if not series_eq(y.SNP, ldscore_array[0].SNP): + raise ValueError('LD Scores for concatenation must have identical SNP columns.') + else: # keep SNP column from only the first file + y = y.drop(['SNP'], axis=1) + + new_col_dict = {c: c + '_' + str(i) for c in y.columns if c != 'SNP'} + y.rename(columns=new_col_dict, inplace=True) + ldscore_array.append(y) + + return pd.concat(ldscore_array, axis=1) + + +def l2_parser(fh, compression): + '''Parse LD Score files''' + x = read_csv(fh, header=0, compression=compression) + if 'MAF' in x.columns and 'CM' in x.columns: # for backwards compatibility w/ v<1.0.0 + x = x.drop(['MAF', 'CM'], axis=1) + return x + + +def annot_parser(fh, compression, frqfile_full=None, compression_frq=None): + '''Parse annot files''' + df_annot = read_csv(fh, header=0, compression=compression).drop(['SNP','CHR', 'BP', 'CM'], axis=1, errors='ignore').astype(float) + if frqfile_full is not None: + df_frq = frq_parser(frqfile_full, compression_frq) + df_annot = df_annot[(.95 > df_frq.FRQ) & (df_frq.FRQ > 0.05)] + return df_annot + + +def frq_parser(fh, compression): + '''Parse frequency files.''' + df = read_csv(fh, header=0, compression=compression) + if 'MAF' in df.columns: + df.rename(columns={'MAF': 'FRQ'}, inplace=True) + return df[['SNP', 'FRQ']] + + +def ldscore(fh, num=None): + '''Parse .l2.ldscore files, split across num chromosomes. See docs/file_formats_ld.txt.''' + suffix = '.l2.ldscore' + if num is not None: # num files, e.g., one per chromosome + chrs = get_present_chrs(fh, num+1) + first_fh = sub_chr(fh, chrs[0]) + suffix + s, compression = which_compression(first_fh) + chr_ld = [l2_parser(sub_chr(fh, i) + suffix + s, compression) for i in chrs] + x = pd.concat(chr_ld) # automatically sorted by chromosome + else: # just one file + s, compression = which_compression(fh + suffix) + x = l2_parser(fh + suffix + s, compression) + + x = x.sort_values(by=['CHR', 'BP']) # SEs will be wrong unless sorted + x = x.drop(['CHR', 'BP'], axis=1).drop_duplicates(subset='SNP') + return x + + +def M(fh, num=None, N=2, common=False): + '''Parses .l{N}.M files, split across num chromosomes. See docs/file_formats_ld.txt.''' + parsefunc = lambda y: [float(z) for z in open(y, 'r').readline().split()] + suffix = '.l' + str(N) + '.M' + if common: + suffix += '_5_50' + + if num is not None: + x = np.sum([parsefunc(sub_chr(fh, i) + suffix) for i in get_present_chrs(fh, num+1)], axis=0) + else: + x = parsefunc(fh + suffix) + + return np.array(x).reshape((1, len(x))) + + +def M_fromlist(flist, num=None, N=2, common=False): + '''Read a list of .M* files and concatenate sideways.''' + return np.hstack([M(fh, num, N, common) for fh in flist]) + + +def annot(fh_list, num=None, frqfile=None): + ''' + Parses .annot files and returns an overlap matrix. See docs/file_formats_ld.txt. + If num is not None, parses .annot files split across [num] chromosomes (e.g., the + output of parallelizing ldsc.py --l2 across chromosomes). + + ''' + annot_suffix = ['.annot' for fh in fh_list] + annot_compression = [] + if num is not None: # 22 files, one for each chromosome + chrs = get_present_chrs(fh, num+1) + for i, fh in enumerate(fh_list): + first_fh = sub_chr(fh, chrs[0]) + annot_suffix[i] + annot_s, annot_comp_single = which_compression(first_fh) + annot_suffix[i] += annot_s + annot_compression.append(annot_comp_single) + + if frqfile is not None: + frq_suffix = '.frq' + first_frqfile = sub_chr(frqfile, 1) + frq_suffix + frq_s, frq_compression = which_compression(first_frqfile) + frq_suffix += frq_s + + y = [] + M_tot = 0 + for chrom in chrs: + if frqfile is not None: + df_annot_chr_list = [annot_parser(sub_chr(fh, chrom) + annot_suffix[i], annot_compression[i], + sub_chr(frqfile, chrom) + frq_suffix, frq_compression) + for i, fh in enumerate(fh_list)] + else: + df_annot_chr_list = [annot_parser(sub_chr(fh, chrom) + annot_suffix[i], annot_compression[i]) + for i, fh in enumerate(fh_list)] + + annot_matrix_chr_list = [np.matrix(df_annot_chr) for df_annot_chr in df_annot_chr_list] + annot_matrix_chr = np.hstack(annot_matrix_chr_list) + y.append(np.dot(annot_matrix_chr.T, annot_matrix_chr)) + M_tot += len(df_annot_chr_list[0]) + + x = sum(y) + else: # just one file + for i, fh in enumerate(fh_list): + annot_s, annot_comp_single = which_compression(fh + annot_suffix[i]) + annot_suffix[i] += annot_s + annot_compression.append(annot_comp_single) + + if frqfile is not None: + frq_suffix = '.frq' + frq_s, frq_compression = which_compression(frqfile + frq_suffix) + frq_suffix += frq_s + + df_annot_list = [annot_parser(fh + annot_suffix[i], annot_compression[i], + frqfile + frq_suffix, frq_compression) for i, fh in enumerate(fh_list)] + + else: + df_annot_list = [annot_parser(fh + annot_suffix[i], annot_compression[i]) + for i, fh in enumerate(fh_list)] + + annot_matrix_list = [np.matrix(y) for y in df_annot_list] + annot_matrix = np.hstack(annot_matrix_list) + x = np.dot(annot_matrix.T, annot_matrix) + M_tot = len(df_annot_list[0]) + + return x, M_tot + + +def __ID_List_Factory__(colnames, keepcol, fname_end, header=None, usecols=None): + + class IDContainer(object): + + def __init__(self, fname): + self.__usecols__ = usecols + self.__colnames__ = colnames + self.__keepcol__ = keepcol + self.__fname_end__ = fname_end + self.__header__ = header + self.__read__(fname) + self.n = len(self.df) + + def __read__(self, fname): + end = self.__fname_end__ + if end and not fname.endswith(end): + raise ValueError('{f} filename must end in {f}'.format(f=end)) + + comp = get_compression(fname) + self.df = pd.read_csv(fname, header=self.__header__, usecols=self.__usecols__, + delim_whitespace=True, compression=comp) + + if self.__colnames__: + self.df.columns = self.__colnames__ + + if self.__keepcol__ is not None: + self.IDList = self.df.iloc[:, [self.__keepcol__]].astype('object') + + def loj(self, externalDf): + '''Returns indices of those elements of self.IDList that appear in exernalDf.''' + r = externalDf.columns[0] + l = self.IDList.columns[0] + merge_df = externalDf.iloc[:, [0]] + merge_df['keep'] = True + z = pd.merge(self.IDList, merge_df, how='left', left_on=l, right_on=r, + sort=False) + ii = z['keep'] == True + return np.nonzero(ii)[0] + + return IDContainer + + +PlinkBIMFile = __ID_List_Factory__(['CHR', 'SNP', 'CM', 'BP', 'A1', 'A2'], 1, '.bim', usecols=[0, 1, 2, 3, 4, 5]) +PlinkFAMFile = __ID_List_Factory__(['IID'], 0, '.fam', usecols=[1]) +FilterFile = __ID_List_Factory__(['ID'], 0, None, usecols=[0]) +AnnotFile = __ID_List_Factory__(None, 2, None, header=0, usecols=None) +ThinAnnotFile = __ID_List_Factory__(None, None, None, header=0, usecols=None) diff --git a/SCRIPTS/ldsc/ldscore/parse.pyc b/SCRIPTS/ldsc/ldscore/parse.pyc new file mode 100644 index 0000000..9e8cef9 Binary files /dev/null and b/SCRIPTS/ldsc/ldscore/parse.pyc differ diff --git a/SCRIPTS/ldsc/ldscore/regressions.py b/SCRIPTS/ldsc/ldscore/regressions.py new file mode 100644 index 0000000..ec8fc49 --- /dev/null +++ b/SCRIPTS/ldsc/ldscore/regressions.py @@ -0,0 +1,743 @@ +''' +(c) 2014 Brendan Bulik-Sullivan and Hilary Finucane + +Estimators of heritability and genetic correlation. + +Shape convention is (n_snp, n_annot) for all classes. +Last column = intercept. + +''' +from __future__ import division +import numpy as np +import pandas as pd +from scipy.stats import norm, chi2 +import jackknife as jk +from irwls import IRWLS +from scipy.stats import t as tdist +from collections import namedtuple +np.seterr(divide='raise', invalid='raise') + +s = lambda x: remove_brackets(str(np.matrix(x))) + + +def update_separators(s, ii): + '''s are separators with ii masked. Returns unmasked separators.''' + maplist = np.arange(len(ii))[np.squeeze(ii)] + mask_to_unmask = lambda i: maplist[i] + t = np.apply_along_axis(mask_to_unmask, 0, s[1:-1]) + t = np.hstack(((0), t, (len(ii)))) + return t + + +def p_z_norm(est, se): + '''Convert estimate and se to Z-score and P-value.''' + try: + Z = est / se + except (FloatingPointError, ZeroDivisionError): + Z = float('inf') + + P = chi2.sf(Z ** 2, 1, loc=0, scale=1) # 0 if Z=inf + return P, Z + + +def remove_brackets(x): + '''Get rid of brackets and trailing whitespace in numpy arrays.''' + return x.replace('[', '').replace(']', '').strip() + + +def append_intercept(x): + ''' + Appends an intercept term to the design matrix for a linear regression. + + Parameters + ---------- + x : np.matrix with shape (n_row, n_col) + Design matrix. Columns are predictors; rows are observations. + + Returns + ------- + x_new : np.matrix with shape (n_row, n_col+1) + Design matrix with intercept term appended. + + ''' + n_row = x.shape[0] + intercept = np.ones((n_row, 1)) + x_new = np.concatenate((x, intercept), axis=1) + return x_new + + +def remove_intercept(x): + '''Removes the last column.''' + n_col = x.shape[1] + return x[:, 0:n_col - 1] + + +def gencov_obs_to_liab(gencov_obs, P1, P2, K1, K2): + ''' + Converts genetic covariance on the observed scale in an ascertained sample to genetic + covariance on the liability scale in the population + + Parameters + ---------- + gencov_obs : float + Genetic covariance on the observed scale in an ascertained sample. + P1, P2 : float in (0,1) + Prevalences of phenotypes 1,2 in the sample. + K1, K2 : float in (0,1) + Prevalences of phenotypes 1,2 in the population. + + Returns + ------- + gencov_liab : float + Genetic covariance between liabilities in the population. + + Note: if a trait is a QT, set P = K = None. + + ''' + c1 = 1 + c2 = 1 + if P1 is not None and K1 is not None: + c1 = np.sqrt(h2_obs_to_liab(1, P1, K1)) + if P2 is not None and K2 is not None: + c2 = np.sqrt(h2_obs_to_liab(1, P2, K2)) + + return gencov_obs * c1 * c2 + + +def h2_obs_to_liab(h2_obs, P, K): + ''' + Converts heritability on the observed scale in an ascertained sample to heritability + on the liability scale in the population. + + Parameters + ---------- + h2_obs : float + Heritability on the observed scale in an ascertained sample. + P : float in (0,1) + Prevalence of the phenotype in the sample. + K : float in (0,1) + Prevalence of the phenotype in the population. + + Returns + ------- + h2_liab : float + Heritability of liability in the population. + + ''' + if np.isnan(P) and np.isnan(K): + return h2_obs + if K <= 0 or K >= 1: + raise ValueError('K must be in the range (0,1)') + if P <= 0 or P >= 1: + raise ValueError('P must be in the range (0,1)') + + thresh = norm.isf(K) + conversion_factor = K ** 2 * \ + (1 - K) ** 2 / (P * (1 - P) * norm.pdf(thresh) ** 2) + return h2_obs * conversion_factor + + +class LD_Score_Regression(object): + + def __init__(self, y, x, w, N, M, n_blocks, intercept=None, slow=False, step1_ii=None, old_weights=False): + for i in [y, x, w, M, N]: + try: + if len(i.shape) != 2: + raise TypeError('Arguments must be 2D arrays.') + except AttributeError: + raise TypeError('Arguments must be arrays.') + + n_snp, self.n_annot = x.shape + if any(i.shape != (n_snp, 1) for i in [y, w, N]): + raise ValueError( + 'N, weights and response (z1z2 or chisq) must have shape (n_snp, 1).') + if M.shape != (1, self.n_annot): + raise ValueError('M must have shape (1, n_annot).') + + M_tot = float(np.sum(M)) + x_tot = np.sum(x, axis=1).reshape((n_snp, 1)) + self.constrain_intercept = intercept is not None + self.intercept = intercept + self.n_blocks = n_blocks + tot_agg = self.aggregate(y, x_tot, N, M_tot, intercept) + initial_w = self._update_weights( + x_tot, w, N, M_tot, tot_agg, intercept) + Nbar = np.mean(N) # keep condition number low + x = np.multiply(N, x) / Nbar + if not self.constrain_intercept: + x, x_tot = append_intercept(x), append_intercept(x_tot) + yp = y + else: + yp = y - intercept + self.intercept_se = 'NA' + del y + self.twostep_filtered = None + if step1_ii is not None and self.constrain_intercept: + raise ValueError( + 'twostep is not compatible with constrain_intercept.') + elif step1_ii is not None and self.n_annot > 1: + raise ValueError( + 'twostep not compatible with partitioned LD Score yet.') + elif step1_ii is not None: + n1 = np.sum(step1_ii) + self.twostep_filtered = n_snp - n1 + x1 = x[np.squeeze(step1_ii), :] + yp1, w1, N1, initial_w1 = map( + lambda a: a[step1_ii].reshape((n1, 1)), (yp, w, N, initial_w)) + update_func1 = lambda a: self._update_func( + a, x1, w1, N1, M_tot, Nbar, ii=step1_ii) + step1_jknife = IRWLS( + x1, yp1, update_func1, n_blocks, slow=slow, w=initial_w1) + step1_int, _ = self._intercept(step1_jknife) + yp = yp - step1_int + x = remove_intercept(x) + x_tot = remove_intercept(x_tot) + update_func2 = lambda a: self._update_func( + a, x_tot, w, N, M_tot, Nbar, step1_int) + s = update_separators(step1_jknife.separators, step1_ii) + step2_jknife = IRWLS( + x, yp, update_func2, n_blocks, slow=slow, w=initial_w, separators=s) + c = np.sum(np.multiply(initial_w, x)) / \ + np.sum(np.multiply(initial_w, np.square(x))) + jknife = self._combine_twostep_jknives( + step1_jknife, step2_jknife, M_tot, c, Nbar) + elif old_weights: + initial_w = np.sqrt(initial_w) + x = IRWLS._weight(x, initial_w) + y = IRWLS._weight(yp, initial_w) + jknife = jk.LstsqJackknifeFast(x, y, n_blocks) + else: + update_func = lambda a: self._update_func( + a, x_tot, w, N, M_tot, Nbar, intercept) + jknife = IRWLS( + x, yp, update_func, n_blocks, slow=slow, w=initial_w) + + self.coef, self.coef_cov, self.coef_se = self._coef(jknife, Nbar) + self.cat, self.cat_cov, self.cat_se =\ + self._cat(jknife, M, Nbar, self.coef, self.coef_cov) + + self.tot, self.tot_cov, self.tot_se = self._tot(self.cat, self.cat_cov) + self.prop, self.prop_cov, self.prop_se =\ + self._prop(jknife, M, Nbar, self.cat, self.tot) + + self.enrichment, self.M_prop = self._enrichment( + M, M_tot, self.cat, self.tot) + if not self.constrain_intercept: + self.intercept, self.intercept_se = self._intercept(jknife) + + self.jknife = jknife + self.tot_delete_values = self._delete_vals_tot(jknife, Nbar, M) + self.part_delete_values = self._delete_vals_part(jknife, Nbar, M) + if not self.constrain_intercept: + self.intercept_delete_values = jknife.delete_values[ + :, self.n_annot] + + self.M = M + + @classmethod + def aggregate(cls, y, x, N, M, intercept=None): + if intercept is None: + intercept = cls.__null_intercept__ + + num = M * (np.mean(y) - intercept) + denom = np.mean(np.multiply(x, N)) + return num / denom + + def _update_func(self, x, ref_ld_tot, w_ld, N, M, Nbar, intercept=None, ii=None): + raise NotImplementedError + + def _delete_vals_tot(self, jknife, Nbar, M): + '''Get delete values for total h2 or gencov.''' + n_annot = self.n_annot + tot_delete_vals = jknife.delete_values[ + :, 0:n_annot] # shape (n_blocks, n_annot) + # shape (n_blocks, 1) + tot_delete_vals = np.dot(tot_delete_vals, M.T) / Nbar + return tot_delete_vals + + def _delete_vals_part(self, jknife, Nbar, M): + '''Get delete values for partitioned h2 or gencov.''' + n_annot = self.n_annot + return jknife.delete_values[:, 0:n_annot] / Nbar + + def _coef(self, jknife, Nbar): + '''Get coefficient estimates + cov from the jackknife.''' + n_annot = self.n_annot + coef = jknife.est[0, 0:n_annot] / Nbar + coef_cov = jknife.jknife_cov[0:n_annot, 0:n_annot] / Nbar ** 2 + coef_se = np.sqrt(np.diag(coef_cov)) + return coef, coef_cov, coef_se + + def _cat(self, jknife, M, Nbar, coef, coef_cov): + '''Convert coefficients to per-category h2 or gencov.''' + cat = np.multiply(M, coef) + cat_cov = np.multiply(np.dot(M.T, M), coef_cov) + cat_se = np.sqrt(np.diag(cat_cov)) + return cat, cat_cov, cat_se + + def _tot(self, cat, cat_cov): + '''Convert per-category h2 to total h2 or gencov.''' + tot = np.sum(cat) + tot_cov = np.sum(cat_cov) + tot_se = np.sqrt(tot_cov) + return tot, tot_cov, tot_se + + def _prop(self, jknife, M, Nbar, cat, tot): + '''Convert total h2 and per-category h2 to per-category proportion h2 or gencov.''' + n_annot = self.n_annot + n_blocks = jknife.delete_values.shape[0] + numer_delete_vals = np.multiply( + M, jknife.delete_values[:, 0:n_annot]) / Nbar # (n_blocks, n_annot) + denom_delete_vals = np.sum( + numer_delete_vals, axis=1).reshape((n_blocks, 1)) + denom_delete_vals = np.dot(denom_delete_vals, np.ones((1, n_annot))) + prop = jk.RatioJackknife( + cat / tot, numer_delete_vals, denom_delete_vals) + return prop.est, prop.jknife_cov, prop.jknife_se + + def _enrichment(self, M, M_tot, cat, tot): + '''Compute proportion of SNPs per-category enrichment for h2 or gencov.''' + M_prop = M / M_tot + enrichment = np.divide(cat, M) / (tot / M_tot) + return enrichment, M_prop + + def _intercept(self, jknife): + '''Extract intercept and intercept SE from block jackknife.''' + n_annot = self.n_annot + intercept = jknife.est[0, n_annot] + intercept_se = jknife.jknife_se[0, n_annot] + return intercept, intercept_se + + def _combine_twostep_jknives(self, step1_jknife, step2_jknife, M_tot, c, Nbar=1): + '''Combine free intercept and constrained intercept jackknives for --two-step.''' + n_blocks, n_annot = step1_jknife.delete_values.shape + n_annot -= 1 + if n_annot > 2: + raise ValueError( + 'twostep not yet implemented for partitioned LD Score.') + + step1_int, _ = self._intercept(step1_jknife) + est = np.hstack( + (step2_jknife.est, np.array(step1_int).reshape((1, 1)))) + delete_values = np.zeros((n_blocks, n_annot + 1)) + delete_values[:, n_annot] = step1_jknife.delete_values[:, n_annot] + delete_values[:, 0:n_annot] = step2_jknife.delete_values -\ + c * (step1_jknife.delete_values[:, n_annot] - + step1_int).reshape((n_blocks, n_annot)) # check this + pseudovalues = jk.Jackknife.delete_values_to_pseudovalues( + delete_values, est) + jknife_est, jknife_var, jknife_se, jknife_cov = jk.Jackknife.jknife( + pseudovalues) + jknife = namedtuple('jknife', + ['est', 'jknife_se', 'jknife_est', 'jknife_var', 'jknife_cov', 'delete_values']) + return jknife(est, jknife_se, jknife_est, jknife_var, jknife_cov, delete_values) + + +class Hsq(LD_Score_Regression): + + __null_intercept__ = 1 + + def __init__(self, y, x, w, N, M, n_blocks=200, intercept=None, slow=False, twostep=None, old_weights=False): + step1_ii = None + if twostep is not None: + step1_ii = y < twostep + + LD_Score_Regression.__init__(self, y, x, w, N, M, n_blocks, intercept=intercept, + slow=slow, step1_ii=step1_ii, old_weights=old_weights) + self.mean_chisq, self.lambda_gc = self._summarize_chisq(y) + if not self.constrain_intercept: + self.ratio, self.ratio_se = self._ratio( + self.intercept, self.intercept_se, self.mean_chisq) + + def _update_func(self, x, ref_ld_tot, w_ld, N, M, Nbar, intercept=None, ii=None): + ''' + Update function for IRWLS + + x is the output of np.linalg.lstsq. + x[0] is the regression coefficients + x[0].shape is (# of dimensions, 1) + the last element of x[0] is the intercept. + + intercept is None --> free intercept + intercept is not None --> constrained intercept + ''' + hsq = M * x[0][0] / Nbar + if intercept is None: + intercept = max(x[0][1]) # divide by zero error if intercept < 0 + else: + if ref_ld_tot.shape[1] > 1: + raise ValueError( + 'Design matrix has intercept column for constrained intercept regression!') + + ld = ref_ld_tot[:, 0].reshape(w_ld.shape) # remove intercept + w = self.weights(ld, w_ld, N, M, hsq, intercept, ii) + return w + + def _summarize_chisq(self, chisq): + '''Compute mean chi^2 and lambda_GC.''' + mean_chisq = np.mean(chisq) + # median and matrix don't play nice + lambda_gc = np.median(np.asarray(chisq)) / 0.4549 + return mean_chisq, lambda_gc + + def _ratio(self, intercept, intercept_se, mean_chisq): + '''Compute ratio (intercept - 1) / (mean chi^2 -1 ).''' + if mean_chisq > 1: + ratio_se = intercept_se / (mean_chisq - 1) + ratio = (intercept - 1) / (mean_chisq - 1) + else: + ratio = 'NA' + ratio_se = 'NA' + + return ratio, ratio_se + + def _overlap_output(self, category_names, overlap_matrix, M_annot, M_tot, print_coefficients): + '''LD Score regression summary for overlapping categories.''' + overlap_matrix_prop = np.zeros([self.n_annot,self.n_annot]) + for i in range(self.n_annot): + overlap_matrix_prop[i, :] = overlap_matrix[i, :] / M_annot + + prop_hsq_overlap = np.dot( + overlap_matrix_prop, self.prop.T).reshape((1, self.n_annot)) + prop_hsq_overlap_var = np.diag( + np.dot(np.dot(overlap_matrix_prop, self.prop_cov), overlap_matrix_prop.T)) + prop_hsq_overlap_se = np.sqrt( + np.maximum(0, prop_hsq_overlap_var)).reshape((1, self.n_annot)) + one_d_convert = lambda x: np.array(x).reshape(np.prod(x.shape)) + prop_M_overlap = M_annot / M_tot + enrichment = prop_hsq_overlap / prop_M_overlap + enrichment_se = prop_hsq_overlap_se / prop_M_overlap + overlap_matrix_diff = np.zeros([self.n_annot,self.n_annot]) + for i in range(self.n_annot): + if not M_tot == M_annot[0,i]: + overlap_matrix_diff[i, :] = overlap_matrix[i,:]/M_annot[0,i] - \ + (M_annot - overlap_matrix[i,:]) / (M_tot-M_annot[0,i]) + + diff_est = np.dot(overlap_matrix_diff,self.coef) + diff_cov = np.dot(np.dot(overlap_matrix_diff,self.coef_cov),overlap_matrix_diff.T) + diff_se = np.sqrt(np.diag(diff_cov)) + diff_p = ['NA' if diff_se[i]==0 else 2*tdist.sf(abs(diff_est[i]/diff_se[i]),self.n_blocks) \ + for i in range(self.n_annot)] + + df = pd.DataFrame({ + 'Category': category_names, + 'Prop._SNPs': one_d_convert(prop_M_overlap), + 'Prop._h2': one_d_convert(prop_hsq_overlap), + 'Prop._h2_std_error': one_d_convert(prop_hsq_overlap_se), + 'Enrichment': one_d_convert(enrichment), + 'Enrichment_std_error': one_d_convert(enrichment_se), + 'Enrichment_p':diff_p, + 'Coefficient': one_d_convert(self.coef), + 'Coefficient_std_error': self.coef_se, + 'Coefficient_z-score': one_d_convert(self.coef) / one_d_convert(self.coef_se) + }) + if print_coefficients: + df = df[['Category', 'Prop._SNPs', 'Prop._h2', 'Prop._h2_std_error', + 'Enrichment','Enrichment_std_error', 'Enrichment_p', + 'Coefficient', 'Coefficient_std_error','Coefficient_z-score']] + else: + df = df[['Category', 'Prop._SNPs', 'Prop._h2', 'Prop._h2_std_error', + 'Enrichment','Enrichment_std_error', 'Enrichment_p']] + return df + + + def summary(self, ref_ld_colnames=None, P=None, K=None, overlap=False): + '''Print summary of the LD Score Regression.''' + if P is not None and K is not None: + T = 'Liability' + c = h2_obs_to_liab(1, P, K) + else: + T = 'Observed' + c = 1 + + out = ['Total ' + T + ' scale h2: ' + + s(c * self.tot) + ' (' + s(c * self.tot_se) + ')'] + if self.n_annot > 1: + if ref_ld_colnames is None: + ref_ld_colnames = ['CAT_' + str(i) + for i in xrange(self.n_annot)] + + out.append('Categories: ' + ' '.join(ref_ld_colnames)) + + if not overlap: + out.append(T + ' scale h2: ' + s(c * self.cat)) + out.append(T + ' scale h2 SE: ' + s(c * self.cat_se)) + out.append('Proportion of SNPs: ' + s(self.M_prop)) + out.append('Proportion of h2g: ' + s(self.prop)) + out.append('Enrichment: ' + s(self.enrichment)) + out.append('Coefficients: ' + s(self.coef)) + out.append('Coefficient SE: ' + s(self.coef_se)) + + out.append('Lambda GC: ' + s(self.lambda_gc)) + out.append('Mean Chi^2: ' + s(self.mean_chisq)) + if self.constrain_intercept: + out.append( + 'Intercept: constrained to {C}'.format(C=s(self.intercept))) + else: + out.append( + 'Intercept: ' + s(self.intercept) + ' (' + s(self.intercept_se) + ')') + if self.mean_chisq > 1: + if self.ratio < 0: + out.append( + 'Ratio < 0 (usually indicates GC correction).') + else: + out.append( + 'Ratio: ' + s(self.ratio) + ' (' + s(self.ratio_se) + ')') + else: + out.append('Ratio: NA (mean chi^2 < 1)') + + return remove_brackets('\n'.join(out)) + + def _update_weights(self, ld, w_ld, N, M, hsq, intercept, ii=None): + if intercept is None: + intercept = self.__null_intercept__ + + return self.weights(ld, w_ld, N, M, hsq, intercept, ii) + + @classmethod + def weights(cls, ld, w_ld, N, M, hsq, intercept=None, ii=None): + ''' + Regression weights. + + Parameters + ---------- + ld : np.matrix with shape (n_snp, 1) + LD Scores (non-partitioned). + w_ld : np.matrix with shape (n_snp, 1) + LD Scores (non-partitioned) computed with sum r^2 taken over only those SNPs included + in the regression. + N : np.matrix of ints > 0 with shape (n_snp, 1) + Number of individuals sampled for each SNP. + M : float > 0 + Number of SNPs used for estimating LD Score (need not equal number of SNPs included in + the regression). + hsq : float in [0,1] + Heritability estimate. + + Returns + ------- + w : np.matrix with shape (n_snp, 1) + Regression weights. Approx equal to reciprocal of conditional variance function. + + ''' + M = float(M) + if intercept is None: + intercept = 1 + + hsq = max(hsq, 0.0) + hsq = min(hsq, 1.0) + ld = np.fmax(ld, 1.0) + w_ld = np.fmax(w_ld, 1.0) + c = hsq * N / M + het_w = 1.0 / (2 * np.square(intercept + np.multiply(c, ld))) + oc_w = 1.0 / w_ld + w = np.multiply(het_w, oc_w) + return w + + +class Gencov(LD_Score_Regression): + __null_intercept__ = 0 + + def __init__(self, z1, z2, x, w, N1, N2, M, hsq1, hsq2, intercept_hsq1, intercept_hsq2, + n_blocks=200, intercept_gencov=None, slow=False, twostep=None): + self.intercept_hsq1 = intercept_hsq1 + self.intercept_hsq2 = intercept_hsq2 + self.hsq1 = hsq1 + self.hsq2 = hsq2 + self.N1 = N1 + self.N2 = N2 + y = z1 * z2 + step1_ii = None + if twostep is not None: + step1_ii = np.logical_and(z1**2 < twostep, z2**2 < twostep) + + LD_Score_Regression.__init__(self, y, x, w, np.sqrt(N1 * N2), M, n_blocks, + intercept=intercept_gencov, slow=slow, step1_ii=step1_ii) + self.p, self.z = p_z_norm(self.tot, self.tot_se) + self.mean_z1z2 = np.mean(np.multiply(z1, z2)) + + def summary(self, ref_ld_colnames, P=None, K=None): + '''Print summary of the LD Score regression.''' + out = [] + if P is not None and K is not None and\ + all((i is not None for i in P)) and all((i is not None for i in K)): + T = 'Liability' + c = gencov_obs_to_liab(1, P[0], P[1], K[0], K[1]) + else: + T = 'Observed' + c = 1 + + out.append('Total ' + T + ' scale gencov: ' + + s(c * self.tot) + ' (' + s(c * self.tot_se) + ')') + if self.n_annot > 1: + out.append('Categories: ' + str(' '.join(ref_ld_colnames))) + out.append(T + ' scale gencov: ' + s(c * self.cat)) + out.append(T + ' scale gencov SE: ' + s(c * self.cat_se)) + out.append('Proportion of SNPs: ' + s(self.M_prop)) + out.append('Proportion of gencov: ' + s(self.prop)) + out.append('Enrichment: ' + s(self.enrichment)) + + out.append('Mean z1*z2: ' + s(self.mean_z1z2)) + if self.constrain_intercept: + out.append( + 'Intercept: constrained to {C}'.format(C=s(self.intercept))) + else: + out.append( + 'Intercept: ' + s(self.intercept) + ' (' + s(self.intercept_se) + ')') + + return remove_brackets('\n'.join(out)) + + def _update_func(self, x, ref_ld_tot, w_ld, N, M, Nbar, intercept=None, ii=None): + ''' + Update function for IRWLS + x is the output of np.linalg.lstsq. + x[0] is the regression coefficients + x[0].shape is (# of dimensions, 1) + the last element of x[0] is the intercept. + + ''' + rho_g = M * x[0][0] / Nbar + if intercept is None: # if the regression includes an intercept + intercept = x[0][1] + + # remove intercept if we have one + ld = ref_ld_tot[:, 0].reshape(w_ld.shape) + if ii is not None: + N1 = self.N1[ii].reshape((w_ld.shape)) + N2 = self.N2[ii].reshape((w_ld.shape)) + else: + N1 = self.N1 + N2 = self.N2 + + return self.weights(ld, w_ld, N1, N2, np.sum(M), self.hsq1, self.hsq2, rho_g, + intercept, self.intercept_hsq1, self.intercept_hsq2, ii) + + def _update_weights(self, ld, w_ld, sqrt_n1n2, M, rho_g, intercept, ii=None): + '''Weight function with the same signature for Hsq and Gencov.''' + w = self.weights(ld, w_ld, self.N1, self.N2, M, self.hsq1, self.hsq2, rho_g, + intercept, self.intercept_hsq1, self.intercept_hsq2) + return w + + @classmethod + def weights(cls, ld, w_ld, N1, N2, M, h1, h2, rho_g, intercept_gencov=None, + intercept_hsq1=None, intercept_hsq2=None, ii=None): + ''' + Regression weights. + + Parameters + ---------- + ld : np.matrix with shape (n_snp, 1) + LD Scores (non-partitioned) + w_ld : np.matrix with shape (n_snp, 1) + LD Scores (non-partitioned) computed with sum r^2 taken over only those SNPs included + in the regression. + M : float > 0 + Number of SNPs used for estimating LD Score (need not equal number of SNPs included in + the regression). + N1, N2 : np.matrix of ints > 0 with shape (n_snp, 1) + Number of individuals sampled for each SNP for each study. + h1, h2 : float in [0,1] + Heritability estimates for each study. + rhog : float in [0,1] + Genetic covariance estimate. + intercept : float + Genetic covariance intercept, on the z1*z2 scale (so should be Ns*rho/sqrt(N1*N2)). + + Returns + ------- + w : np.matrix with shape (n_snp, 1) + Regression weights. Approx equal to reciprocal of conditional variance function. + + ''' + M = float(M) + if intercept_gencov is None: + intercept_gencov = 0 + if intercept_hsq1 is None: + intercept_hsq1 = 1 + if intercept_hsq2 is None: + intercept_hsq2 = 1 + + h1, h2 = max(h1, 0.0), max(h2, 0.0) + h1, h2 = min(h1, 1.0), min(h2, 1.0) + rho_g = min(rho_g, 1.0) + rho_g = max(rho_g, -1.0) + ld = np.fmax(ld, 1.0) + w_ld = np.fmax(w_ld, 1.0) + a = np.multiply(N1, h1 * ld) / M + intercept_hsq1 + b = np.multiply(N2, h2 * ld) / M + intercept_hsq2 + sqrt_n1n2 = np.sqrt(np.multiply(N1, N2)) + c = np.multiply(sqrt_n1n2, rho_g * ld) / M + intercept_gencov + try: + het_w = 1.0 / (np.multiply(a, b) + np.square(c)) + except FloatingPointError: # bizarre error; should never happen + raise FloatingPointError('Why did you set hsq intercept <= 0?') + + oc_w = 1.0 / w_ld + w = np.multiply(het_w, oc_w) + return w + + +class RG(object): + + def __init__(self, z1, z2, x, w, N1, N2, M, intercept_hsq1=None, intercept_hsq2=None, + intercept_gencov=None, n_blocks=200, slow=False, twostep=None): + self.intercept_gencov = intercept_gencov + self._negative_hsq = None + n_snp, n_annot = x.shape + hsq1 = Hsq(np.square(z1), x, w, N1, M, n_blocks=n_blocks, intercept=intercept_hsq1, + slow=slow, twostep=twostep) + hsq2 = Hsq(np.square(z2), x, w, N2, M, n_blocks=n_blocks, intercept=intercept_hsq2, + slow=slow, twostep=twostep) + gencov = Gencov(z1, z2, x, w, N1, N2, M, hsq1.tot, hsq2.tot, hsq1.intercept, + hsq2.intercept, n_blocks, intercept_gencov=intercept_gencov, slow=slow, + twostep=twostep) + gencov.N1 = None # save memory + gencov.N2 = None + self.hsq1, self.hsq2, self.gencov = hsq1, hsq2, gencov + if (hsq1.tot <= 0 or hsq2.tot <= 0): + self._negative_hsq = True + self.rg_ratio = self.rg = self.rg_se = 'NA' + self.p = self.z = 'NA' + else: + rg_ratio = np.array( + gencov.tot / np.sqrt(hsq1.tot * hsq2.tot)).reshape((1, 1)) + denom_delete_values = np.sqrt( + np.multiply(hsq1.tot_delete_values, hsq2.tot_delete_values)) + rg = jk.RatioJackknife( + rg_ratio, gencov.tot_delete_values, denom_delete_values) + self.rg_jknife = float(rg.jknife_est) + self.rg_se = float(rg.jknife_se) + self.rg_ratio = float(rg_ratio) + self.p, self.z = p_z_norm(self.rg_ratio, self.rg_se) + + def summary(self, silly=False): + '''Print output of Gencor object.''' + out = [] + if self._negative_hsq: + out.append('Genetic Correlation: nan (nan) (h2 out of bounds) ') + out.append('Z-score: nan (nan) (h2 out of bounds)') + out.append('P: nan (nan) (h2 out of bounds)') + out.append('WARNING: One of the h2\'s was out of bounds.') + out.append( + 'This usually indicates a data-munging error ' + + 'or that h2 or N is low.') + elif (self.rg_ratio > 1.2 or self.rg_ratio < -1.2) and not silly: + out.append('Genetic Correlation: nan (nan) (rg out of bounds) ') + out.append('Z-score: nan (nan) (rg out of bounds)') + out.append('P: nan (nan) (rg out of bounds)') + out.append('WARNING: rg was out of bounds.') + if self.intercept_gencov is None: + out.append( + 'This often means that h2 is not significantly ' + + 'different from zero.') + else: + out.append( + 'This often means that you have constrained' + + ' the intercepts to the wrong values.') + else: + out.append( + 'Genetic Correlation: ' + s(self.rg_ratio) + + ' (' + s(self.rg_se) + ')') + out.append('Z-score: ' + s(self.z)) + out.append('P: ' + s(self.p)) + return remove_brackets('\n'.join(out)) diff --git a/SCRIPTS/ldsc/ldscore/regressions.pyc b/SCRIPTS/ldsc/ldscore/regressions.pyc new file mode 100644 index 0000000..d7bcff0 Binary files /dev/null and b/SCRIPTS/ldsc/ldscore/regressions.pyc differ diff --git a/SCRIPTS/ldsc/ldscore/sumstats.py b/SCRIPTS/ldsc/ldscore/sumstats.py new file mode 100644 index 0000000..1c57491 --- /dev/null +++ b/SCRIPTS/ldsc/ldscore/sumstats.py @@ -0,0 +1,581 @@ +''' +(c) 2014 Brendan Bulik-Sullivan and Hilary Finucane + +This module deals with getting all the data needed for LD Score regression from files +into memory and checking that the input makes sense. There is no math here. LD Score +regression is implemented in the regressions module. +''' +from __future__ import division +import numpy as np +import pandas as pd +from scipy import stats +import itertools as it +import parse as ps +import regressions as reg +import sys +import traceback +import copy +import os +import glob + + +_N_CHR = 22 +# complementary bases +COMPLEMENT = {'A': 'T', 'T': 'A', 'C': 'G', 'G': 'C'} +# bases +BASES = COMPLEMENT.keys() +# true iff strand ambiguous +STRAND_AMBIGUOUS = {''.join(x): x[0] == COMPLEMENT[x[1]] + for x in it.product(BASES, BASES) + if x[0] != x[1]} +# SNPS we want to keep (pairs of alleles) +VALID_SNPS = {x for x in map(lambda y: ''.join(y), it.product(BASES, BASES)) + if x[0] != x[1] and not STRAND_AMBIGUOUS[x]} +# T iff SNP 1 has the same alleles as SNP 2 (allowing for strand or ref allele flip). +MATCH_ALLELES = {x for x in map(lambda y: ''.join(y), it.product(VALID_SNPS, VALID_SNPS)) + # strand and ref match + if ((x[0] == x[2]) and (x[1] == x[3])) or + # ref match, strand flip + ((x[0] == COMPLEMENT[x[2]]) and (x[1] == COMPLEMENT[x[3]])) or + # ref flip, strand match + ((x[0] == x[3]) and (x[1] == x[2])) or + ((x[0] == COMPLEMENT[x[3]]) and (x[1] == COMPLEMENT[x[2]]))} # strand and ref flip +# T iff SNP 1 has the same alleles as SNP 2 w/ ref allele flip. +FLIP_ALLELES = {''.join(x): + ((x[0] == x[3]) and (x[1] == x[2])) or # strand match + # strand flip + ((x[0] == COMPLEMENT[x[3]]) and (x[1] == COMPLEMENT[x[2]])) + for x in MATCH_ALLELES} + + +def _splitp(fstr): + flist = fstr.split(',') + flist = [os.path.expanduser(os.path.expandvars(x)) for x in flist] + return flist + + +def _select_and_log(x, ii, log, msg): + '''Fiter down to rows that are True in ii. Log # of SNPs removed.''' + new_len = ii.sum() + if new_len == 0: + raise ValueError(msg.format(N=0)) + else: + x = x[ii] + log.log(msg.format(N=new_len)) + return x + + +def smart_merge(x, y): + '''Check if SNP columns are equal. If so, save time by using concat instead of merge.''' + if len(x) == len(y) and (x.index == y.index).all() and (x.SNP == y.SNP).all(): + x = x.reset_index(drop=True) + y = y.reset_index(drop=True).drop('SNP', 1) + out = pd.concat([x, y], axis=1) + else: + out = pd.merge(x, y, how='inner', on='SNP') + return out + + +def _read_ref_ld(args, log): + '''Read reference LD Scores.''' + ref_ld = _read_chr_split_files(args.ref_ld_chr, args.ref_ld, log, + 'reference panel LD Score', ps.ldscore_fromlist) + log.log( + 'Read reference panel LD Scores for {N} SNPs.'.format(N=len(ref_ld))) + return ref_ld + + +def _read_annot(args, log): + '''Read annot matrix.''' + try: + if args.ref_ld is not None: + overlap_matrix, M_tot = _read_chr_split_files(args.ref_ld_chr, args.ref_ld, log, + 'annot matrix', ps.annot, frqfile=args.frqfile) + elif args.ref_ld_chr is not None: + overlap_matrix, M_tot = _read_chr_split_files(args.ref_ld_chr, args.ref_ld, log, + 'annot matrix', ps.annot, frqfile=args.frqfile_chr) + except Exception: + log.log('Error parsing .annot file.') + raise + + return overlap_matrix, M_tot + + +def _read_M(args, log, n_annot): + '''Read M (--M, --M-file, etc).''' + if args.M: + try: + M_annot = [float(x) for x in _splitp(args.M)] + except ValueError as e: + raise ValueError('Could not cast --M to float: ' + str(e.args)) + else: + if args.ref_ld: + M_annot = ps.M_fromlist( + _splitp(args.ref_ld), common=(not args.not_M_5_50)) + elif args.ref_ld_chr: + M_annot = ps.M_fromlist( + _splitp(args.ref_ld_chr), _N_CHR, common=(not args.not_M_5_50)) + + try: + M_annot = np.array(M_annot).reshape((1, n_annot)) + except ValueError as e: + raise ValueError( + '# terms in --M must match # of LD Scores in --ref-ld.\n' + str(e.args)) + + return M_annot + + +def _read_w_ld(args, log): + '''Read regression SNP LD.''' + if (args.w_ld and ',' in args.w_ld) or (args.w_ld_chr and ',' in args.w_ld_chr): + raise ValueError( + '--w-ld must point to a single fileset (no commas allowed).') + w_ld = _read_chr_split_files(args.w_ld_chr, args.w_ld, log, + 'regression weight LD Score', ps.ldscore_fromlist) + if len(w_ld.columns) != 2: + raise ValueError('--w-ld may only have one LD Score column.') + w_ld.columns = ['SNP', 'LD_weights'] # prevent colname conflicts w/ ref ld + log.log( + 'Read regression weight LD Scores for {N} SNPs.'.format(N=len(w_ld))) + return w_ld + + +def _read_chr_split_files(chr_arg, not_chr_arg, log, noun, parsefunc, **kwargs): + '''Read files split across 22 chromosomes (annot, ref_ld, w_ld).''' + try: + if not_chr_arg: + log.log('Reading {N} from {F} ... ({p})'.format(N=noun, F=not_chr_arg, p=parsefunc.__name__)) + out = parsefunc(_splitp(not_chr_arg), **kwargs) + elif chr_arg: + f = ps.sub_chr(chr_arg, '[1-22]') + log.log('Reading {N} from {F} ... ({p})'.format(N=noun, F=f, p=parsefunc.__name__)) + out = parsefunc(_splitp(chr_arg), _N_CHR, **kwargs) + except ValueError as e: + log.log('Error parsing {N}.'.format(N=noun)) + raise e + + return out + + +def _read_sumstats(args, log, fh, alleles=False, dropna=False): + '''Parse summary statistics.''' + log.log('Reading summary statistics from {S} ...'.format(S=fh)) + sumstats = ps.sumstats(fh, alleles=alleles, dropna=dropna) + log_msg = 'Read summary statistics for {N} SNPs.' + log.log(log_msg.format(N=len(sumstats))) + m = len(sumstats) + sumstats = sumstats.drop_duplicates(subset='SNP') + if m > len(sumstats): + log.log( + 'Dropped {M} SNPs with duplicated rs numbers.'.format(M=m - len(sumstats))) + + return sumstats + + +def _check_ld_condnum(args, log, ref_ld): + '''Check condition number of LD Score matrix.''' + if len(ref_ld.shape) >= 2: + cond_num = int(np.linalg.cond(ref_ld)) + if cond_num > 100000: + if args.invert_anyway: + warn = "WARNING: LD Score matrix condition number is {C}. " + warn += "Inverting anyway because the --invert-anyway flag is set." + log.log(warn.format(C=cond_num)) + else: + warn = "WARNING: LD Score matrix condition number is {C}. " + warn += "Remove collinear LD Scores. " + raise ValueError(warn.format(C=cond_num)) + + +def _check_variance(log, M_annot, ref_ld): + '''Remove zero-variance LD Scores.''' + ii = ref_ld.ix[:, 1:].var() == 0 # NB there is a SNP column here + if ii.all(): + raise ValueError('All LD Scores have zero variance.') + else: + log.log('Removing partitioned LD Scores with zero variance.') + ii_snp = np.array([True] + list(~ii)) + ii_m = np.array(~ii) + ref_ld = ref_ld.ix[:, ii_snp] + M_annot = M_annot[:, ii_m] + + return M_annot, ref_ld, ii + + +def _warn_length(log, sumstats): + if len(sumstats) < 200000: + log.log( + 'WARNING: number of SNPs less than 200k; this is almost always bad.') + + +def _print_cov(ldscore_reg, ofh, log): + '''Prints covariance matrix of slopes.''' + log.log( + 'Printing covariance matrix of the estimates to {F}.'.format(F=ofh)) + np.savetxt(ofh, ldscore_reg.coef_cov) + + +def _print_delete_values(ldscore_reg, ofh, log): + '''Prints block jackknife delete-k values''' + log.log('Printing block jackknife delete values to {F}.'.format(F=ofh)) + np.savetxt(ofh, ldscore_reg.tot_delete_values) + +def _print_part_delete_values(ldscore_reg, ofh, log): + '''Prints partitioned block jackknife delete-k values''' + log.log('Printing partitioned block jackknife delete values to {F}.'.format(F=ofh)) + np.savetxt(ofh, ldscore_reg.part_delete_values) + + +def _merge_and_log(ld, sumstats, noun, log): + '''Wrap smart merge with log messages about # of SNPs.''' + sumstats = smart_merge(ld, sumstats) + msg = 'After merging with {F}, {N} SNPs remain.' + if len(sumstats) == 0: + raise ValueError(msg.format(N=len(sumstats), F=noun)) + else: + log.log(msg.format(N=len(sumstats), F=noun)) + + return sumstats + + +def _read_ld_sumstats(args, log, fh, alleles=False, dropna=True): + sumstats = _read_sumstats(args, log, fh, alleles=alleles, dropna=dropna) + ref_ld = _read_ref_ld(args, log) + n_annot = len(ref_ld.columns) - 1 + M_annot = _read_M(args, log, n_annot) + M_annot, ref_ld, novar_cols = _check_variance(log, M_annot, ref_ld) + w_ld = _read_w_ld(args, log) + sumstats = _merge_and_log(ref_ld, sumstats, 'reference panel LD', log) + sumstats = _merge_and_log(sumstats, w_ld, 'regression SNP LD', log) + w_ld_cname = sumstats.columns[-1] + ref_ld_cnames = ref_ld.columns[1:len(ref_ld.columns)] + return M_annot, w_ld_cname, ref_ld_cnames, sumstats, novar_cols + +def cell_type_specific(args, log): + '''Cell type specific analysis''' + args = copy.deepcopy(args) + if args.intercept_h2 is not None: + args.intercept_h2 = float(args.intercept_h2) + if args.no_intercept: + args.intercept_h2 = 1 + + M_annot_all_regr, w_ld_cname, ref_ld_cnames_all_regr, sumstats, novar_cols = \ + _read_ld_sumstats(args, log, args.h2_cts) + M_tot = np.sum(M_annot_all_regr) + _check_ld_condnum(args, log, ref_ld_cnames_all_regr) + _warn_length(log, sumstats) + n_snp = len(sumstats) + n_blocks = min(n_snp, args.n_blocks) + if args.chisq_max is None: + chisq_max = max(0.001*sumstats.N.max(), 80) + else: + chisq_max = args.chisq_max + + ii = np.ravel(sumstats.Z**2 < chisq_max) + sumstats = sumstats.ix[ii, :] + log.log('Removed {M} SNPs with chi^2 > {C} ({N} SNPs remain)'.format( + C=chisq_max, N=np.sum(ii), M=n_snp-np.sum(ii))) + n_snp = np.sum(ii) # lambdas are late-binding, so this works + ref_ld_all_regr = np.array(sumstats[ref_ld_cnames_all_regr]).reshape((len(sumstats),-1)) + chisq = np.array(sumstats.Z**2) + keep_snps = sumstats[['SNP']] + + s = lambda x: np.array(x).reshape((n_snp, 1)) + results_columns = ['Name', 'Coefficient', 'Coefficient_std_error', 'Coefficient_P_value'] + results_data = [] + for (name, ct_ld_chr) in [x.split() for x in open(args.ref_ld_chr_cts).readlines()]: + ref_ld_cts_allsnps = _read_chr_split_files(ct_ld_chr, None, log, + 'cts reference panel LD Score', ps.ldscore_fromlist) + log.log('Performing regression.') + ref_ld_cts = np.array(pd.merge(keep_snps, ref_ld_cts_allsnps, on='SNP', how='left').ix[:,1:]) + if np.any(np.isnan(ref_ld_cts)): + raise ValueError ('Missing some LD scores from cts files. Are you sure all SNPs in ref-ld-chr are also in ref-ld-chr-cts') + + ref_ld = np.hstack([ref_ld_cts, ref_ld_all_regr]) + M_cts = ps.M_fromlist( + _splitp(ct_ld_chr), _N_CHR, common=(not args.not_M_5_50)) + M_annot = np.hstack([M_cts, M_annot_all_regr]) + hsqhat = reg.Hsq(s(chisq), ref_ld, s(sumstats[w_ld_cname]), s(sumstats.N), + M_annot, n_blocks=n_blocks, intercept=args.intercept_h2, + twostep=None, old_weights=True) + coef, coef_se = hsqhat.coef[0], hsqhat.coef_se[0] + results_data.append((name, coef, coef_se, stats.norm.sf(coef/coef_se))) + if args.print_all_cts: + for i in range(1, len(ct_ld_chr.split(','))): + coef, coef_se = hsqhat.coef[i], hsqhat.coef_se[i] + results_data.append((name+'_'+str(i), coef, coef_se, stats.norm.sf(coef/coef_se))) + + + df_results = pd.DataFrame(data = results_data, columns = results_columns) + df_results.sort_values(by = 'Coefficient_P_value', inplace=True) + df_results.to_csv(args.out+'.cell_type_results.txt', sep='\t', index=False) + log.log('Results printed to '+args.out+'.cell_type_results.txt') + + +def estimate_h2(args, log): + '''Estimate h2 and partitioned h2.''' + args = copy.deepcopy(args) + if args.samp_prev is not None and args.pop_prev is not None: + args.samp_prev, args.pop_prev = map( + float, [args.samp_prev, args.pop_prev]) + if args.intercept_h2 is not None: + args.intercept_h2 = float(args.intercept_h2) + if args.no_intercept: + args.intercept_h2 = 1 + M_annot, w_ld_cname, ref_ld_cnames, sumstats, novar_cols = _read_ld_sumstats( + args, log, args.h2) + ref_ld = np.array(sumstats[ref_ld_cnames]) + _check_ld_condnum(args, log, ref_ld_cnames) + _warn_length(log, sumstats) + n_snp = len(sumstats) + n_blocks = min(n_snp, args.n_blocks) + n_annot = len(ref_ld_cnames) + chisq_max = args.chisq_max + old_weights = False + if n_annot == 1: + if args.two_step is None and args.intercept_h2 is None: + args.two_step = 30 + else: + old_weights = True + if args.chisq_max is None: + chisq_max = max(0.001*sumstats.N.max(), 80) + + s = lambda x: np.array(x).reshape((n_snp, 1)) + chisq = s(sumstats.Z**2) + if chisq_max is not None: + ii = np.ravel(chisq < chisq_max) + sumstats = sumstats.ix[ii, :] + log.log('Removed {M} SNPs with chi^2 > {C} ({N} SNPs remain)'.format( + C=chisq_max, N=np.sum(ii), M=n_snp-np.sum(ii))) + n_snp = np.sum(ii) # lambdas are late-binding, so this works + ref_ld = np.array(sumstats[ref_ld_cnames]) + chisq = chisq[ii].reshape((n_snp, 1)) + + if args.two_step is not None: + log.log('Using two-step estimator with cutoff at {M}.'.format(M=args.two_step)) + + hsqhat = reg.Hsq(chisq, ref_ld, s(sumstats[w_ld_cname]), s(sumstats.N), + M_annot, n_blocks=n_blocks, intercept=args.intercept_h2, + twostep=args.two_step, old_weights=old_weights) + + if args.print_cov: + _print_cov(hsqhat, args.out + '.cov', log) + if args.print_delete_vals: + _print_delete_values(hsqhat, args.out + '.delete', log) + _print_part_delete_values(hsqhat, args.out + '.part_delete', log) + + log.log(hsqhat.summary(ref_ld_cnames, P=args.samp_prev, K=args.pop_prev, overlap = args.overlap_annot)) + if args.overlap_annot: + overlap_matrix, M_tot = _read_annot(args, log) + + # overlap_matrix = overlap_matrix[np.array(~novar_cols), np.array(~novar_cols)]#np.logical_not + df_results = hsqhat._overlap_output(ref_ld_cnames, overlap_matrix, M_annot, M_tot, args.print_coefficients) + df_results.to_csv(args.out+'.results', sep="\t", index=False) + log.log('Results printed to '+args.out+'.results') + + return hsqhat + + +def estimate_rg(args, log): + '''Estimate rg between trait 1 and a list of other traits.''' + args = copy.deepcopy(args) + rg_paths, rg_files = _parse_rg(args.rg) + n_pheno = len(rg_paths) + f = lambda x: _split_or_none(x, n_pheno) + args.intercept_h2, args.intercept_gencov, args.samp_prev, args.pop_prev = map(f, + (args.intercept_h2, args.intercept_gencov, args.samp_prev, args.pop_prev)) + map(lambda x: _check_arg_len(x, n_pheno), ((args.intercept_h2, '--intercept-h2'), + (args.intercept_gencov, '--intercept-gencov'), + (args.samp_prev, '--samp-prev'), + (args.pop_prev, '--pop-prev'))) + if args.no_intercept: + args.intercept_h2 = [1 for _ in xrange(n_pheno)] + args.intercept_gencov = [0 for _ in xrange(n_pheno)] + p1 = rg_paths[0] + out_prefix = args.out + rg_files[0] + M_annot, w_ld_cname, ref_ld_cnames, sumstats, _ = _read_ld_sumstats(args, log, p1, + alleles=True, dropna=True) + RG = [] + n_annot = M_annot.shape[1] + if n_annot == 1 and args.two_step is None and args.intercept_h2 is None: + args.two_step = 30 + if args.two_step is not None: + log.log('Using two-step estimator with cutoff at {M}.'.format(M=args.two_step)) + + for i, p2 in enumerate(rg_paths[1:n_pheno]): + log.log( + 'Computing rg for phenotype {I}/{N}'.format(I=i + 2, N=len(rg_paths))) + try: + loop = _read_other_sumstats(args, log, p2, sumstats, ref_ld_cnames) + rghat = _rg(loop, args, log, M_annot, ref_ld_cnames, w_ld_cname, i) + RG.append(rghat) + _print_gencor(args, log, rghat, ref_ld_cnames, i, rg_paths, i == 0) + out_prefix_loop = out_prefix + '_' + rg_files[i + 1] + if args.print_cov: + _print_rg_cov(rghat, out_prefix_loop, log) + if args.print_delete_vals: + _print_rg_delete_values(rghat, out_prefix_loop, log) + + except Exception: # keep going if phenotype 50/100 causes an error + msg = 'ERROR computing rg for phenotype {I}/{N}, from file {F}.' + log.log(msg.format(I=i + 2, N=len(rg_paths), F=rg_paths[i + 1])) + ex_type, ex, tb = sys.exc_info() + log.log(traceback.format_exc(ex) + '\n') + if len(RG) <= i: # if exception raised before appending to RG + RG.append(None) + + log.log('\nSummary of Genetic Correlation Results\n' + + _get_rg_table(rg_paths, RG, args)) + return RG + + +def _read_other_sumstats(args, log, p2, sumstats, ref_ld_cnames): + loop = _read_sumstats(args, log, p2, alleles=True, dropna=False) + loop = _merge_sumstats_sumstats(args, sumstats, loop, log) + loop = loop.dropna(how='any') + alleles = loop.A1 + loop.A2 + loop.A1x + loop.A2x + if not args.no_check_alleles: + loop = _select_and_log(loop, _filter_alleles(alleles), log, + '{N} SNPs with valid alleles.') + loop['Z2'] = _align_alleles(loop.Z2, alleles) + + loop = loop.drop(['A1', 'A1x', 'A2', 'A2x'], axis=1) + _check_ld_condnum(args, log, loop[ref_ld_cnames]) + _warn_length(log, loop) + return loop + + +def _get_rg_table(rg_paths, RG, args): + '''Print a table of genetic correlations.''' + t = lambda attr: lambda obj: getattr(obj, attr, 'NA') + x = pd.DataFrame() + x['p1'] = [rg_paths[0] for i in xrange(1, len(rg_paths))] + x['p2'] = rg_paths[1:len(rg_paths)] + x['rg'] = map(t('rg_ratio'), RG) + x['se'] = map(t('rg_se'), RG) + x['z'] = map(t('z'), RG) + x['p'] = map(t('p'), RG) + if args.samp_prev is not None and \ + args.pop_prev is not None and \ + all((i is not None for i in args.samp_prev)) and \ + all((i is not None for it in args.pop_prev)): + + c = map(lambda x, y: reg.h2_obs_to_liab(1, x, y), args.samp_prev[1:], args.pop_prev[1:]) + x['h2_liab'] = map(lambda x, y: x * y, c, map(t('tot'), map(t('hsq2'), RG))) + x['h2_liab_se'] = map(lambda x, y: x * y, c, map(t('tot_se'), map(t('hsq2'), RG))) + else: + x['h2_obs'] = map(t('tot'), map(t('hsq2'), RG)) + x['h2_obs_se'] = map(t('tot_se'), map(t('hsq2'), RG)) + + x['h2_int'] = map(t('intercept'), map(t('hsq2'), RG)) + x['h2_int_se'] = map(t('intercept_se'), map(t('hsq2'), RG)) + x['gcov_int'] = map(t('intercept'), map(t('gencov'), RG)) + x['gcov_int_se'] = map(t('intercept_se'), map(t('gencov'), RG)) + return x.to_string(header=True, index=False) + '\n' + + +def _print_gencor(args, log, rghat, ref_ld_cnames, i, rg_paths, print_hsq1): + l = lambda x: x + ''.join(['-' for i in range(len(x.replace('\n', '')))]) + P = [args.samp_prev[0], args.samp_prev[i + 1]] + K = [args.pop_prev[0], args.pop_prev[i + 1]] + if args.samp_prev is None and args.pop_prev is None: + args.samp_prev = [None, None] + args.pop_prev = [None, None] + if print_hsq1: + log.log(l('\nHeritability of phenotype 1\n')) + log.log(rghat.hsq1.summary(ref_ld_cnames, P=P[0], K=K[0])) + + log.log( + l('\nHeritability of phenotype {I}/{N}\n'.format(I=i + 2, N=len(rg_paths)))) + log.log(rghat.hsq2.summary(ref_ld_cnames, P=P[1], K=K[1])) + log.log(l('\nGenetic Covariance\n')) + log.log(rghat.gencov.summary(ref_ld_cnames, P=P, K=K)) + log.log(l('\nGenetic Correlation\n')) + log.log(rghat.summary() + '\n') + + +def _merge_sumstats_sumstats(args, sumstats1, sumstats2, log): + '''Merge two sets of summary statistics.''' + sumstats1.rename(columns={'N': 'N1', 'Z': 'Z1'}, inplace=True) + sumstats2.rename( + columns={'A1': 'A1x', 'A2': 'A2x', 'N': 'N2', 'Z': 'Z2'}, inplace=True) + x = _merge_and_log(sumstats1, sumstats2, 'summary statistics', log) + return x + + +def _filter_alleles(alleles): + '''Remove bad variants (mismatched alleles, non-SNPs, strand ambiguous).''' + ii = alleles.apply(lambda y: y in MATCH_ALLELES) + return ii + + +def _align_alleles(z, alleles): + '''Align Z1 and Z2 to same choice of ref allele (allowing for strand flip).''' + try: + z *= (-1) ** alleles.apply(lambda y: FLIP_ALLELES[y]) + except KeyError as e: + msg = 'Incompatible alleles in .sumstats files: %s. ' % e.args + msg += 'Did you forget to use --merge-alleles with munge_sumstats.py?' + raise KeyError(msg) + return z + + +def _rg(sumstats, args, log, M_annot, ref_ld_cnames, w_ld_cname, i): + '''Run the regressions.''' + n_snp = len(sumstats) + s = lambda x: np.array(x).reshape((n_snp, 1)) + if args.chisq_max is not None: + ii = sumstats.Z1**2*sumstats.Z2**2 < args.chisq_max**2 + n_snp = np.sum(ii) # lambdas are late binding, so this works + sumstats = sumstats[ii] + n_blocks = min(args.n_blocks, n_snp) + ref_ld = sumstats.as_matrix(columns=ref_ld_cnames) + intercepts = [args.intercept_h2[0], args.intercept_h2[ + i + 1], args.intercept_gencov[i + 1]] + rghat = reg.RG(s(sumstats.Z1), s(sumstats.Z2), + ref_ld, s(sumstats[w_ld_cname]), s( + sumstats.N1), s(sumstats.N2), M_annot, + intercept_hsq1=intercepts[0], intercept_hsq2=intercepts[1], + intercept_gencov=intercepts[2], n_blocks=n_blocks, twostep=args.two_step) + + return rghat + + +def _parse_rg(rg): + '''Parse args.rg.''' + rg_paths = _splitp(rg) + rg_files = [x.split('/')[-1] for x in rg_paths] + if len(rg_paths) < 2: + raise ValueError( + 'Must specify at least two phenotypes for rg estimation.') + + return rg_paths, rg_files + + +def _print_rg_delete_values(rg, fh, log): + '''Print block jackknife delete values.''' + _print_delete_values(rg.hsq1, fh + '.hsq1.delete', log) + _print_delete_values(rg.hsq2, fh + '.hsq2.delete', log) + _print_delete_values(rg.gencov, fh + '.gencov.delete', log) + + +def _print_rg_cov(rghat, fh, log): + '''Print covariance matrix of estimates.''' + _print_cov(rghat.hsq1, fh + '.hsq1.cov', log) + _print_cov(rghat.hsq2, fh + '.hsq2.cov', log) + _print_cov(rghat.gencov, fh + '.gencov.cov', log) + + +def _split_or_none(x, n): + if x is not None: + y = map(float, x.replace('N', '-').split(',')) + else: + y = [None for _ in xrange(n)] + return y + + +def _check_arg_len(x, n): + x, m = x + if len(x) != n: + raise ValueError( + '{M} must have the same number of arguments as --rg/--h2.'.format(M=m)) diff --git a/SCRIPTS/ldsc/ldscore/sumstats.pyc b/SCRIPTS/ldsc/ldscore/sumstats.pyc new file mode 100644 index 0000000..8ac5505 Binary files /dev/null and b/SCRIPTS/ldsc/ldscore/sumstats.pyc differ diff --git a/SCRIPTS/ldsc/make_annot.py b/SCRIPTS/ldsc/make_annot.py new file mode 100644 index 0000000..cf337b4 --- /dev/null +++ b/SCRIPTS/ldsc/make_annot.py @@ -0,0 +1,56 @@ +#!/usr/bin/env python +from __future__ import print_function +import pandas as pd +import numpy as np +import argparse +from pybedtools import BedTool +import gzip + +def gene_set_to_bed(args): + print('making gene set bed file') + GeneSet = pd.read_csv(args.gene_set_file, header = None, names = ['GENE']) + all_genes = pd.read_csv(args.gene_coord_file, delim_whitespace = True) + df = pd.merge(GeneSet, all_genes, on = 'GENE', how = 'inner') + df['START'] = np.maximum(1, df['START'] - args.windowsize) + df['END'] = df['END'] + args.windowsize + iter_df = [['chr'+(str(x1).lstrip('chr')), x2 - 1, x3] for (x1,x2,x3) in np.array(df[['CHR', 'START', 'END']])] + return BedTool(iter_df).sort().merge() + +def make_annot_files(args, bed_for_annot): + print('making annot file') + df_bim = pd.read_csv(args.bimfile, + delim_whitespace=True, usecols = [0,1,2,3], names = ['CHR','SNP','CM','BP']) + iter_bim = [['chr'+str(x1), x2 - 1, x2] for (x1, x2) in np.array(df_bim[['CHR', 'BP']])] + bimbed = BedTool(iter_bim) + annotbed = bimbed.intersect(bed_for_annot) + bp = [x.start + 1 for x in annotbed] + df_int = pd.DataFrame({'BP': bp, 'ANNOT':1}) + df_annot = pd.merge(df_bim, df_int, how='left', on='BP') + df_annot.fillna(0, inplace=True) + df_annot = df_annot[['ANNOT']].astype(int) + if args.annot_file.endswith('.gz'): + with gzip.open(args.annot_file, 'wb') as f: + df_annot.to_csv(f, sep = "\t", index = False) + else: + df_annot.to_csv(args.annot_file, sep="\t", index=False) + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--gene-set-file', type=str, help='a file of gene names, one line per gene.') + parser.add_argument('--gene-coord-file', type=str, default='ENSG_coord.txt', help='a file with columns GENE, CHR, START, and END, where START and END are base pair coordinates of TSS and TES. This file can contain more genes than are in the gene set. We provide ENSG_coord.txt as a default.') + parser.add_argument('--windowsize', type=int, help='how many base pairs to add around the transcribed region to make the annotation?') + parser.add_argument('--bed-file', type=str, help='the UCSC bed file with the regions that make up your annotation') + parser.add_argument('--nomerge', action='store_true', default=False, help='don\'t merge the bed file; make an annot file wi th values proportional to the number of intervals in the bedfile overlapping the SNP.') + parser.add_argument('--bimfile', type=str, help='plink bim file for the dataset you will use to compute LD scores.') + parser.add_argument('--annot-file', type=str, help='the name of the annot file to output.') + + args = parser.parse_args() + + if args.gene_set_file is not None: + bed_for_annot = gene_set_to_bed(args) + else: + bed_for_annot = BedTool(args.bed_file).sort() + if not args.nomerge: + bed_for_annot = bed_for_annot.merge() + + make_annot_files(args, bed_for_annot) diff --git a/SCRIPTS/ldsc/munge_sumstats.py b/SCRIPTS/ldsc/munge_sumstats.py new file mode 100644 index 0000000..b84e98c --- /dev/null +++ b/SCRIPTS/ldsc/munge_sumstats.py @@ -0,0 +1,745 @@ +#!/usr/bin/env python +from __future__ import division +import pandas as pd +import numpy as np +import os +import sys +import traceback +import gzip +import bz2 +import argparse +from scipy.stats import chi2 +from ldscore import sumstats +from ldsc import MASTHEAD, Logger, sec_to_str +import time +np.seterr(invalid='ignore') + +try: + x = pd.DataFrame({'A': [1, 2, 3]}) + x.sort_values(by='A') +except AttributeError: + raise ImportError('LDSC requires pandas version >= 0.17.0') + +null_values = { + + 'LOG_ODDS': 0, + 'BETA': 0, + 'OR': 1, + 'Z': 0 +} + +default_cnames = { + + # RS NUMBER + 'SNP': 'SNP', + 'MARKERNAME': 'SNP', + 'SNPID': 'SNP', + 'RS': 'SNP', + 'RSID': 'SNP', + 'RS_NUMBER': 'SNP', + 'RS_NUMBERS': 'SNP', + # NUMBER OF STUDIES + 'NSTUDY': 'NSTUDY', + 'N_STUDY': 'NSTUDY', + 'NSTUDIES': 'NSTUDY', + 'N_STUDIES': 'NSTUDY', + # P-VALUE + 'P': 'P', + 'PVALUE': 'P', + 'P_VALUE': 'P', + 'PVAL': 'P', + 'P_VAL': 'P', + 'GC_PVALUE': 'P', + # ALLELE 1 + 'A1': 'A1', + 'ALLELE1': 'A1', + 'ALLELE_1': 'A1', + 'EFFECT_ALLELE': 'A1', + 'REFERENCE_ALLELE': 'A1', + 'INC_ALLELE': 'A1', + 'EA': 'A1', + # ALLELE 2 + 'A2': 'A2', + 'ALLELE2': 'A2', + 'ALLELE_2': 'A2', + 'OTHER_ALLELE': 'A2', + 'NON_EFFECT_ALLELE': 'A2', + 'DEC_ALLELE': 'A2', + 'NEA': 'A2', + # N + 'N': 'N', + 'NCASE': 'N_CAS', + 'CASES_N': 'N_CAS', + 'N_CASE': 'N_CAS', + 'N_CASES': 'N_CAS', + 'N_CONTROLS': 'N_CON', + 'N_CAS': 'N_CAS', + 'N_CON': 'N_CON', + 'N_CASE': 'N_CAS', + 'NCONTROL': 'N_CON', + 'CONTROLS_N': 'N_CON', + 'N_CONTROL': 'N_CON', + 'WEIGHT': 'N', # metal does this. possibly risky. + # SIGNED STATISTICS + 'ZSCORE': 'Z', + 'Z-SCORE': 'Z', + 'GC_ZSCORE': 'Z', + 'Z': 'Z', + 'OR': 'OR', + 'B': 'BETA', + 'BETA': 'BETA', + 'LOG_ODDS': 'LOG_ODDS', + 'EFFECTS': 'BETA', + 'EFFECT': 'BETA', + 'SIGNED_SUMSTAT': 'SIGNED_SUMSTAT', + # INFO + 'INFO': 'INFO', + # MAF + 'EAF': 'FRQ', + 'FRQ': 'FRQ', + 'MAF': 'FRQ', + 'FRQ_U': 'FRQ', + 'F_U': 'FRQ', +} + +describe_cname = { + 'SNP': 'Variant ID (e.g., rs number)', + 'P': 'p-Value', + 'A1': 'Allele 1, interpreted as ref allele for signed sumstat.', + 'A2': 'Allele 2, interpreted as non-ref allele for signed sumstat.', + 'N': 'Sample size', + 'N_CAS': 'Number of cases', + 'N_CON': 'Number of controls', + 'Z': 'Z-score (0 --> no effect; above 0 --> A1 is trait/risk increasing)', + 'OR': 'Odds ratio (1 --> no effect; above 1 --> A1 is risk increasing)', + 'BETA': '[linear/logistic] regression coefficient (0 --> no effect; above 0 --> A1 is trait/risk increasing)', + 'LOG_ODDS': 'Log odds ratio (0 --> no effect; above 0 --> A1 is risk increasing)', + 'INFO': 'INFO score (imputation quality; higher --> better imputation)', + 'FRQ': 'Allele frequency', + 'SIGNED_SUMSTAT': 'Directional summary statistic as specified by --signed-sumstats.', + 'NSTUDY': 'Number of studies in which the SNP was genotyped.' +} + +numeric_cols = ['P', 'N', 'N_CAS', 'N_CON', 'Z', 'OR', 'BETA', 'LOG_ODDS', 'INFO', 'FRQ', 'SIGNED_SUMSTAT', 'NSTUDY'] + +def read_header(fh): + '''Read the first line of a file and returns a list with the column names.''' + (openfunc, compression) = get_compression(fh) + return [x.rstrip('\n') for x in openfunc(fh).readline().split()] + + +def get_cname_map(flag, default, ignore): + ''' + Figure out which column names to use. + + Priority is + (1) ignore everything in ignore + (2) use everything in flags that is not in ignore + (3) use everything in default that is not in ignore or in flags + + The keys of flag are cleaned. The entries of ignore are not cleaned. The keys of defualt + are cleaned. But all equality is modulo clean_header(). + + ''' + clean_ignore = [clean_header(x) for x in ignore] + cname_map = {x: flag[x] for x in flag if x not in clean_ignore} + cname_map.update( + {x: default[x] for x in default if x not in clean_ignore + flag.keys()}) + return cname_map + + +def get_compression(fh): + ''' + Read filename suffixes and figure out whether it is gzipped,bzip2'ed or not compressed + ''' + if fh.endswith('gz'): + compression = 'gzip' + openfunc = gzip.open + elif fh.endswith('bz2'): + compression = 'bz2' + openfunc = bz2.BZ2File + else: + openfunc = open + compression = None + + return openfunc, compression + + +def clean_header(header): + ''' + For cleaning file headers. + - convert to uppercase + - replace dashes '-' with underscores '_' + - replace dots '.' (as in R) with underscores '_' + - remove newlines ('\n') + ''' + return header.upper().replace('-', '_').replace('.', '_').replace('\n', '') + + +def filter_pvals(P, log, args): + '''Remove out-of-bounds P-values''' + ii = (P > 0) & (P <= 1) + bad_p = (~ii).sum() + if bad_p > 0: + msg = 'WARNING: {N} SNPs had P outside of (0,1]. The P column may be mislabeled.' + log.log(msg.format(N=bad_p)) + + return ii + + +def filter_info(info, log, args): + '''Remove INFO < args.info_min (default 0.9) and complain about out-of-bounds INFO.''' + if type(info) is pd.Series: # one INFO column + jj = ((info > 2.0) | (info < 0)) & info.notnull() + ii = info >= args.info_min + elif type(info) is pd.DataFrame: # several INFO columns + jj = (((info > 2.0) & info.notnull()).any(axis=1) | ( + (info < 0) & info.notnull()).any(axis=1)) + ii = (info.sum(axis=1) >= args.info_min * (len(info.columns))) + else: + raise ValueError('Expected pd.DataFrame or pd.Series.') + + bad_info = jj.sum() + if bad_info > 0: + msg = 'WARNING: {N} SNPs had INFO outside of [0,1.5]. The INFO column may be mislabeled.' + log.log(msg.format(N=bad_info)) + + return ii + + +def filter_frq(frq, log, args): + ''' + Filter on MAF. Remove MAF < args.maf_min and out-of-bounds MAF. + ''' + jj = (frq < 0) | (frq > 1) + bad_frq = jj.sum() + if bad_frq > 0: + msg = 'WARNING: {N} SNPs had FRQ outside of [0,1]. The FRQ column may be mislabeled.' + log.log(msg.format(N=bad_frq)) + + frq = np.minimum(frq, 1 - frq) + ii = frq > args.maf_min + return ii & ~jj + + +def filter_alleles(a): + '''Remove alleles that do not describe strand-unambiguous SNPs''' + return a.isin(sumstats.VALID_SNPS) + + +def parse_dat(dat_gen, convert_colname, merge_alleles, log, args): + '''Parse and filter a sumstats file chunk-wise''' + tot_snps = 0 + dat_list = [] + msg = 'Reading sumstats from {F} into memory {N} SNPs at a time.' + log.log(msg.format(F=args.sumstats, N=int(args.chunksize))) + drops = {'NA': 0, 'P': 0, 'INFO': 0, + 'FRQ': 0, 'A': 0, 'SNP': 0, 'MERGE': 0} + for block_num, dat in enumerate(dat_gen): + sys.stdout.write('.') + tot_snps += len(dat) + old = len(dat) + dat = dat.dropna(axis=0, how="any", subset=filter( + lambda x: x != 'INFO', dat.columns)).reset_index(drop=True) + drops['NA'] += old - len(dat) + dat.columns = map(lambda x: convert_colname[x], dat.columns) + + wrong_types = [c for c in dat.columns if c in numeric_cols and not np.issubdtype(dat[c].dtype, np.number)] + if len(wrong_types) > 0: + raise ValueError('Columns {} are expected to be numeric'.format(wrong_types)) + + ii = np.array([True for i in xrange(len(dat))]) + if args.merge_alleles: + old = ii.sum() + ii = dat.SNP.isin(merge_alleles.SNP) + drops['MERGE'] += old - ii.sum() + if ii.sum() == 0: + continue + + dat = dat[ii].reset_index(drop=True) + ii = np.array([True for i in xrange(len(dat))]) + + if 'INFO' in dat.columns: + old = ii.sum() + ii &= filter_info(dat['INFO'], log, args) + new = ii.sum() + drops['INFO'] += old - new + old = new + + if 'FRQ' in dat.columns: + old = ii.sum() + ii &= filter_frq(dat['FRQ'], log, args) + new = ii.sum() + drops['FRQ'] += old - new + old = new + + old = ii.sum() + if args.keep_maf: + dat.drop( + [x for x in ['INFO'] if x in dat.columns], inplace=True, axis=1) + else: + dat.drop( + [x for x in ['INFO', 'FRQ'] if x in dat.columns], inplace=True, axis=1) + ii &= filter_pvals(dat.P, log, args) + new = ii.sum() + drops['P'] += old - new + old = new + if not args.no_alleles: + dat.A1 = dat.A1.str.upper() + dat.A2 = dat.A2.str.upper() + ii &= filter_alleles(dat.A1 + dat.A2) + new = ii.sum() + drops['A'] += old - new + old = new + + if ii.sum() == 0: + continue + + dat_list.append(dat[ii].reset_index(drop=True)) + + sys.stdout.write(' done\n') + dat = pd.concat(dat_list, axis=0).reset_index(drop=True) + msg = 'Read {N} SNPs from --sumstats file.\n'.format(N=tot_snps) + if args.merge_alleles: + msg += 'Removed {N} SNPs not in --merge-alleles.\n'.format( + N=drops['MERGE']) + + msg += 'Removed {N} SNPs with missing values.\n'.format(N=drops['NA']) + msg += 'Removed {N} SNPs with INFO <= {I}.\n'.format( + N=drops['INFO'], I=args.info_min) + msg += 'Removed {N} SNPs with MAF <= {M}.\n'.format( + N=drops['FRQ'], M=args.maf_min) + msg += 'Removed {N} SNPs with out-of-bounds p-values.\n'.format( + N=drops['P']) + msg += 'Removed {N} variants that were not SNPs or were strand-ambiguous.\n'.format( + N=drops['A']) + msg += '{N} SNPs remain.'.format(N=len(dat)) + log.log(msg) + return dat + + +def process_n(dat, args, log): + '''Determine sample size from --N* flags or N* columns. Filter out low N SNPs.s''' + if all(i in dat.columns for i in ['N_CAS', 'N_CON']): + N = dat.N_CAS + dat.N_CON + P = dat.N_CAS / N + dat['N'] = N * P / P[N == N.max()].mean() + dat.drop(['N_CAS', 'N_CON'], inplace=True, axis=1) + # NB no filtering on N done here -- that is done in the next code block + + if 'N' in dat.columns: + n_min = args.n_min if args.n_min else dat.N.quantile(0.9) / 1.5 + old = len(dat) + dat = dat[dat.N >= n_min].reset_index(drop=True) + new = len(dat) + log.log('Removed {M} SNPs with N < {MIN} ({N} SNPs remain).'.format( + M=old - new, N=new, MIN=n_min)) + + elif 'NSTUDY' in dat.columns and 'N' not in dat.columns: + nstudy_min = args.nstudy_min if args.nstudy_min else dat.NSTUDY.max() + old = len(dat) + dat = dat[dat.NSTUDY >= nstudy_min].drop( + ['NSTUDY'], axis=1).reset_index(drop=True) + new = len(dat) + log.log('Removed {M} SNPs with NSTUDY < {MIN} ({N} SNPs remain).'.format( + M=old - new, N=new, MIN=nstudy_min)) + + if 'N' not in dat.columns: + if args.N: + dat['N'] = args.N + log.log('Using N = {N}'.format(N=args.N)) + elif args.N_cas and args.N_con: + dat['N'] = args.N_cas + args.N_con + if args.daner is None: + msg = 'Using N_cas = {N1}; N_con = {N2}' + log.log(msg.format(N1=args.N_cas, N2=args.N_con)) + else: + raise ValueError('Cannot determine N. This message indicates a bug.\n' + 'N should have been checked earlier in the program.') + + return dat + + +def p_to_z(P, N): + '''Convert P-value and N to standardized beta.''' + return np.sqrt(chi2.isf(P, 1)) + + +def check_median(x, expected_median, tolerance, name): + '''Check that median(x) is within tolerance of expected_median.''' + m = np.median(x) + if np.abs(m - expected_median) > tolerance: + msg = 'WARNING: median value of {F} is {V} (should be close to {M}). This column may be mislabeled.' + raise ValueError(msg.format(F=name, M=expected_median, V=round(m, 2))) + else: + msg = 'Median value of {F} was {C}, which seems sensible.'.format( + C=m, F=name) + + return msg + + +def parse_flag_cnames(log, args): + ''' + Parse flags that specify how to interpret nonstandard column names. + + flag_cnames is a dict that maps (cleaned) arguments to internal column names + ''' + cname_options = [ + [args.nstudy, 'NSTUDY', '--nstudy'], + [args.snp, 'SNP', '--snp'], + [args.N_col, 'N', '--N'], + [args.N_cas_col, 'N_CAS', '--N-cas-col'], + [args.N_con_col, 'N_CON', '--N-con-col'], + [args.a1, 'A1', '--a1'], + [args.a2, 'A2', '--a2'], + [args.p, 'P', '--P'], + [args.frq, 'FRQ', '--nstudy'], + [args.info, 'INFO', '--info'] + ] + flag_cnames = {clean_header(x[0]): x[1] + for x in cname_options if x[0] is not None} + if args.info_list: + try: + flag_cnames.update( + {clean_header(x): 'INFO' for x in args.info_list.split(',')}) + except ValueError: + log.log( + 'The argument to --info-list should be a comma-separated list of column names.') + raise + + null_value = None + if args.signed_sumstats: + try: + cname, null_value = args.signed_sumstats.split(',') + null_value = float(null_value) + flag_cnames[clean_header(cname)] = 'SIGNED_SUMSTAT' + except ValueError: + log.log( + 'The argument to --signed-sumstats should be column header comma number.') + raise + + return [flag_cnames, null_value] + + +def allele_merge(dat, alleles, log): + ''' + WARNING: dat now contains a bunch of NA's~ + Note: dat now has the same SNPs in the same order as --merge alleles. + ''' + dat = pd.merge( + alleles, dat, how='left', on='SNP', sort=False).reset_index(drop=True) + ii = dat.A1.notnull() + a1234 = dat.A1[ii] + dat.A2[ii] + dat.MA[ii] + match = a1234.apply(lambda y: y in sumstats.MATCH_ALLELES) + jj = pd.Series(np.zeros(len(dat), dtype=bool)) + jj[ii] = match + old = ii.sum() + n_mismatch = (~match).sum() + if n_mismatch < old: + log.log('Removed {M} SNPs whose alleles did not match --merge-alleles ({N} SNPs remain).'.format(M=n_mismatch, + N=old - n_mismatch)) + else: + raise ValueError( + 'All SNPs have alleles that do not match --merge-alleles.') + + dat.loc[~jj.astype('bool'), [i for i in dat.columns if i != 'SNP']] = float('nan') + dat.drop(['MA'], axis=1, inplace=True) + return dat + +parser = argparse.ArgumentParser() +parser.add_argument('--sumstats', default=None, type=str, + help="Input filename.") +parser.add_argument('--N', default=None, type=float, + help="Sample size If this option is not set, will try to infer the sample " + "size from the input file. If the input file contains a sample size " + "column, and this flag is set, the argument to this flag has priority.") +parser.add_argument('--N-cas', default=None, type=float, + help="Number of cases. If this option is not set, will try to infer the number " + "of cases from the input file. If the input file contains a number of cases " + "column, and this flag is set, the argument to this flag has priority.") +parser.add_argument('--N-con', default=None, type=float, + help="Number of controls. If this option is not set, will try to infer the number " + "of controls from the input file. If the input file contains a number of controls " + "column, and this flag is set, the argument to this flag has priority.") +parser.add_argument('--out', default=None, type=str, + help="Output filename prefix.") +parser.add_argument('--info-min', default=0.9, type=float, + help="Minimum INFO score.") +parser.add_argument('--maf-min', default=0.01, type=float, + help="Minimum MAF.") +parser.add_argument('--daner', default=False, action='store_true', + help="Use this flag to parse Stephan Ripke's daner* file format.") +parser.add_argument('--daner-n', default=False, action='store_true', + help="Use this flag to parse more recent daner* formatted files, which " + "include sample size column 'Nca' and 'Nco'.") +parser.add_argument('--no-alleles', default=False, action="store_true", + help="Don't require alleles. Useful if only unsigned summary statistics are available " + "and the goal is h2 / partitioned h2 estimation rather than rg estimation.") +parser.add_argument('--merge-alleles', default=None, type=str, + help="Same as --merge, except the file should have three columns: SNP, A1, A2, " + "and all alleles will be matched to the --merge-alleles file alleles.") +parser.add_argument('--n-min', default=None, type=float, + help='Minimum N (sample size). Default is (90th percentile N) / 2.') +parser.add_argument('--chunksize', default=5e6, type=int, + help='Chunksize.') + +# optional args to specify column names +parser.add_argument('--snp', default=None, type=str, + help='Name of SNP column (if not a name that ldsc understands). NB: case insensitive.') +parser.add_argument('--N-col', default=None, type=str, + help='Name of N column (if not a name that ldsc understands). NB: case insensitive.') +parser.add_argument('--N-cas-col', default=None, type=str, + help='Name of N column (if not a name that ldsc understands). NB: case insensitive.') +parser.add_argument('--N-con-col', default=None, type=str, + help='Name of N column (if not a name that ldsc understands). NB: case insensitive.') +parser.add_argument('--a1', default=None, type=str, + help='Name of A1 column (if not a name that ldsc understands). NB: case insensitive.') +parser.add_argument('--a2', default=None, type=str, + help='Name of A2 column (if not a name that ldsc understands). NB: case insensitive.') +parser.add_argument('--p', default=None, type=str, + help='Name of p-value column (if not a name that ldsc understands). NB: case insensitive.') +parser.add_argument('--frq', default=None, type=str, + help='Name of FRQ or MAF column (if not a name that ldsc understands). NB: case insensitive.') +parser.add_argument('--signed-sumstats', default=None, type=str, + help='Name of signed sumstat column, comma null value (e.g., Z,0 or OR,1). NB: case insensitive.') +parser.add_argument('--info', default=None, type=str, + help='Name of INFO column (if not a name that ldsc understands). NB: case insensitive.') +parser.add_argument('--info-list', default=None, type=str, + help='Comma-separated list of INFO columns. Will filter on the mean. NB: case insensitive.') +parser.add_argument('--nstudy', default=None, type=str, + help='Name of NSTUDY column (if not a name that ldsc understands). NB: case insensitive.') +parser.add_argument('--nstudy-min', default=None, type=float, + help='Minimum # of studies. Default is to remove everything below the max, unless there is an N column,' + ' in which case do nothing.') +parser.add_argument('--ignore', default=None, type=str, + help='Comma-separated list of column names to ignore.') +parser.add_argument('--a1-inc', default=False, action='store_true', + help='A1 is the increasing allele.') +parser.add_argument('--keep-maf', default=False, action='store_true', + help='Keep the MAF column (if one exists).') + + +# set p = False for testing in order to prevent printing +def munge_sumstats(args, p=True): + if args.out is None: + raise ValueError('The --out flag is required.') + + START_TIME = time.time() + log = Logger(args.out + '.log') + try: + if args.sumstats is None: + raise ValueError('The --sumstats flag is required.') + if args.no_alleles and args.merge_alleles: + raise ValueError( + '--no-alleles and --merge-alleles are not compatible.') + if args.daner and args.daner_n: + raise ValueError('--daner and --daner-n are not compatible. Use --daner for sample ' + + 'size from FRQ_A/FRQ_U headers, use --daner-n for values from Nca/Nco columns') + + if p: + defaults = vars(parser.parse_args('')) + opts = vars(args) + non_defaults = [x for x in opts.keys() if opts[x] != defaults[x]] + header = MASTHEAD + header += "Call: \n" + header += './munge_sumstats.py \\\n' + options = ['--'+x.replace('_','-')+' '+str(opts[x])+' \\' for x in non_defaults] + header += '\n'.join(options).replace('True','').replace('False','') + header = header[0:-1]+'\n' + log.log(header) + + file_cnames = read_header(args.sumstats) # note keys not cleaned + flag_cnames, signed_sumstat_null = parse_flag_cnames(log, args) + if args.ignore: + ignore_cnames = [clean_header(x) for x in args.ignore.split(',')] + else: + ignore_cnames = [] + + # remove LOG_ODDS, BETA, Z, OR from the default list + if args.signed_sumstats is not None or args.a1_inc: + mod_default_cnames = {x: default_cnames[ + x] for x in default_cnames if default_cnames[x] not in null_values} + else: + mod_default_cnames = default_cnames + + cname_map = get_cname_map( + flag_cnames, mod_default_cnames, ignore_cnames) + if args.daner: + frq_u = filter(lambda x: x.startswith('FRQ_U_'), file_cnames)[0] + frq_a = filter(lambda x: x.startswith('FRQ_A_'), file_cnames)[0] + N_cas = float(frq_a[6:]) + N_con = float(frq_u[6:]) + log.log( + 'Inferred that N_cas = {N1}, N_con = {N2} from the FRQ_[A/U] columns.'.format(N1=N_cas, N2=N_con)) + args.N_cas = N_cas + args.N_con = N_con + # drop any N, N_cas, N_con or FRQ columns + for c in ['N', 'N_CAS', 'N_CON', 'FRQ']: + for d in [x for x in cname_map if cname_map[x] == 'c']: + del cname_map[d] + + cname_map[frq_u] = 'FRQ' + + if args.daner_n: + frq_u = filter(lambda x: x.startswith('FRQ_U_'), file_cnames)[0] + cname_map[frq_u] = 'FRQ' + try: + dan_cas = clean_header(file_cnames[file_cnames.index('Nca')]) + except ValueError: + raise ValueError('Could not find Nca column expected for daner-n format') + + try: + dan_con = clean_header(file_cnames[file_cnames.index('Nco')]) + except ValueError: + raise ValueError('Could not find Nco column expected for daner-n format') + + cname_map[dan_cas] = 'N_CAS' + cname_map[dan_con] = 'N_CON' + + cname_translation = {x: cname_map[clean_header(x)] for x in file_cnames if + clean_header(x) in cname_map} # note keys not cleaned + cname_description = { + x: describe_cname[cname_translation[x]] for x in cname_translation} + if args.signed_sumstats is None and not args.a1_inc: + sign_cnames = [ + x for x in cname_translation if cname_translation[x] in null_values] + if len(sign_cnames) > 1: + raise ValueError( + 'Too many signed sumstat columns. Specify which to ignore with the --ignore flag.') + if len(sign_cnames) == 0: + raise ValueError( + 'Could not find a signed summary statistic column.') + + sign_cname = sign_cnames[0] + signed_sumstat_null = null_values[cname_translation[sign_cname]] + cname_translation[sign_cname] = 'SIGNED_SUMSTAT' + else: + sign_cname = 'SIGNED_SUMSTATS' + + # check that we have all the columns we need + if not args.a1_inc: + req_cols = ['SNP', 'P', 'SIGNED_SUMSTAT'] + else: + req_cols = ['SNP', 'P'] + + for c in req_cols: + if c not in cname_translation.values(): + raise ValueError('Could not find {C} column.'.format(C=c)) + + # check aren't any duplicated column names in mapping + for field in cname_translation: + numk = file_cnames.count(field) + if numk > 1: + raise ValueError('Found {num} columns named {C}'.format(C=field,num=str(numk))) + + # check multiple different column names don't map to same data field + for head in cname_translation.values(): + numc = cname_translation.values().count(head) + if numc > 1: + raise ValueError('Found {num} different {C} columns'.format(C=head,num=str(numc))) + + if (not args.N) and (not (args.N_cas and args.N_con)) and ('N' not in cname_translation.values()) and\ + (any(x not in cname_translation.values() for x in ['N_CAS', 'N_CON'])): + raise ValueError('Could not determine N.') + if ('N' in cname_translation.values() or all(x in cname_translation.values() for x in ['N_CAS', 'N_CON']))\ + and 'NSTUDY' in cname_translation.values(): + nstudy = [ + x for x in cname_translation if cname_translation[x] == 'NSTUDY'] + for x in nstudy: + del cname_translation[x] + if not args.no_alleles and not all(x in cname_translation.values() for x in ['A1', 'A2']): + raise ValueError('Could not find A1/A2 columns.') + + log.log('Interpreting column names as follows:') + log.log('\n'.join([x + ':\t' + cname_description[x] + for x in cname_description]) + '\n') + + if args.merge_alleles: + log.log( + 'Reading list of SNPs for allele merge from {F}'.format(F=args.merge_alleles)) + (openfunc, compression) = get_compression(args.merge_alleles) + merge_alleles = pd.read_csv(args.merge_alleles, compression=compression, header=0, + delim_whitespace=True, na_values='.') + if any(x not in merge_alleles.columns for x in ["SNP", "A1", "A2"]): + raise ValueError( + '--merge-alleles must have columns SNP, A1, A2.') + + log.log( + 'Read {N} SNPs for allele merge.'.format(N=len(merge_alleles))) + merge_alleles['MA'] = ( + merge_alleles.A1 + merge_alleles.A2).apply(lambda y: y.upper()) + merge_alleles.drop( + [x for x in merge_alleles.columns if x not in ['SNP', 'MA']], axis=1, inplace=True) + else: + merge_alleles = None + + (openfunc, compression) = get_compression(args.sumstats) + + # figure out which columns are going to involve sign information, so we can ensure + # they're read as floats + signed_sumstat_cols = [k for k,v in cname_translation.items() if v=='SIGNED_SUMSTAT'] + dat_gen = pd.read_csv(args.sumstats, delim_whitespace=True, header=0, + compression=compression, usecols=cname_translation.keys(), + na_values=['.', 'NA'], iterator=True, chunksize=args.chunksize, + dtype={c:np.float64 for c in signed_sumstat_cols}) + + dat = parse_dat(dat_gen, cname_translation, merge_alleles, log, args) + if len(dat) == 0: + raise ValueError('After applying filters, no SNPs remain.') + + old = len(dat) + dat = dat.drop_duplicates(subset='SNP').reset_index(drop=True) + new = len(dat) + log.log('Removed {M} SNPs with duplicated rs numbers ({N} SNPs remain).'.format( + M=old - new, N=new)) + # filtering on N cannot be done chunkwise + dat = process_n(dat, args, log) + dat.P = p_to_z(dat.P, dat.N) + dat.rename(columns={'P': 'Z'}, inplace=True) + if not args.a1_inc: + log.log( + check_median(dat.SIGNED_SUMSTAT, signed_sumstat_null, 0.1, sign_cname)) + dat.Z *= (-1) ** (dat.SIGNED_SUMSTAT < signed_sumstat_null) + dat.drop('SIGNED_SUMSTAT', inplace=True, axis=1) + # do this last so we don't have to worry about NA values in the rest of + # the program + if args.merge_alleles: + dat = allele_merge(dat, merge_alleles, log) + + out_fname = args.out + '.sumstats' + print_colnames = [ + c for c in dat.columns if c in ['SNP', 'N', 'Z', 'A1', 'A2']] + if args.keep_maf and 'FRQ' in dat.columns: + print_colnames.append('FRQ') + msg = 'Writing summary statistics for {M} SNPs ({N} with nonmissing beta) to {F}.' + log.log( + msg.format(M=len(dat), F=out_fname + '.gz', N=dat.N.notnull().sum())) + if p: + dat.to_csv(out_fname + '.gz', sep="\t", index=False, + columns=print_colnames, float_format='%.3f', compression = 'gzip') + + log.log('\nMetadata:') + CHISQ = (dat.Z ** 2) + mean_chisq = CHISQ.mean() + log.log('Mean chi^2 = ' + str(round(mean_chisq, 3))) + if mean_chisq < 1.02: + log.log("WARNING: mean chi^2 may be too small.") + + log.log('Lambda GC = ' + str(round(CHISQ.median() / 0.4549, 3))) + log.log('Max chi^2 = ' + str(round(CHISQ.max(), 3))) + log.log('{N} Genome-wide significant SNPs (some may have been removed by filtering).'.format(N=(CHISQ + > 29).sum())) + return dat + + except Exception: + log.log('\nERROR converting summary statistics:\n') + ex_type, ex, tb = sys.exc_info() + log.log(traceback.format_exc(ex)) + raise + finally: + log.log('\nConversion finished at {T}'.format(T=time.ctime())) + log.log('Total time elapsed: {T}'.format( + T=sec_to_str(round(time.time() - START_TIME, 2)))) + +if __name__ == '__main__': + munge_sumstats(parser.parse_args(), p=True) diff --git a/SCRIPTS/ldsc/requirements.txt b/SCRIPTS/ldsc/requirements.txt new file mode 100644 index 0000000..cc076b0 --- /dev/null +++ b/SCRIPTS/ldsc/requirements.txt @@ -0,0 +1,6 @@ +bitarray>=0.8,<0.9 +nose>=1.3,<1.4 +pybedtools>=0.7,<0.8 +scipy>=0.18,<0.19 +numpy>=1.16,<1.17 +pandas>=0.20,<0.21 diff --git a/SCRIPTS/ldsc/setup.py b/SCRIPTS/ldsc/setup.py new file mode 100644 index 0000000..e78ccfb --- /dev/null +++ b/SCRIPTS/ldsc/setup.py @@ -0,0 +1,20 @@ +from setuptools import setup + +setup(name='ldsc', + version='1.0', + description='LD Score Regression (LDSC)', + url='http://github.com/bulik/ldsc', + author='Brendan Bulik-Sullivan and Hilary Finucane', + author_email='', + license='GPLv3', + packages=['ldscore'], + scripts=['ldsc.py', 'munge_sumstats.py'], + install_requires = [ + 'bitarray>=0.8,<0.9', + 'nose>=1.3,<1.4', + 'pybedtools>=0.7,<0.8', + 'scipy>=0.18,<0.19', + 'numpy>=1.16,<1.17', + 'pandas>=0.20,<0.21' + ] +) diff --git a/SCRIPTS/ldsc/w_hm3.snplist b/SCRIPTS/ldsc/w_hm3.snplist new file mode 100644 index 0000000..f65a717 --- /dev/null +++ b/SCRIPTS/ldsc/w_hm3.snplist @@ -0,0 +1,1217312 @@ +SNP A1 A2 +rs3094315 G A +rs3131972 A G +rs3131969 A G +rs1048488 C T +rs3115850 T C +rs2286139 C T +rs12562034 A G +rs4040617 G A +rs2980300 T C +rs2519031 G A +rs4970383 A C +rs4475691 T C +rs1806509 C A +rs7537756 G A +rs13302982 A G +rs4040604 G T +rs28576697 C T +rs1110052 G T +rs7523549 T C +rs3748592 A G +rs3748593 A C +rs2272756 A G +rs2340582 A G +rs4246503 A G +rs3748594 A G +rs17160698 C T +rs3748595 A C +rs3748597 T C +rs13303106 A G +rs28415373 T C +rs13303010 G A +rs6696281 T C +rs28391282 A G +rs28504611 T C +rs6694632 G A +rs13303118 G T +rs2341354 A G +rs6665000 C A +rs2341362 T C +rs9777703 C T +rs9777939 A G +rs1891910 A G +rs35940137 A G +rs3121567 T C +rs3128117 C T +rs2465126 G A +rs2341365 G A +rs15842 T C +rs1891906 C A +rs6657048 T C +rs2710888 T C +rs4970393 G A +rs2710875 C T +rs3128097 G A +rs17160776 T C +rs9803031 C T +rs13303287 T G +rs9778201 A G +rs4275402 C T +rs2465136 C T +rs2488991 G T +rs7526076 A G +rs4075116 C T +rs3934834 T C +rs3766192 C T +rs3766191 T C +rs9442372 A G +rs10907177 G A +rs3737728 A G +rs9442398 A G +rs6689308 G A +rs6687776 T C +rs6678318 A G +rs9651273 A G +rs11260595 A C +rs6671356 C T +rs6604968 G A +rs4970405 G A +rs12726255 G A +rs4970409 A G +rs7548798 T C +rs17160824 A G +rs11807848 C T +rs12757754 G A +rs9442373 C A +rs2298217 T C +rs12145826 A G +rs10907182 T C +rs4970357 C A +rs9442380 T C +rs7553429 C A +rs4970362 A G +rs9442385 T G +rs9660710 A C +rs4970420 A G +rs4442317 C T +rs4970421 A G +rs11260542 G A +rs9442387 C T +rs12092254 A G +rs6668667 A G +rs1320565 T C +rs1320571 A G +rs3813204 A G +rs4297230 T C +rs11260549 A G +rs4314833 C T +rs11260554 T G +rs11260556 A G +rs10907175 C A +rs9729550 C A +rs3819001 C T +rs11466691 C T +rs1815606 T G +rs11466681 T C +rs3753349 T C +rs34945898 A G +rs11721 A C +rs12036216 A G +rs2887286 C T +rs3813199 A G +rs6603781 A G +rs6671424 A G +rs3766186 A C +rs7515488 T C +rs11260562 A G +rs7528416 C T +rs6697886 A G +rs4970364 C T +rs6675798 C T +rs2297864 G A +rs11260566 T C +rs12093154 A G +rs3818646 T C +rs7524470 G A +rs12072310 C A +rs11804831 C T +rs11260570 C T +rs4018608 C T +rs12073590 C A +rs6667923 A C +rs3737722 G A +rs11260577 C T +rs6689813 C T +rs6685064 T C +rs28488099 A C +rs3753340 A G +rs6603788 T C +rs12077700 T C +rs2144440 G A +rs13306651 A G +rs1749951 A G +rs604618 A G +rs1739855 C T +rs2477782 C T +rs11586188 A G +rs11260584 T G +rs12410087 C A +rs1536168 A G +rs3737717 G A +rs12103 T C +rs12142199 G A +rs34054191 A G +rs4970432 G A +rs1810745 A G +rs3845293 A C +rs35424002 A G +rs3855955 A G +rs182532 T C +rs307380 G A +rs34389364 G A +rs34876581 T C +rs2765027 T G +rs2765023 T C +rs2765021 C T +rs17160669 T C +rs2765015 A G +rs2765033 T C +rs2649589 G A +rs2649588 C T +rs2477777 C T +rs1240743 C A +rs35242196 A C +rs2291889 A G +rs1240707 T C +rs1240708 G A +rs12089560 G A +rs1695824 C A +rs873927 G A +rs1240703 A C +rs1312568 G A +rs1240732 G A +rs1153105 C T +rs819976 A C +rs819980 C T +rs6690515 A G +rs6669795 C A +rs9439458 C T +rs9439462 T C +rs12032637 G A +rs6683520 T C +rs1987191 C T +rs3118505 T C +rs1571149 G A +rs1571150 A C +rs7290 C T +rs3766180 C T +rs3766178 C T +rs7533 G A +rs7517401 A G +rs2031709 A C +rs3128342 A C +rs1887284 A G +rs7531530 T C +rs3820075 G A +rs3766170 C T +rs3766169 C A +rs2296716 T C +rs9439468 G A +rs6603791 G A +rs6603793 T C +rs7519837 T C +rs6687029 C A +rs6604983 G A +rs4648786 G A +rs28414855 T G +rs11552172 C T +rs4648611 A G +rs12748433 C T +rs28635343 T C +rs28464684 T C +rs28456011 A G +rs6604981 A G +rs4639697 G T +rs6699975 T G +rs28707307 G A +rs4074196 G A +rs4074197 C T +rs7520934 C A +rs28487995 A G +rs3737622 A G +rs601141 A C +rs2179383 C T +rs909823 A C +rs2294488 T C +rs2294489 T C +rs4648764 G A +rs1014988 G A +rs7407 C T +rs34306661 C T +rs2281173 A G +rs2235536 T C +rs2235537 C T +rs35672141 T C +rs1107910 C T +rs876029 G T +rs7531583 A G +rs34238514 T C +rs12041925 G T +rs12044597 A G +rs10907194 T C +rs7534617 G T +rs742359 A C +rs2272908 C T +rs3737628 C T +rs9660180 G A +rs9970652 T C +rs16825336 A G +rs12742323 T G +rs2180311 T C +rs4648726 C T +rs9786963 T C +rs10907187 A G +rs9786942 A G +rs7553640 T C +rs11589067 A G +rs6603797 T C +rs11260622 A G +rs4648727 A C +rs12126768 G T +rs6692959 T C +rs6681938 C T +rs12402876 A G +rs7526236 A G +rs10907192 A G +rs16824500 A G +rs7511905 A C +rs9660106 A G +rs4648592 A G +rs35511469 T G +rs3855951 C T +rs11260624 T C +rs7525092 T C +rs6603803 A G +rs16824526 C A +rs6603804 T C +rs6603805 A G +rs6688000 A G +rs12093735 T C +rs745910 C T +rs4648732 T C +rs28625223 C T +rs2377037 A C +rs2474460 T C +rs2748988 A G +rs2748987 A G +rs2247560 G A +rs28710181 T C +rs2748986 G T +rs941532 A G +rs2490548 G A +rs28508199 G A +rs2803349 T C +rs1884454 T G +rs2144687 C T +rs2803348 A G +rs2250833 A G +rs28633659 T C +rs752965 T C +rs2748984 C T +rs28637822 T C +rs2803316 A G +rs16824543 A G +rs2748981 G A +rs12758705 A G +rs2803329 A G +rs2262190 A G +rs2803337 G A +rs3820011 A C +rs2803291 T C +rs16824588 C T +rs13303201 A C +rs7528894 T C +rs28645431 T C +rs4648745 C T +rs3795283 T G +rs6661597 C T +rs6605081 T C +rs6656398 G A +rs13303016 G A +rs13303344 C A +rs4481796 T C +rs28603108 G A +rs3121819 A G +rs3128318 C A +rs3128322 G A +rs3128323 G A +rs17715203 A G +rs9442614 A G +rs3121822 C T +rs3128317 A C +rs3795277 C A +rs6692648 C T +rs1878745 G A +rs2678939 A C +rs6663158 A G +rs2254669 G A +rs16824697 A G +rs16824712 G A +rs2678947 C A +rs16824749 C T +rs2803310 C T +rs17776713 A G +rs2459994 T C +rs12755035 A G +rs3128326 C T +rs884080 G A +rs7513222 A G +rs2376802 C T +rs908742 A G +rs10910029 A G +rs10910030 C T +rs10752741 G A +rs10797413 G A +rs12410859 T C +rs1878752 G A +rs4648805 G A +rs4648807 T C +rs4648808 T C +rs11584491 A G +rs6603813 G T +rs3128337 A G +rs3107145 G A +rs3107146 T C +rs3107147 G A +rs3107151 T G +rs3128291 A G +rs3107156 T C +rs3107157 T C +rs3128296 G T +rs425277 T C +rs3753242 T C +rs3128297 T C +rs424079 C A +rs12026349 T G +rs2503706 A G +rs427811 G T +rs377283 C T +rs385039 G A +rs2257684 G A +rs262672 T G +rs3107126 A C +rs262669 A G +rs9442619 T C +rs2257182 C T +rs262663 C T +rs262662 G A +rs2459984 G A +rs3123592 C T +rs262657 A G +rs262656 C A +rs262654 A G +rs262653 G T +rs262651 G A +rs3052 T C +rs262641 G A +rs3128309 A G +rs626479 A G +rs2460001 C T +rs262689 A G +rs262688 G T +rs262687 A G +rs3123595 T C +rs262685 G A +rs2460002 A G +rs2503701 C T +rs6665593 A G +rs7512482 C T +rs262683 T C +rs2460000 A G +rs263532 C T +rs12403265 T C +rs262680 T C +rs263526 C T +rs10910034 T C +rs1713712 T C +rs260513 A G +rs260512 A G +rs16824948 T C +rs12084736 T C +rs10797417 G A +rs7547453 T C +rs11588312 T C +rs7553178 G A +rs4648819 G A +rs12045693 A C +rs10910047 A G +rs12119470 T C +rs2017143 T C +rs2643885 C A +rs903919 T C +rs2132303 T C +rs884941 T C +rs884940 T C +rs12743493 A G +rs10910050 A C +rs2843163 T C +rs2643888 A G +rs12562937 T C +rs903916 T C +rs1809822 C A +rs1496555 A G +rs2279702 A G +rs2843159 T C +rs2643904 A G +rs2173049 T C +rs2843157 T C +rs12082516 C T +rs12080256 A C +rs35513983 G A +rs2645075 T C +rs2643893 C T +rs2645092 C T +rs2645065 A C +rs903904 T C +rs2645066 A G +rs2843143 T C +rs2843142 A G +rs12730391 A G +rs2645072 A C +rs2055204 G A +rs7527871 C A +rs4648831 T C +rs2840525 G A +rs2840528 G A +rs2645082 T C +rs3795272 C T +rs12049067 T C +rs2843129 G A +rs2840530 G A +rs2840531 T C +rs2645085 C T +rs2645087 A G +rs903914 A G +rs2840542 T C +rs2643891 A G +rs7545940 C T +rs2840538 T C +rs6665546 A G +rs10910060 A G +rs10910061 A G +rs12031614 T C +rs2279703 T C +rs7519807 A G +rs12033324 A C +rs2843160 G T +rs903901 A G +rs2643901 T C +rs2843127 A G +rs903903 G A +rs4648633 C T +rs16825102 A G +rs1123571 A G +rs3736330 T C +rs2840532 C T +rs2645081 A G +rs6698680 G A +rs3001344 A G +rs6693447 G T +rs3001337 T C +rs3001336 G A +rs1129333 A G +rs1129332 T C +rs2494427 T C +rs6671420 G A +rs2494428 C T +rs11811122 G T +rs12143412 A C +rs10910070 A G +rs11577147 G A +rs12731309 T C +rs2494591 A G +rs10910071 G A +rs35799953 T G +rs12023660 T C +rs12022929 T G +rs2494437 G T +rs4531246 T G +rs2494641 T C +rs4648843 T C +rs4233033 C T +rs4592207 C T +rs6659405 T G +rs10449222 A G +rs4443836 T G +rs34241773 T C +rs11581548 A G +rs10910078 T C +rs12040359 T C +rs1998760 T C +rs1998759 A G +rs2477696 A G +rs2494626 T C +rs13376356 T C +rs11588930 A G +rs12049628 A G +rs10797428 A G +rs4648639 G A +rs4648640 G A +rs7512269 T C +rs10910082 C T +rs2477702 C T +rs2477703 C T +rs3762444 T C +rs2296442 T C +rs2477707 T C +rs3814298 T C +rs1107685 G A +rs4648559 C T +rs2236397 A C +rs7535528 A G +rs2247308 G A +rs2236395 G A +rs7548829 A G +rs3122920 A G +rs2246732 G A +rs2494636 A G +rs2985862 T C +rs2494625 T C +rs3122923 C A +rs1980789 C A +rs3122922 A G +rs6694195 G A +rs12406506 G T +rs9803764 C T +rs11584295 A G +rs3001339 C T +rs6661956 C T +rs2494606 A G +rs2477716 G A +rs1886730 C T +rs2257763 A C +rs2281852 A C +rs11573979 T C +rs2234167 A G +rs2495365 G A +rs10797432 T C +rs6667605 T C +rs2260965 G A +rs10910093 T C +rs4310388 T G +rs4648648 A G +rs735000 T C +rs734999 T C +rs10910097 T C +rs2296443 A G +rs867436 T C +rs3748816 G A +rs2260976 A G +rs2843401 T C +rs12133956 A C +rs2843402 T C +rs2764845 G T +rs2843403 T C +rs2764841 G A +rs2843404 T C +rs2985855 A C +rs2985857 C T +rs6424092 C A +rs10910099 C A +rs10910105 A G +rs10797437 C T +rs4445406 C T +rs10797440 G A +rs10752748 A G +rs6684865 A G +rs12117343 T C +rs11590198 A G +rs3890745 C T +rs4474198 T C +rs6698817 T C +rs4648662 A G +rs4648499 A C +rs4648390 T C +rs4648356 A C +rs2377041 T C +rs897623 A G +rs4648360 T C +rs10909880 T C +rs10909890 A G +rs12071313 G A +rs4609377 C T +rs4648481 T G +rs4648482 C T +rs6680471 T C +rs4648384 A C +rs12049543 A C +rs1456460 T G +rs12409348 T C +rs6683974 T C +rs1563469 A G +rs897615 T C +rs10910019 C T +rs10797342 C T +rs2045333 T C +rs3100865 G T +rs1456465 A G +rs1456466 C T +rs2842910 C T +rs2606406 A G +rs12035436 T G +rs880725 G A +rs880724 T C +rs897631 C T +rs897632 A G +rs1869970 G A +rs1869971 T C +rs7340016 G A +rs1563468 C T +rs897634 C T +rs4648432 C A +rs9782915 G A +rs2045331 A G +rs2045332 A G +rs2606411 T C +rs12403214 G T +rs4648441 T G +rs1869972 A G +rs1473386 C T +rs10797368 T C +rs16823228 G A +rs2842909 T C +rs2925499 C T +rs2606414 T C +rs2842933 A G +rs2842906 A G +rs1890339 T G +rs10909845 T C +rs11583804 G A +rs878201 A G +rs2485944 T C +rs2485945 C T +rs12752801 T C +rs4072769 A G +rs12044030 C T +rs12046158 C T +rs7533362 A G +rs6681741 T C +rs1572657 C T +rs10797373 G A +rs10909852 C T +rs10909855 T C +rs17391750 A G +rs6424066 C T +rs12022429 A G +rs16823335 A G +rs946177 G A +rs2027262 C T +rs2027264 A G +rs6685625 C T +rs7534897 T C +rs6691208 A C +rs4576609 T C +rs3795262 G T +rs3795263 A G +rs7412983 C A +rs2142569 A G +rs12039548 C T +rs12034573 A G +rs34408665 C T +rs6661908 A G +rs41334845 T G +rs10489589 C T +rs10489588 A G +rs9661525 G T +rs2993510 C T +rs12409277 C T +rs6692292 C T +rs717795 C T +rs2993480 T C +rs1109251 A G +rs1125017 C A +rs2376821 T C +rs6665853 A G +rs2297829 A C +rs2993486 G A +rs4648451 A C +rs1569419 T C +rs760567 A G +rs6695131 T C +rs2981881 C T +rs926246 C T +rs926244 G A +rs7543737 A G +rs2817168 G A +rs2993493 A C +rs1890336 C A +rs4648453 T G +rs2993501 A C +rs12135664 T C +rs2993502 T C +rs6690373 G A +rs876740 T C +rs2817174 C T +rs6698974 T C +rs2817178 C T +rs2235176 C T +rs10797380 G A +rs7538096 C T +rs2817184 G A +rs2651909 A G +rs2817185 G A +rs731031 C T +rs2651902 T C +rs2742673 T G +rs1798246 C A +rs2651899 C T +rs2651935 T C +rs2651936 C T +rs17390062 C T +rs12733843 A G +rs2817134 T C +rs2651929 C T +rs2651927 C A +rs12135062 T G +rs4233024 C T +rs10752733 A G +rs2817138 T G +rs2651916 C T +rs10737190 G A +rs6685614 G A +rs7539775 G A +rs2651912 T C +rs10797383 C T +rs9659245 T C +rs10909895 C T +rs11577655 T C +rs4308918 A G +rs13328662 G A +rs10492941 A C +rs3753758 A G +rs6424068 G A +rs10909901 T C +rs12124147 T C +rs2651906 T C +rs2651898 G A +rs16823529 A G +rs12075867 C T +rs2651895 T G +rs2817148 G A +rs2651891 A G +rs11804075 A G +rs16823542 T C +rs10909903 T C +rs16823551 A G +rs1553288 T C +rs2253344 T C +rs6424069 C T +rs10797384 G A +rs2817158 A G +rs12036784 A G +rs2249336 C T +rs905138 T G +rs2455118 G A 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C +rs10990300 T C +rs1580650 T C +rs7858600 A G +rs7047925 G A +rs10820274 G T +rs12335504 A G +rs7853823 C A +rs7871109 A C +rs16922174 G A +rs7044964 A C +rs10990302 T C +rs4743578 A G +rs1116755 G A +rs2131272 T C +rs1388920 A G +rs12237372 G A +rs10990316 A G +rs1492602 A G +rs4743582 G A +rs994864 T C +rs994865 C T +rs1388921 A G +rs10820282 A G +rs7854657 G A +rs10990317 T G +rs12551330 C T +rs7869941 C T +rs7028100 T C +rs10820283 G A +rs9695822 G A +rs1027261 C T +rs4584195 A G +rs10217639 C T +rs7848024 A G +rs1586884 A G +rs10820288 C T +rs1492580 A G +rs13294991 C A +rs1552766 G A +rs7866606 T G +rs1352981 G A +rs1352980 G A +rs7033449 C T +rs1013136 C T +rs4237207 T G +rs4742859 T C +rs1907787 C A +rs860935 T C +rs10990347 C T +rs10990353 G A +rs16922210 A G +rs1492598 A G +rs10512305 G A +rs4742860 C A +rs1907788 G A +rs1342895 G A +rs9785252 T C +rs843271 A G +rs843270 T G +rs843269 T C +rs859996 C T +rs843266 T C +rs7035848 G A +rs843264 A G +rs843261 G A +rs4743599 G A 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A G +rs9722952 T C +rs7043936 T C +rs7469429 T C +rs11523300 A G +rs6606591 G A +rs6606590 A G +rs9414737 T C +rs6422890 C T +rs11516128 A G +rs6422889 C T +rs28787940 A G +rs6606588 A G +rs13288824 A C +rs11562621 C T +rs28563072 T C +rs11534413 T C +rs13296446 A G +rs9695429 T C +rs11534415 A G +rs34155071 T C +rs28649497 T C +rs13297080 T C +rs9414731 A G +rs6606582 A G +rs9722391 A G +rs28699612 A G +rs9410194 C T +rs3793468 T C +rs4962237 T C +rs872341 T C +rs11137268 T C +rs11137287 T C +rs10867091 A G +rs4962232 C T +rs1891629 T C +rs7037200 T C +rs3750381 T C +rs17065442 C A +rs2480105 C T +rs11137410 T C +rs821304 G T +rs28875616 G A +rs11137428 A G +rs10867123 G A +rs1099298 C T +rs3737320 T C +rs9410023 A G +rs13301653 G T +rs1048370 T C +rs10110 C A +rs1105221 T C +rs12553790 T C +rs11137131 C T +rs821323 C A +rs12683290 C T +rs11137135 A G +rs950078 A G +rs2149561 T C +rs863701 G A +rs1891628 A G +rs2259657 G A +rs2297004 A G +rs928046 T C +rs11137141 T C +rs34223864 A G 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+rs7023346 A G +rs7045459 G T +rs7035895 T C +rs7043700 T C +rs2168526 A G +rs10780196 A G +rs7035939 A G +rs7040050 A G +rs2124655 A G +rs7023369 T C +rs7028989 T C +rs936248 A G +rs7044797 T C +rs12005780 C T +rs7019274 A G +rs7852253 G A +rs7852364 G A +rs7859352 G A +rs7865267 T C +rs3750506 G A +rs7865887 G A +rs6559261 G A +rs1547504 G A +rs7020939 G A +rs7853637 C A +rs2278975 C T +rs10746565 C T +rs7860423 A G +rs6559263 C T +rs11137360 G A +rs1123190 T C +rs7023278 G A +rs10867099 G A +rs10867100 T G +rs10867101 A G +rs6559264 C T +rs1993430 A G +rs10114026 T C +rs7869841 T C +rs7874239 T C +rs11137363 T G +rs11137364 G A +rs10780200 C T +rs936249 T C +rs2290582 A G +rs12000233 T C +rs10867105 C T +rs10123067 T C +rs2168525 G A +rs3812541 G A +rs4876925 T C +rs1531166 A G +rs1531165 A G +rs10867107 C T +rs7357733 T G +rs11137372 A G +rs1378954 G A +rs9314645 G A +rs766373 A G +rs2229948 C T +rs4876928 T C +rs2739257 C A +rs2739258 A G +rs2606356 C T +rs2606357 C T +rs2606358 A G +rs2739260 A G +rs9410161 G A +rs3750507 T C +rs3750508 T C +rs3750509 T C +rs3750510 C T +rs9410163 G A +rs9777369 T C +rs9410073 A G +rs17583562 A G +rs11137379 A G +rs2914521 C T diff --git a/SCRIPTS/liftover.metagwastoolkit.py b/SCRIPTS/liftover.metagwastoolkit.py new file mode 100644 index 0000000..71ed3bf --- /dev/null +++ b/SCRIPTS/liftover.metagwastoolkit.py @@ -0,0 +1,287 @@ +#!/usr/bin/env python3 + +# Change log +# * v1.0.0 2024-04-11: Initial version. +# Version information +VERSION_NAME = 'liftOver' +VERSION = '1.0.0' +VERSION_DATE = '2024-04-19' +COPYRIGHT = 'Copyright 1979-2024. Sander W. van der Laan | s.w.vanderlaan [at] gmail [dot] com | https://vanderlaanand.science.' +COPYRIGHT_TEXT = f'\nThe MIT License (MIT). \n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and \nassociated documentation files (the "Software"), to deal in the Software without restriction, \nincluding without limitation the rights to use, copy, modify, merge, publish, distribute, \nsublicense, and/or sell copies of the Software, and to permit persons to whom the Software is \nfurnished to do so, subject to the following conditions: \n\nThe above copyright notice and this permission notice shall be included in all copies \nor substantial portions of the Software. \n\nTHE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, \nINCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR \nPURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS \nBE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, \nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE \nOR OTHER DEALINGS IN THE SOFTWARE. \n\nReference: http://opensource.org.' + +import sys +from tqdm import tqdm +import os +import argparse +from pyliftover import LiftOver +from argparse import RawTextHelpFormatter +import shutil +import time +from datetime import datetime +from datetime import timedelta +import subprocess + +# Function to convert coordinates +# convert_coordinates(args.input, args.output_dir, args.pos_type, args.build, args.verbose) +def convert_coordinates(input_file, output_dir, pos_type, build, verbose=False): + # Initialize LiftOver object based on build + if build == 'b37_to_b38': + lo = LiftOver('hg19', 'hg38') + elif build == 'b38_to_b37': + lo = LiftOver('hg38', 'hg19') + elif build == 'b17_to_b37': + lo = LiftOver('hg17', 'hg19') + elif build == 'b17_to_b38': + lo = LiftOver('hg17', 'hg38') + elif build == 'b18_to_b37': + lo = LiftOver('hg18', 'hg19') + elif build == 'b18_to_b38': + lo = LiftOver('hg18', 'hg38') + else: + print("Invalid build option. Supported options are 'b37_to_b38', 'b38_to_b37', 'b17_to_b37', 'b17_to_b38', 'b18_to_b37', 'b18_to_b38'.") + return + + # Check if project directory and output directory exist + if not os.path.exists(output_dir): + print(f"Error: Project directory '{output_dir}' does not exist.") + exit(1) + if not os.path.exists(output_dir): + print(f"Output directory '{output_dir}' does not exist. Making it for you.") + os.makedirs(output_dir) + + # Output filename + output_file = os.path.join(output_dir, f'lifover.{build}.converted_coordinates.txt') + failed_log_file = os.path.join(output_dir, f'lifover.{build}.failed_variants.log') + + successful_variants = 0 + failed_variants = 0 + + with open(input_file, 'r') as f_in, open(output_file, 'w') as f_out, open(failed_log_file, 'w') as f_failed_log_file: + f_failed_log_file.write(f"+ {VERSION_NAME} v{VERSION} +\n") + f_failed_log_file.write(f"Listing variants that failed lift over from {build}.\n") + f_failed_log_file.write(f"\nRSID\tChromosome\tPosition\n") + + if verbose: + print(f"- writing converted coordinates to : {output_file}") + print(f"- writing failed variants to: {failed_log_file}") + if pos_type == 0: + print(f"- converting to 0-based; adding strand and conversion chain score to output file.\n") + else: + print(f"- converting to 1-based.\n") + # Write the header to the output file + if pos_type == 0: + f_out.write(f"RSID\tChromosome\tPosition\tStrand\tConversion_Chain_Score\n") + else: + f_out.write(f"RSID\tChromosome\tPosition\n") + + # Skip the header line if it exists + header_line = next(f_in, None) + if header_line: + if header_line.strip().lower().startswith(('snpid', 'rsid', 'variant_id', 'snp', 'variantid', 'dbsnpid')): # Adjust based on actual header + if verbose: + print(f"Header in {input_file}. Skipping.") + # Skip the header line + pass + else: + if verbose: + print(f"No header in {input_file}.") + # Not a header line, process it + parts = header_line.strip().split() + snpid, chromosome, position = parts[0], parts[1], int(parts[2]) + # Convert coordinates + # check lifover output + # https://github.com/konstantint/pyliftover/blob/master/pyliftover/liftover.py#L66 + # Returns a list of possible conversions for a given chromosome position. The list may be empty + # (no conversion), have a single element (unique conversion), or several elements (position + # mapped to several chains). The list contains tuples (target_chromosome, target_position, + # target_strand, conversion_chain_score), where conversion_chain_score is the "alignment score" + # field specified at the chain used to perform conversion. If there are several possible conversions, + # they are sorted by decreasing conversion_chain_score. + if verbose: + print(f"Processing the remaining lines. Converting coordinates.") + new_coords = lo.convert_coordinate(chromosome, position) + # If conversion is successful, write to output + if new_coords: + if verbose: + print(f"- success for {chromosome}:{position}.") + new_chr, new_pos, new_strand, new_conversion_score = new_coords[0] + # Strip 'chr' prefix if necessary + if build == 'b38_to_b37' or build == 'b17_to_b37' or build == 'b18_to_b37': + new_chr_stripped = new_chr.lstrip('chr') # Strip 'chr' prefix + else: + new_chr_stripped = new_chr + # Write to output file + if pos_type == 0: + new_pos -= 1 # Convert to 0-based + print(f"- success for {chromosome}:{position} - found this: {new_coords}.") + f_out.write(f"{snpid}\t{new_chr_stripped}\t{new_pos}\t{new_strand}\t{new_conversion_score}\n") + else: + f_out.write(f"{snpid}\t{new_chr_stripped}\t{new_pos}\n") + successful_variants += 1 + else: + print(f"- failed for {chromosome}:{position}.") + f_failed_log_file.write(f"{snpid}\t{chromosome}\t{position}\n") + failed_variants += 1 + + # Process the remaining lines + if verbose: + print(f"Processing the remaining lines. Converting coordinates.") + + # # Initialize tqdm progress bar + # progress_bar = tqdm(desc="Converting coordinates", total=sum(1 for line in f_in), file=sys.stdout) + + for line in f_in: + parts = line.strip().split() + snpid, chromosome, position = parts[0], parts[1], int(parts[2]) + + # Convert coordinates + new_coords = lo.convert_coordinate(chromosome, position) + + # If conversion is successful, write to output + if new_coords: + new_chr, new_pos, new_strand, new_conversion_score = new_coords[0] + # Strip 'chr' prefix if necessary + if build == 'b38_to_b37' or build == 'b17_to_b37' or build == 'b18_to_b37': + new_chr_stripped = new_chr.lstrip('chr') # Strip 'chr' prefix + else: + new_chr_stripped = new_chr + # Write to output file + if pos_type == 0: + new_pos -= 1 # Convert to 0-based + print(f"- success for {chromosome}:{position} - found this: {new_coords}.") + f_out.write(f"{snpid}\t{new_chr_stripped}\t{new_pos}\t{new_strand}\t{new_conversion_score}\n") + else: + f_out.write(f"{snpid}\t{new_chr_stripped}\t{new_pos}\n") + successful_variants += 1 + else: + print(f"- failed for {chromosome}:{position}.") + f_failed_log_file.write(f"{snpid}\t{chromosome}\t{position}\n") + failed_variants += 1 + + # # Update progress bar + # progress_bar.update(1) + # # Close progress bar + # progress_bar.close() + # Write the total number of failed variants to the failed variants log file + f_failed_log_file.write(f"\nTotal number of failed variants: {failed_variants}. No further failed variants.\n") + return successful_variants, failed_variants + +def main(): + parser = argparse.ArgumentParser(description=f''' ++ {VERSION_NAME} v{VERSION} + + +This script converts genomic coordinates between genome builds using the `pyliftover` package. +The input `--input_file` file should be a tab-separated file with two columns: chromosome and position. +The output file will be a tab-separated file with two columns: chromosome and position. By default, +the output file will be named `converted_coordinates.txt` and saved in the current directory. Optionally, +the output directory can be specified using the `--output_dir` argument. + +By default the input is expected to be b37 and positions will be lift over to b38. Optionally, the build can be +specified using the `--build` argument. Supported options are 'b37_to_b38' and 'b38_to_b37'. By default, +the position type is 1-based. Optionally, the position type can be specified using the `--pos_type` argument. + +By default the script will log the failed variants to a file named `failed_variants.log` in the output directory. +By default, the script will not log the conversion process. Optionally, the script can log the conversion process +using the `--log` argument. + +Optionally, the script can print extra information using the `--verbose` argument. + +Example usage: + python liftover.py --input_file input.txt --output_dir /path/to/output_dir --build b37_to_b38 --pos_type 1 --log --verbose + + ''', + epilog=f''' ++ {VERSION_NAME} v{VERSION}. {COPYRIGHT} \n{COPYRIGHT_TEXT}+''', + formatter_class=argparse.RawTextHelpFormatter) + parser.add_argument('--input', '-i', type=str, help='Path to input tab-delimited textfile with 3 columns: snpid chr bp.') + parser.add_argument('--output_dir', '-o', type=str, default=os.getcwd(), help='Output directory. Default is current directory.') + parser.add_argument('--pos_type', '-p', type=int, default=1, choices=[0, 1], help='Position type (0 for 0-based, 1 for 1-based). Default is 1.') + parser.add_argument('--build', '-b', type=str, choices=['b37_to_b38', 'b38_to_b37', 'b17_to_b37', 'b17_to_b38', 'b18_to_b37', 'b18_to_b38'], help='Build for conversion. Default is b37_to_b38.') + parser.add_argument('--log', action='store_true', help='Enable logging. Optional.') + parser.add_argument('--verbose', '-v', action='store_true', help='Print extra information. Optional.') + parser.add_argument('--version', '-V', action='version', version=f'%(prog)s {VERSION} ({VERSION_DATE}).') + + args = parser.parse_args() + # Start the script + print(f"+ {VERSION_NAME} v{VERSION} ({VERSION_DATE}) +") + print(f"\nStarting liftOver job {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}.") + # Check if build is provided + if args.build is None: + print(f"Note. Build was not provided; assuming b37 to b38 liftover.\n") + args.build = 'b37_to_b38' + else: + print(f"Build was provided: {args.build}.\n") + + # Check if required arguments are provided + # if args.interaction == "interaction" and args.interactionparam is None: + # print(f"Error: Interaction analysis is requested, but no parameter is provided (see https://www.cog-genomics.org/plink/1.9/assoc#linear).") + # parser.print_help() + # exit(1) + required_args = ['input'] + missing_args = [arg for arg in required_args if not getattr(args, arg)] + if missing_args: + print(f"Error. The following required arguments are missing: {', '.join(missing_args)}.") + print(f"Please provide the required arguments, as shown below.\n") + parser.print_help() + exit(1) + # Start the timer + start_time = time.time() + + # Get today's date + today_date = datetime.now() + + # Format the date as yyyymmdd + formatted_today = today_date.strftime("%Y%m%d") + + # Run the conversion + if args.verbose: + print(f"Converting coordinates using the following parameters:") + print(f"- input: {args.input} (data output directory is {args.output_dir})") + if args.build == 'b38_to_b37' or args.build == 'b17_to_b37' or args.build == 'b18_to_b37': + print(f"- build {args.build}; stripping 'chr' prefix from chromosome names") + else: + print(f"- build: {args.build}") + print(f"- position-type: {args.pos_type}") + successful_variants, failed_variants = convert_coordinates(args.input, args.output_dir, args.pos_type, args.build, args.verbose) + + # Calculate the elapsed time in seconds + elapsed_time = time.time() - start_time + # Convert seconds to a timedelta object + time_delta = timedelta(seconds=elapsed_time) + # Extract hours, minutes, seconds, and milliseconds + hours, remainder = divmod(time_delta.seconds, 3600) + minutes, seconds = divmod(remainder, 60) + milliseconds = round(time_delta.microseconds / 1000) + # Print the script execution time in the desired format + formatted_time = f"{hours} hours, {minutes} minutes, {seconds} seconds, {milliseconds} milliseconds" + + print(f"\nScript executed on {today_date.strftime('%Y-%m-%d')}. Total execution time was {formatted_time} (minus writing time).") + print(f"Successful variants: {successful_variants}, Failed variants: {failed_variants}") + + if args.log: + # Create a log file + log_file = os.path.join(args.output_dir, f'lifover.{args.build}.log') + with open(log_file, 'w') as f_log_file: + f_log_file.write(f"+ {VERSION_NAME} v{VERSION} +\n") + f_log_file.write(f"Start time: {start_time}\n") + f_log_file.write(f"liftOver job settings:") + f_log_file.write(f"- input: {args.input} (data output directory is {args.output_dir})\n") + if args.build == 'b38_to_b37' or args.build == 'b17_to_b37' or args.build == 'b18_to_b37': + f_log_file.write(f"- build {args.build}; stripping 'chr' prefix from chromosome names\n") + else: + f_log_file.write(f"- build: {args.build}\n") + f_log_file.write(f"- position-type: {args.pos_type}\n") + f_log_file.write(f"\nScript executed on {today_date.strftime('%Y-%m-%d')}. Total execution time was {formatted_time} (minus writing time).") + f_log_file.write(f"Successful variants: {successful_variants}. Failed variants: {failed_variants}.") + # Write the execution time to the failed variants log file + failed_log_file = os.path.join(args.output_dir, f'lifover.{args.build}.failed_variants.log') + with open(failed_log_file, 'a') as f_failed_log_file: + f_failed_log_file.write(f"\nScript executed on {today_date.strftime('%Y-%m-%d')}. Total execution time was {formatted_time} (minus writing time).") + +if __name__ == "__main__": + main() + +# Print the version number +print(f"\n+ {VERSION_NAME} v{VERSION} ({VERSION_DATE}). {COPYRIGHT} +") +print(f"{COPYRIGHT_TEXT}") +# End of file diff --git a/SCRIPTS/metagwastoolkit.downstream.analysis.sh b/SCRIPTS/metagwastoolkit.downstream.analysis.sh new file mode 100644 index 0000000..5872a9d --- /dev/null +++ b/SCRIPTS/metagwastoolkit.downstream.analysis.sh @@ -0,0 +1,503 @@ + #!/bin/bash + +### Creating display functions +### Setting colouring +NONE='\033[00m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +BOLD='\033[1m' +ITALIC='\033[3m' +UNDERLINE='\033[4m' +STRIKETHROUGH='\033[9m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2015-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} + +script_arguments_error() { + echoerror "$1" # Additional message + echoerror "- Argument #1 is path_to/filename of the configuration file." + echoerror "- Argument #2 is path_to/filename of the list of GWAS files with names." + echoerror "" + echoerror "An example command would be: run_metagwastoolkit.sh [arg1] [arg2]" + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "" + script_copyright_message + exit 1 +} + +echobold "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " MetaGWASToolKit: A TOOLKIT FOR THE META-ANALYSIS OF GENOME-WIDE ASSOCIATION STUDIES" +echobold " --- PERFORM AND PREPARE DOWNSTREAM ANALYSES ---" +echobold "" +echobold "* Version: v1.7.3" +echobold "" +echobold "* Last update: 2023-01-21" +echobold "* Based on: MANTEL, as written by Sara Pulit, Jessica van Setten, and Paul de Bakker." +echobold "* Written by: Sander W. van der Laan | s.w.vanderlaan@gmail.com." +echobold " Sara Pulit; " +echobold " Jessica van Setten; " +echobold " Paul I.W. de Bakker." +echobold "* Testers: Jessica van Setten." +echobold "* Description: Perform a meta-analysis of genome-wide association studies. It will do the following:" +echobold " - Automatically parse the various cohort files." +echobold " - Harmonize GWAS datasets relative to a reference." +echobold " - Perform QC on GWAS datasets using user-defined settings." +echobold " - Produce plots (PDF and PNG) for quick inspection and publication." +echobold " - Run a meta-analysis using Random, Fixed, and Z-score methods." +echobold " - Correct results for the genomic inflation factor." +echobold " - Clump results based on a p-value threshold for downstream (meta-)analyses." +echobold " - Produce plots (PDF and PNG) of the final meta-analysis results for publication." +echobold " - Produce LocusZoom style regional plots for genome-wide significant hits." +echobold " - Produce a ReadMe file." +echobold "" +echobold "* REQUIRED: " +echobold " - A high-performance computer cluster with a qsub system" +echobold " - R v3.2+, Python 2.7+, Perl." +echobold " - Required Python 2.7+ modules: [pandas], [scipy], [numpy]." +echobold " - Required Perl modules: [YAML], [Statistics::Distributions], [Getopt::Long]." +echobold " - Note: it will also work on a Mac OS X system with R and Python installed." +### ADD-IN: function to check requirements... +### This might be a viable option! https://gist.github.com/JamieMason/4761049 +echobold "" +echobold "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + + +########################################################################################## +### SET THE SCENE FOR THE SCRIPT +########################################################################################## + +### START of if-else statement for the number of command-line arguments passed ### +if [[ $# -lt 2 ]]; then + echo "" + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echoerrorflash " *** Oh, oh, computer says no! Number of arguments found "$#". ***" + echoerror "You must supply [2] arguments when running *** MetaGWASToolKit ***!" + script_arguments_error +else + echo "These are the "$#" arguments that passed:" + echo "The configuration file.................: "$(basename ${1}) # argument 1 + echo "The list of GWAS files.................: "$(basename ${2}) # argument 2 + + ### SETTING DIRECTORIES (from configuration file). + # Loading the configuration file (please refer to the MetaGWASToolKit-Manual for specifications of this file). + source "$1" # Depends on arg1. + + CONFIGURATIONFILE="$1" # Depends on arg1 -- but also on where it resides!!! + SOFTWARE=${SOFTWARE} # from configuration file + + # Where MetaGWASToolKit resides + METAGWASTOOLKIT=${METAGWASTOOLKITDIR} # from configuration file + SCRIPTS=${METAGWASTOOLKIT}/SCRIPTS + #RESOURCES=${METAGWASTOOLKIT}/RESOURCES + RESOURCES="/hpc/local/Rocky8/dhl_ec/software/MetaGWASToolKit/RESOURCES" + + # Project information + ORIGINALS=${DATA_UPLOAD_FREEZE} # from configuration file + PROJECTDIR=${PROJECTDIR} # from configuration file + SUBPROJECTDIRNAME=${SUBPROJECTDIRNAME} # from configuration file + OUTPUTDIRNAME=${OUTPUTDIRNAME} # from configuration file + GWASFILES="$2" # Depends on arg2 -- all the GWAS dataset information + REFERENCE=${REFERENCE} # from configuration file + POPULATION=${POPULATION} # from configuration file + + # Needed for pipeline + python3_exe="/hpc/local/Rocky8/dhl_ec/software/mambaforge3/envs/polyfun/bin/python3" + METARESULTDIR="${PROJECTDIR}/${OUTPUTDIRNAME}/${SUBPROJECTDIRNAME}/META" + PHENOTYPE_FILE="${PROJECTDIR}/${OUTPUTDIRNAME}/${PROJECTNAME}.PHENOTYPES.txt" + TRAIT_FILE=${TRAITFILE} + #REF_LD="${RESOURCES}/${POPULATION}_w_ld_chr/" + #REF_LD="/hpc/dhl_ec/esmulders/MetaGWASToolKit/RESOURCES/${POPULATION}_w_ld_chr/" + REF_LD=${REF_LD} + REF=${REF} + #REF="/hpc/dhl_ec/esmulders/references" + #SNPLIST="${RESOURCES}/w_hm3.snplist" + SNPLIST=${SNPLIST} + #GSMR_REF="/hpc/dhl_ec/esmulders/gsmr/gsmr_ref_data.${POPULATION}.txt" + GSMR_REF=${GSMR_REF} + + PERFORM_LDSC=${PERFORM_LDSC} + PERFORM=POLYFUN=${PERFORM_POLYFUN} + PERFORM_GSMR=${PERFORM_GSMR} + + #polyfun + min_maf=0.005 + max_num_causal=1 + pip_cutoff=0.50 + CHUNKSIZE=500000 + + ########################################################################################## + ### CREATE THE OUTPUT DIRECTORIES + echo "" + echo "Checking for the existence of the output directory [ ${OUTPUTDIRNAME} ]." + if [ ! -d ${PROJECTDIR}/${OUTPUTDIRNAME} ]; then + echo "> Output directory doesn't exist - Mr. Bourne will create it for you." + mkdir -v ${PROJECTDIR}/${OUTPUTDIRNAME} + else + echo "> Output directory already exists." + fi + METAOUTPUT=${OUTPUTDIRNAME} + + echo "" + echo "Checking for the existence of the subproject directory [ ${METAOUTPUT}/${SUBPROJECTDIRNAME} ]." + if [ ! -d ${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME} ]; then + echo "> Subproject directory doesn't exist - Mr. Bourne will create it for you." + mkdir -v ${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME} + else + echo "> Subproject directory already exists." + fi + SUBPROJECTDIR=${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME} + + echo "" + echo "Checking for the existence of the raw METARESULTDIR directory [ ${METAOUTPUT}/${SUBPROJECTDIRNAME}/RAW ]." + if [ ! -d ${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME}/RAW ]; then + echo "> Raw METARESULTDIR directory doesn't exist - Mr. Bourne will create it for you." + mkdir -v ${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME}/RAW + else + echo "> Raw METARESULTDIR directory already exists." + fi + # Setting directory for raw METARESULTDIR. + RAWDATA=${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME}/RAW + + echo "" + echo "Checking for the existence of the meta-analysis results directory [ ${METAOUTPUT}/${SUBPROJECTDIRNAME}/META ]." + if [ ! -d ${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME}/META ]; then + echo "> Meta-analysis results directory doesn't exist - Mr. Bourne will create it for you." + mkdir -v ${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME}/META + else + echo "> Meta-analysis results directory already exists." + fi + # Setting directory for meta-analysis METARESULTDIR. + METARESULTDIR=${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME}/META + + echo "Checking for the existence of the meta-analysis temporary results directory [ ${METAOUTPUT}/${SUBPROJECTDIRNAME}/META/TEMP ]." + if [ ! -d ${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME}/META/TEMP ]; then + echo "> Meta-analysis results temporary directory doesn't exist - Mr. Bourne will create it for you." + mkdir -v ${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME}/META/TEMP + else + echo "> Meta-analysis results temporary directory already exists." + fi + # Setting directory for meta-analysis temporary METARESULTDIR. + METATEMPRESULTDIR=${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME}/META/TEMP + + ########################################################################################## + ### SETTING UP THE OUTPUT AND RAWDATA DIRECTORIES + echo "" + ### Making raw METARESULTDIR directories, unless they already exist. Depends on arg2. + if [[ ${REFERENCE} = "1Gp1" ]]; then + + echo "" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "" + echo "The scene is properly set, and directories are created! 🖖" + echo "MetaGWASToolKit program........................: "${METAGWASTOOLKIT} + echo "MetaGWASToolKit scripts........................: "${SCRIPTS} + echo "MetaGWASToolKit resources......................: "${RESOURCES} + echo "Reference used.................................: "${REFERENCE} + echo "Main directory.................................: "${PROJECTDIR} + echo "Main analysis output directory.................: "${METAOUTPUT} + echo "Subproject's analysis output directory.........: "${METAOUTPUT}/${SUBPROJECTDIRNAME} + echo "Original METARESULTDIR directory........................: "${ORIGINALS} + echo "We are processing these cohort(s)..............:" + while IFS='' read -r GWASCOHORT || [[ -n "$GWASCOHORT" ]]; do + LINE=${GWASCOHORT} + COHORT=$(echo "${LINE}" | awk '{ print $1 }') + echo " * ${COHORT}" + done < ${GWASFILES} + echo "Raw METARESULTDIR directory.............................: "${METAOUTPUT}/${SUBPROJECTDIRNAME}/RAW + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "" + + elif [[ ${REFERENCE} = "1Gp3" ]]; then + + echo "" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "" + echo "The scene is properly set, and directories are created! 🖖" + echo "MetaGWASToolKit program........................: "${METAGWASTOOLKIT} + echo "MetaGWASToolKit scripts........................: "${SCRIPTS} + echo "MetaGWASToolKit resources......................: "${RESOURCES} + echo "Reference used.................................: "${REFERENCE} + echo "Main directory.................................: "${PROJECTDIR} + echo "Main analysis output directory.................: "${METAOUTPUT} + echo "Subproject's analysis output directory.........: "${METAOUTPUT}/${SUBPROJECTDIRNAME} + echo "Original METARESULTDIR directory........................: "${ORIGINALS} + echo "We are processing these cohort(s)..............:" + while IFS='' read -r GWASCOHORT || [[ -n "$GWASCOHORT" ]]; do + LINE=${GWASCOHORT} + COHORT=$(echo "${LINE}" | awk '{ print $1 }') + echo " * ${COHORT}" + done < ${GWASFILES} + echo "Raw METARESULTDIR directory.............................: "${METAOUTPUT}/${SUBPROJECTDIRNAME}/RAW + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "" + + elif [[ ${REFERENCE} = "1Gp3GONL5" ]]; then + + echo "" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "" + echo "The scene is properly set, and directories are created! 🖖" + echo "MetaGWASToolKit program........................: "${METAGWASTOOLKIT} + echo "MetaGWASToolKit scripts........................: "${SCRIPTS} + echo "MetaGWASToolKit resources......................: "${RESOURCES} + echo "Reference used.................................: "${REFERENCE} + echo "Main directory.................................: "${PROJECTDIR} + echo "Main analysis output directory.................: "${METAOUTPUT} + echo "Subproject's analysis output directory.........: "${METAOUTPUT}/${SUBPROJECTDIRNAME} + echo "Original METARESULTDIR directory........................: "${ORIGINALS} + echo "We are processing these cohort(s)..............:" + while IFS='' read -r GWASCOHORT || [[ -n "$GWASCOHORT" ]]; do + LINE=${GWASCOHORT} + COHORT=$(echo "${LINE}" | awk '{ print $1 }') + echo " * ${COHORT}" + done < ${GWASFILES} + echo "Raw METARESULTDIR directory.............................: "${METAOUTPUT}/${SUBPROJECTDIRNAME}/RAW + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "" + + elif [[ ${REFERENCE} = "HM2" || ${REFERENCE} = "GONL4" || ${REFERENCE} = "GONL5" ]]; then + echoerrornooption "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echoerrornooption "" + echoerrorflashnooption " *** Oh, computer says no! This option is not available yet. ***" + echoerrornooption "Unfortunately using ${REFERENCE} as a reference is not possible yet. Currently only 1Gp1 is available." + echoerrornooption "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + ### The wrong arguments are passed, so we'll exit the script now! + echo "" + script_copyright_message + exit 1 + + else + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echoerror "" + echoerrorflash " *** Oh, computer says no! Argument not recognised. ***" + echoerror "You have the following options as reference for the quality control" + echoerror "and meta-analysis:" + echonooption " - [HM2] HapMap2 (r27, b36, hg18)." + echoerror " - [1Gp1] 1000G (phase 1, release 3, 20101123 version, updated on 20110521 " + echoerror " and revised on Feb/Mar 2012, b37, hg19)." + echoerror " - [1Gp3] 1000G (phase 3, release 5c, 20130502 version, b37, hg19)." + echonooption " - [GoNL4] Genome of the Netherlands, version 4." + echonooption " - [GONL5] Genome of the Netherlands, version 5." + echoerror " - [1Gp3GONL5] integrated 1000G phase 3, version 5 and GoNL5." + echonooption "(Opaque: not an option yet)" + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + ### The wrong arguments are passed, so we'll exit the script now! + echo "" + script_copyright_message + exit 1 + fi + + if [ "$PERFORM_LDSC" == "YES" ]; then + # LDSC + mkdir -p "${METARESULTDIR}/ldsc" + + # Initialize variables + TRAIT_JOB_IDS=() + + # Trait analysis + data_analysed="TRAIT" + #if [ "$data_analysed" == "TRAIT" ] && [ -n "$TRAIT_FILE" ]; then + TRAITS=() + PATHS=() + while IFS= read -r line || [ -n "$line" ]; do + read -r trait path <<< "$line" + TRAITS+=("$trait") + PATHS+=("$path") + done < "$TRAIT_FILE" + + if [ ${#TRAITS[@]} -eq 0 ]; then + echo "Trait file is empty or not properly formatted." + exit 1 + fi + + for (( i=0; i<${#TRAITS[@]}; i++ )); do + TRAIT="${TRAITS[i]}" + TRAITPATH="${PATHS[i]}" + echo "Analyzing trait: ${TRAIT}" + JOB_ID=$(sbatch --parsable ${SCRIPTS}/ldsc/ldsc.run.sh \ + -w "$data_analysed" \ + -d "$METARESULTDIR" \ + -j "$PROJECTNAME" \ + -p "$PHENOTYPE_FILE" \ + -t "$TRAIT" \ + -i "$TRAITPATH" \ + -l "$REF_LD" \ + -h "$SNPLIST" \ + -r rsid \ + -n n \ + -a a1 \ + -b a2 \ + -v p \ + -f eaf \ + -c "$CHUNKSIZE") + TRAIT_JOB_IDS+=("$JOB_ID") + done + fi + + # Phenotype analysis + if [ "$PERFORM_LDSC" == "YES" ]; then + mkdir -p "${METARESULTDIR}/ldsc" + data_analysed="PHENOTYPE" + #if [ "$data_analysed" == "PHENOTYPE" ]; then + if [ -n "$PHENOTYPE_FILE" ]; then + PHENOTYPES=() + SAMPLESIZES=() + while IFS=' ' read -r phenotype sample_size; do + PHENOTYPES+=("$phenotype") + SAMPLESIZES+=("$sample_size") + done < "$PHENOTYPE_FILE" + + if [ ${#PHENOTYPES[@]} -eq 0 ]; then + echo "Phenotype file is empty or not properly formatted." + exit 1 + fi + else + echo "Phenotype file (-p) is required." + exit 1 + fi + + # Create a comma-separated list of trait job IDs for dependency + TRAIT_JOB_DEP=$(IFS=,; echo "${TRAIT_JOB_IDS[*]}") + + for (( i=0; i<${#PHENOTYPES[@]}; i++ )); do + PHENOTYPE="${PHENOTYPES[i]}" + SAMPLE_SIZE="${SAMPLESIZES[i]}" + echo "Analyzing phenotype: ${PHENOTYPE} with sample size ${SAMPLE_SIZE}" + sbatch --dependency=afterok:$TRAIT_JOB_DEP ${SCRIPTS}/ldsc/ldsc.run.sh \ + -w "$data_analysed" \ + -d "$METARESULTDIR" \ + -p "$PHENOTYPE" \ + -t "$TRAIT_FILE" \ + -l "$REF_LD" \ + -h "$SNPLIST" \ + -r rsid \ + -n n \ + -a a1 \ + -b a2 \ + -v p \ + -f eaf \ + -c "$CHUNKSIZE" + done + #fi + fi + + + #GSMR + if [ "$PERFORM_GSMR" == "YES" ]; then + # GSMR + mkdir -p "${METARESULTDIR}/gsmr" + #if [ "$data_analysed" == "PHENOTYPE" ]; then + if [ -n "$PHENOTYPE_FILE" ]; then + PHENOTYPES=() + while IFS=' ' read -r phenotype sample_size; do + PHENOTYPES+=("$phenotype") + done < "$PHENOTYPE_FILE" + + if [ ${#PHENOTYPES[@]} -eq 0 ]; then + echo "Phenotype file is empty or not properly formatted." + exit 1 + fi + else + echo "Phenotype file (-p) is required." + exit 1 + fi + + for (( i=0; i<${#PHENOTYPES[@]}; i++ )); do + PHENOTYPE="${PHENOTYPES[i]}" + sbatch ${SCRIPTS}/gsmr/gsmr.analysis.sh \ + -t "${TRAIT_FILE}" \ + -d "${METARESULTDIR}" \ + -j "${PROJECTNAME}" \ + -p "${PHENOTYPE}" \ + -r "${GSMR_REF}" + done + #fi + fi + + ### PolyFun + if [ "$PERFORM_POLYFUN" == "YES" ]; then + # PolyFun + mkdir -p "${METARESULTDIR}/polyfun" + #if [ "$data_analysed" == "PHENOTYPE" ]; then + if [ -n "$PHENOTYPE_FILE" ]; then + PHENOTYPES=() + SAMPLESIZES=() + while IFS=' ' read -r phenotype sample_size; do + PHENOTYPES+=("$phenotype") + SAMPLESIZES+=("$sample_size") + done < "$PHENOTYPE_FILE" + if [ ${#PHENOTYPES[@]} -eq 0 ]; then + echo "Phenotype file is empty or not properly formatted." + exit 1 + fi + else + echo "Phenotype file (-p) is required." + exit 1 + fi + for (( i=0; i<${#PHENOTYPES[@]}; i++ )); do + PHENOTYPE="${PHENOTYPES[i]}" + SAMPLESIZE="${SAMPLESIZES[i]}" + #echo "${PHENOTYPE}" + bash ${SCRIPTS}/polyfun/polyfun.run.sh \ + -p "${PHENOTYPE}" \ + -s "${SAMPLESIZE}" \ + -d "${METARESULTDIR}" \ + -j "${METARESULTDIR}/GWASCatalog/${PROJECTNAME}.b37.gwaslab.qc.ssf.tsv.gz" \ + -l "${REF}" + done + #fi + fi +fi \ No newline at end of file diff --git a/SCRIPTS/metagwastoolkit.gwaslab.sh b/SCRIPTS/metagwastoolkit.gwaslab.sh new file mode 100644 index 0000000..f8b27ed --- /dev/null +++ b/SCRIPTS/metagwastoolkit.gwaslab.sh @@ -0,0 +1,332 @@ +#!/usr/bin/env bash + +### Creating display functions +### Setting colouring +NONE='\033[00m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +BOLD='\033[1m' +ITALIC='\033[3m' +UNDERLINE='\033[4m' +STRIKETHROUGH='\033[9m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2015-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} + +script_arguments_error() { + echoerror "$1" # Additional message + echoerror "- Argument #1 is path_to/filename of the configuration file." + echoerror "- Argument #2 is path_to/filename of the list of GWAS files with names." + echoerror "" + echoerror "An example command would be: run_metagwastoolkit.sh [arg1] [arg2]" + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "" + script_copyright_message + exit 1 +} + +echobold "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " MetaGWASToolKit: A TOOLKIT FOR THE META-ANALYSIS OF GENOME-WIDE ASSOCIATION STUDIES" +echobold " --- PERFORM AND PREPARE DOWNSTREAM ANALYSES ---" +echobold "" +echobold "* Version: v1.7.3" +echobold "" +echobold "* Last update: 2023-01-21" +echobold "* Based on: MANTEL, as written by Sara Pulit, Jessica van Setten, and Paul de Bakker." +echobold "* Written by: Sander W. van der Laan | s.w.vanderlaan@gmail.com." +echobold " Sara Pulit; " +echobold " Jessica van Setten; " +echobold " Paul I.W. de Bakker." +echobold "* Testers: Jessica van Setten." +echobold "* Description: Perform a meta-analysis of genome-wide association studies. It will do the following:" +echobold " - Automatically parse the various cohort files." +echobold " - Harmonize GWAS datasets relative to a reference." +echobold " - Perform QC on GWAS datasets using user-defined settings." +echobold " - Produce plots (PDF and PNG) for quick inspection and publication." +echobold " - Run a meta-analysis using Random, Fixed, and Z-score methods." +echobold " - Correct results for the genomic inflation factor." +echobold " - Clump results based on a p-value threshold for downstream (meta-)analyses." +echobold " - Produce plots (PDF and PNG) of the final meta-analysis results for publication." +echobold " - Produce LocusZoom style regional plots for genome-wide significant hits." +echobold " - Produce a ReadMe file." +echobold "" +echobold "* REQUIRED: " +echobold " - A high-performance computer cluster with a qsub system" +echobold " - R v3.2+, Python 2.7+, Perl." +echobold " - Required Python 2.7+ modules: [pandas], [scipy], [numpy]." +echobold " - Required Perl modules: [YAML], [Statistics::Distributions], [Getopt::Long]." +echobold " - Note: it will also work on a Mac OS X system with R and Python installed." +### ADD-IN: function to check requirements... +### This might be a viable option! https://gist.github.com/JamieMason/4761049 +echobold "" +echobold "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + + +########################################################################################## +### SET THE SCENE FOR THE SCRIPT +########################################################################################## + +### START of if-else statement for the number of command-line arguments passed ### +if [[ $# -lt 2 ]]; then + echo "" + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echoerrorflash " *** Oh, oh, computer says no! Number of arguments found "$#". ***" + echoerror "You must supply [2] arguments when running *** MetaGWASToolKit ***!" + script_arguments_error +else + echo "These are the "$#" arguments that passed:" + echo "The configuration file.................: "$(basename ${1}) # argument 1 + echo "The list of GWAS files.................: "$(basename ${2}) # argument 2 + + ### SETTING DIRECTORIES (from configuration file). + # Loading the configuration file (please refer to the MetaGWASToolKit-Manual for specifications of this file). + source "$1" # Depends on arg1. + + CONFIGURATIONFILE="$1" # Depends on arg1 -- but also on where it resides!!! + SOFTWARE=${SOFTWARE} # from configuration file + + # Where MetaGWASToolKit resides + METAGWASTOOLKIT=${METAGWASTOOLKITDIR} # from configuration file + SCRIPTS=${METAGWASTOOLKIT}/SCRIPTS + #RESOURCES=${METAGWASTOOLKIT}/RESOURCES + RESOURCES="/hpc/local/Rocky8/dhl_ec/software/MetaGWASToolKit/RESOURCES" + + # Project information + ORIGINALS=${DATA_UPLOAD_FREEZE} # from configuration file + PROJECTDIR=${PROJECTDIR} # from configuration file + SUBPROJECTDIRNAME=${SUBPROJECTDIRNAME} # from configuration file + OUTPUTDIRNAME=${OUTPUTDIRNAME} # from configuration file + PROJECTNAME=${PROJECTNAME} + GWASFILES="$2" # Depends on arg2 -- all the GWAS dataset information + REFERENCE=${REFERENCE} # from configuration file + POPULATION=${POPULATION} # from configuration file + MAKE_FIGURES=${GWASLABPLOT} + PERFORM_QC=${PERFORM_QC} + ONLY_QC=${ONLY_QC} + SELECT_LEADS=${SELECT_LEADS} + EAF=${MAF} + DF=${DF} + DAF=${DAF} + # Needed for pipeline + METARESULTDIR="${PROJECTDIR}/${OUTPUTDIRNAME}/${SUBPROJECTDIRNAME}/META" + # has to contain / at the end, otherwise files will be downloaded in the previous folder + REF="/hpc/dhl_ec/esmulders/references/gwaslab/" + + ########################################################################################## + ### CREATE THE OUTPUT DIRECTORIES + echo "" + echo "Checking for the existence of the output directory [ ${OUTPUTDIRNAME} ]." + if [ ! -d ${PROJECTDIR}/${OUTPUTDIRNAME} ]; then + echo "> Output directory doesn't exist - Mr. Bourne will create it for you." + mkdir -v ${PROJECTDIR}/${OUTPUTDIRNAME} + else + echo "> Output directory already exists." + fi + METAOUTPUT=${OUTPUTDIRNAME} + + echo "" + echo "Checking for the existence of the subproject directory [ ${METAOUTPUT}/${SUBPROJECTDIRNAME} ]." + if [ ! -d ${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME} ]; then + echo "> Subproject directory doesn't exist - Mr. Bourne will create it for you." + mkdir -v ${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME} + else + echo "> Subproject directory already exists." + fi + SUBPROJECTDIR=${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME} + + echo "" + echo "Checking for the existence of the raw METARESULTDIR directory [ ${METAOUTPUT}/${SUBPROJECTDIRNAME}/RAW ]." + if [ ! -d ${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME}/RAW ]; then + echo "> Raw METARESULTDIR directory doesn't exist - Mr. Bourne will create it for you." + mkdir -v ${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME}/RAW + else + echo "> Raw METARESULTDIR directory already exists." + fi + # Setting directory for raw METARESULTDIR. + RAWDATA=${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME}/RAW + + echo "" + echo "Checking for the existence of the meta-analysis results directory [ ${METAOUTPUT}/${SUBPROJECTDIRNAME}/META ]." + if [ ! -d ${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME}/META ]; then + echo "> Meta-analysis results directory doesn't exist - Mr. Bourne will create it for you." + mkdir -v ${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME}/META + else + echo "> Meta-analysis results directory already exists." + fi + # Setting directory for meta-analysis METARESULTDIR. + METARESULTDIR=${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME}/META + + echo "Checking for the existence of the meta-analysis temporary results directory [ ${METAOUTPUT}/${SUBPROJECTDIRNAME}/META/TEMP ]." + if [ ! -d ${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME}/META/TEMP ]; then + echo "> Meta-analysis results temporary directory doesn't exist - Mr. Bourne will create it for you." + mkdir -v ${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME}/META/TEMP + else + echo "> Meta-analysis results temporary directory already exists." + fi + # Setting directory for meta-analysis temporary METARESULTDIR. + METATEMPRESULTDIR=${PROJECTDIR}/${METAOUTPUT}/${SUBPROJECTDIRNAME}/META/TEMP + + ########################################################################################## + ### SETTING UP THE OUTPUT AND RAWDATA DIRECTORIES + echo "" + ### Making raw METARESULTDIR directories, unless they already exist. Depends on arg2. + if [[ ${REFERENCE} = "1Gp1" ]]; then + + echo "" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "" + echo "The scene is properly set, and directories are created! 🖖" + echo "MetaGWASToolKit program........................: "${METAGWASTOOLKIT} + echo "MetaGWASToolKit scripts........................: "${SCRIPTS} + echo "MetaGWASToolKit resources......................: "${RESOURCES} + echo "Reference used.................................: "${REFERENCE} + echo "Main directory.................................: "${PROJECTDIR} + echo "Main analysis output directory.................: "${METAOUTPUT} + echo "Subproject's analysis output directory.........: "${METAOUTPUT}/${SUBPROJECTDIRNAME} + echo "Original METARESULTDIR directory........................: "${ORIGINALS} + echo "We are processing these cohort(s)..............:" + while IFS='' read -r GWASCOHORT || [[ -n "$GWASCOHORT" ]]; do + LINE=${GWASCOHORT} + COHORT=$(echo "${LINE}" | awk '{ print $1 }') + echo " * ${COHORT}" + done < ${GWASFILES} + echo "Raw METARESULTDIR directory.............................: "${METAOUTPUT}/${SUBPROJECTDIRNAME}/RAW + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "" + + elif [[ ${REFERENCE} = "1Gp3" ]]; then + + echo "" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "" + echo "The scene is properly set, and directories are created! 🖖" + echo "MetaGWASToolKit program........................: "${METAGWASTOOLKIT} + echo "MetaGWASToolKit scripts........................: "${SCRIPTS} + echo "MetaGWASToolKit resources......................: "${RESOURCES} + echo "Reference used.................................: "${REFERENCE} + echo "Main directory.................................: "${PROJECTDIR} + echo "Main analysis output directory.................: "${METAOUTPUT} + echo "Subproject's analysis output directory.........: "${METAOUTPUT}/${SUBPROJECTDIRNAME} + echo "Original METARESULTDIR directory........................: "${ORIGINALS} + echo "We are processing these cohort(s)..............:" + while IFS='' read -r GWASCOHORT || [[ -n "$GWASCOHORT" ]]; do + LINE=${GWASCOHORT} + COHORT=$(echo "${LINE}" | awk '{ print $1 }') + echo " * ${COHORT}" + done < ${GWASFILES} + echo "Raw METARESULTDIR directory.............................: "${METAOUTPUT}/${SUBPROJECTDIRNAME}/RAW + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "" + + elif [[ ${REFERENCE} = "1Gp3GONL5" ]]; then + + echo "" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "" + echo "The scene is properly set, and directories are created! 🖖" + echo "MetaGWASToolKit program........................: "${METAGWASTOOLKIT} + echo "MetaGWASToolKit scripts........................: "${SCRIPTS} + echo "MetaGWASToolKit resources......................: "${RESOURCES} + echo "Reference used.................................: "${REFERENCE} + echo "Main directory.................................: "${PROJECTDIR} + echo "Main analysis output directory.................: "${METAOUTPUT} + echo "Subproject's analysis output directory.........: "${METAOUTPUT}/${SUBPROJECTDIRNAME} + echo "Original METARESULTDIR directory........................: "${ORIGINALS} + echo "We are processing these cohort(s)..............:" + while IFS='' read -r GWASCOHORT || [[ -n "$GWASCOHORT" ]]; do + LINE=${GWASCOHORT} + COHORT=$(echo "${LINE}" | awk '{ print $1 }') + echo " * ${COHORT}" + done < ${GWASFILES} + echo "Raw METARESULTDIR directory.............................: "${METAOUTPUT}/${SUBPROJECTDIRNAME}/RAW + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "" + + elif [[ ${REFERENCE} = "HM2" || ${REFERENCE} = "GONL4" || ${REFERENCE} = "GONL5" ]]; then + echoerrornooption "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echoerrornooption "" + echoerrorflashnooption " *** Oh, computer says no! This option is not available yet. ***" + echoerrornooption "Unfortunately using ${REFERENCE} as a reference is not possible yet. Currently only 1Gp1 is available." + echoerrornooption "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + ### The wrong arguments are passed, so we'll exit the script now! + echo "" + script_copyright_message + exit 1 + + else + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echoerror "" + echoerrorflash " *** Oh, computer says no! Argument not recognised. ***" + echoerror "You have the following options as reference for the quality control" + echoerror "and meta-analysis:" + echonooption " - [HM2] HapMap2 (r27, b36, hg18)." + echoerror " - [1Gp1] 1000G (phase 1, release 3, 20101123 version, updated on 20110521 " + echoerror " and revised on Feb/Mar 2012, b37, hg19)." + echoerror " - [1Gp3] 1000G (phase 3, release 5c, 20130502 version, b37, hg19)." + echonooption " - [GoNL4] Genome of the Netherlands, version 4." + echonooption " - [GONL5] Genome of the Netherlands, version 5." + echoerror " - [1Gp3GONL5] integrated 1000G phase 3, version 5 and GoNL5." + echonooption "(Opaque: not an option yet)" + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + ### The wrong arguments are passed, so we'll exit the script now! + echo "" + script_copyright_message + exit 1 + fi + + source /hpc/local/Rocky8/dhl_ec/software/mambaforge3/bin/activate gwaslab_env + python3 ${SCRIPTS}/gwaslab_sumstats.py -g ${PROJECTNAME} -d ${METARESULTDIR} -p ${POPULATION} -r ${REF} --qc ${PERFORM_QC} --figures ${MAKE_FIGURES} --onlyqc {ONLY_QC} --leads ${SELECT_LEADS} +fi \ No newline at end of file diff --git a/SCRIPTS/pipeline.gwaslab.py b/SCRIPTS/pipeline.gwaslab.py new file mode 100644 index 0000000..433f2f4 --- /dev/null +++ b/SCRIPTS/pipeline.gwaslab.py @@ -0,0 +1,420 @@ +# Import packages +import os +import glob +import importlib +import sys +from subprocess import check_output +import polars as pl +import gwaslab as gl +import argparse +from datetime import datetime +import pandas as pd +import pyarrow as pa +from scipy import stats +import numpy as np +import cmcrameri as ccm +from cmcrameri import cm +import matplotlib +import matplotlib.pyplot as plt +import seaborn as sns +import liftover +from liftover import get_lifter + +parser = argparse.ArgumentParser(description="Parser commands.") +# parser.add_argument("-i", "--identifier", help="The VariantID identifier to use (" + ", ".join(["VariantID"] + alt_ids) + ").", type=str) +# parser.add_argument("-s", "--subsample", help="Number of rows to use for subsampling, when determining the optimal VariantID (default = 100,000)", type=int) + +requiredNamed = parser.add_argument_group('required named arguments') + +requiredNamed.add_argument("-g", "--gwas", help="The name of the GWAS study.", type=str) +requiredNamed.add_argument("-i", "--input", help="The path and name of the parsed input file.", type=str) +requiredNamed.add_argument("-d", "--directory", help="The path to the results directory.", type=str) +requiredNamed.add_argument("-r", "--reference", help="The path to the references directory.", type=str) +requiredNamed.add_argument("-p", "--population", help="Population analysed.", type=str) +requiredNamed.add_argument("-f", "--figures", help="Make plots or not?(YES or NO).", type=str) +requiredNamed.add_argument("-q", "--qc", help="Perform Quality Control or not?(YES or NO).", type=str) + +#requiredNamed.add_argument("-o", "--output", help="File name for the output file to store the results.", type=str) +args = parser.parse_args() + +#reference_identifier = args.identifier +#### set some general defaults + +PHENOTYPE = args.gwas +INPUT= args.input +#PHENOTYPE = "cox_DEAD_ALL" # option +#SUBSTUDY_PHENO = f"META_{PHENOTYPE}" + +POPULATION = args.population + +#POPULATION = "EUR" # option + +perform_qc ="YES" + +# Reference data directory +REF_loc = args.reference + +#REF_loc = "/hpc/dhl_ec/esmulders/references" +print("Checking contents of the reference directory:") +print(check_output(["ls", os.path.join(REF_loc)]).decode("utf8")) + +# GWAS data directory + +GWAS_RES_loc = args.directory +# print("Checking contents of the GWAS results directory:") +# print(check_output(["ls", os.path.join(GWAS_RES_loc)]).decode("utf8")) +# GWAS_RES_loc = "/hpc/dhl_ec/svanderlaan/projects/consortia/CHARGE_cIMT_Sex/CHARGE_cIMT_EUR/cimt_eur/META/" +# print("Checking contents of the GWAS results directory:") +# print(check_output(["ls", os.path.join(GWAS_RES_loc)]).decode("utf8")) + +# GWAS_DATASETS_loc = "/hpc/dhl_ec/esmulders/GENIUS_CHD/gwaslab/" +# print("Checking contents of the GWAS Datasets directory:") +# print(check_output(["ls", os.path.join(GWAS_DATASETS_loc)]).decode("utf8")) + +# Check if the GWASCatalog directory exists within GWAS_RES_loc +if not os.path.exists(os.path.join(GWAS_RES_loc, "GWASCatalog")): + # If it doesn't exist, create it + os.makedirs(os.path.join(GWAS_RES_loc, "GWASCatalog")) + +# GWAS Catalog directory +GWASCatalog_loc = os.path.join(GWAS_RES_loc, "GWASCatalog/") + +# # List the files in the GWASCatalog directory +# files = os.listdir(GWASCatalog_loc) +# print("Files in GWASCatalog directory:", files) + +print(check_output( + ["ls", os.path.join(GWASCatalog_loc)]).decode("utf8")) + +# general plotting directory +make_plots="YES" +PLOTTER=args.figures +# Check if the directory exists +if not (os.path.join(GWAS_RES_loc, "PLOTS")): + # If it doesn't exist, create it + os.makedirs(os.path.join(GWAS_RES_loc, "PLOTS")) +PLOTS_loc = os.path.join(GWAS_RES_loc, "PLOTS/") + +# regional association plots directory +REG_PLOTS_loc = PLOTS_loc + "/Regional_Association_Plots" + +# Check if the directory exists +if not os.path.exists(REG_PLOTS_loc): + # If it doesn't exist, create it + os.makedirs(REG_PLOTS_loc) + +# gwas_data_sumstats = gl.load_pickle( +# os.path.join( +# GWASCatalog_loc + PHENOTYPE + ".b37.gwaslab.pkl", +# ) +# ) + + + +### Load data +temp = pl.read_csv( + source=os.path.join( + GWAS_RES_loc + "/input/" + INPUT, + ), + has_header=True, + separator="\t", + ignore_errors=False, + # n_rows=1000, # for debugging + quote_char=None, + # necessary to fix issues with missing values when reading data + null_values=["NA"], + # There is an error at import (from temp to pandas()): + # Could not parse `X` as dtype `i64` at column 'CHR' (column number 2) + # https://stackoverflow.com/questions/75797640/how-to-specify-column-types-in-python-polars-read-csv + # https://stackoverflow.com/questions/71790235/switching-between-dtypes-within-a-dataframe + # https://pola-rs.github.io/polars/user-guide/concepts/data-types/ + dtypes={"CHR": pl.Utf8}, +) +# change polars dataframe to pandas dataframe +gwas_data = temp.to_pandas() +del temp + + +if make_plots == "YES": + # CAF plot + plt.figure() + sns.histplot( + data=gwas_data, + x="EAF", + bins=25, + kde=False, + stat="frequency", + color="#1290D9", + ) + plt.title("Histogram of Coded Allele Frequency") + plt.savefig( + os.path.join(PLOTS_loc, f"histogram.EAF.{PHENOTYPE}.png"), + dpi=300, + bbox_inches="tight", + format="png", + ) + plt.close() + + # BETA plot + plt.figure() + sns.histplot( + data=gwas_data, + x="Beta", + bins=25, + kde=False, + stat="frequency", + color="#E55738", + ) + plt.title("Histogram of Beta Fixed") + plt.savefig( + os.path.join(PLOTS_loc, f"histogram.effect.{PHENOTYPE}.png"), + dpi=300, + bbox_inches="tight", + format="png", + ) + plt.close() + + +### FIX COLUMNS +# Convert CHR column to string type to handle non-numeric chromosomes +gwas_data["CHR"] = gwas_data["CHR"].astype(str) + +# Optionally map non-numeric chromosomes to integers if required +chromosome_mapping = { + 'X': '23', + 'Y': '24', + 'MT': '25' +} + +gwas_data["CHR"] = gwas_data["CHR"].replace(chromosome_mapping) + +# Now you can convert it to an integer type +gwas_data["CHR"] = gwas_data["CHR"].astype("Int64") + + +gwas_data[["BP"]] = gwas_data[["BP"]].astype("Int64") + +# create new SNPID column based on chromosome, position, and alleles +# down the road we need an SNPID column to merge with the reference data and which does not contain 'ID' because this is not correctly interpreted by GWASLab +gwas_data["Marker"] = ( + gwas_data["CHR"].astype(str) + + ":" + + gwas_data["BP"].astype(str) + + ":" + + gwas_data["EffectAllele"].astype(str) + + ":" + + gwas_data["OtherAllele"].astype(str) +) + +gwas_data.rename(columns={"SNP": "VariantID"}, inplace=True) + +# Create CAVEAT column +gwas_data['CAVEAT'].fillna('None', inplace=True) + + + +### GWASLAB - create variable +# Specify the columns: +gwas_data_sumstats = gl.Sumstats( + gwas_data, + snpid="Marker", + # rsid="RSID", # not available + chrom="CHR", + pos="BP", + ea="EffectAllele", + nea="OtherAllele", + eaf="EAF", + beta="Beta", + se="SE", + p="P", + # direction="Direction", # only for meta-GWAS + n="N", + # info="Info", # not available + other=[ + "Imputed", + "CAVEAT", + "DF", + ], + build="19", + verbose=True, +) +# clean up +del gwas_data + + +# Execute `basic_check` function - first we just make sure the data has the expected format, columns, and datatypes. +# full data +gwas_data_sumstats.basic_check(verbose=True) + +# Remove duplicates +gwas_data_sumstats.remove_dup( + mode="md", # remove multi-allelic and duplicate variants + remove=False, # remove NAs + keep_col="P", + keep_ascend=True, + # keep the first variant, with the lowest p-value (sorted by that column) + keep="first", +) + + +# .check_ref(): Check if NEA is aligned with the reference sequence. After checking, the tracking status code will be changed accordingly. + +# full dataset +gwas_data_sumstats.check_ref( + ref_seq=REF_loc + "/gwaslab/hg19.fa", + # chr_dict=gl.get_number_to_NC(build="19") +) + +# we make sure to flip the alleles based on the status code +gwas_data_sumstats.flip_allele_stats() +# infer strand for palindromic SNPs/align indistinguishable indels +gwas_data_sumstats.infer_strand( + ref_infer=REF_loc + f"/gwaslab/{POPULATION}.ALL.split_norm_af.1kgp3v5.hg19.vcf.gz", + ref_alt_freq="AF", + n_cores=8 +) +gwas_data_sumstats.flip_allele_stats() + + +# Assign rsID by matching SNPID with CHR:POS:REF:ALT in the reference +gwas_data_sumstats.assign_rsid( + n_cores=8, + # this works for common variants + ref_rsid_tsv=REF_loc + "/gwaslab/1kg_dbsnp151_hg19_auto.txt.gz", + # ref_rsid_vcf=gl.get_path("dbsnp_v156_hg19"), + chr_dict=gl.get_number_to_NC( + build="19" + ), # this is needed as in the VCF file, the chromosome is in NC format +) + +# Check if SNPIDs are correct +gwas_data_sumstats.fix_id( + fixid=True, + forcefixid=True, + overwrite=True, +) + +gwas_data_sumstats.check_af( + ref_infer=REF_loc + f"/gwaslab/{POPULATION}.ALL.split_norm_af.1kgp3v5.hg19.vcf.gz", + ref_alt_freq="AF", + n_cores=8, +) +#plot allele frequency comparison plot against reference +gwas_data_sumstats.plot_daf(threshold=0.12) + + +# select caveats in dataset +temp = gwas_data_sumstats.data["CAVEAT"].value_counts() + +temp.to_csv( + os.path.join( + GWASCatalog_loc + PHENOTYPE + ".b37.counts_caveats.csv", + ) +) +del temp + +gl.dump_pickle( + gwas_data_sumstats, + os.path.join( + GWASCatalog_loc + PHENOTYPE + ".b37.gwaslab.pkl", + ), + overwrite=True, +) + +gwas_data_sumstats.log.show() + +gwas_data_sumstats.log.save( + os.path.join( + GWASCatalog_loc + PHENOTYPE + ".b37.gwaslab.log", + ) +) + +gwas_data_sumstats.to_format( + os.path.join(GWASCatalog_loc + PHENOTYPE + ".b37.gwaslab"), + fmt="ssf", + build="19", +) + + +# manhattan and qq plot +if make_plots == "YES": + # Manhattan and QQ plot + gwas_data_sumstats.plot_mqq( + skip=2, + cut=10, + sig_line=True, + sig_level=5e-8, + anno="GENENAME", + anno_style="right", + windowsizekb=500, + arm_offset=2, + repel_force=0.02, # default 0.01 + use_rank=True, + build="19", + stratified=True, + drop_chr_start=True, + title=f"{PHENOTYPE}", + save=os.path.join(PLOTS_loc, f"manhattan.500kb.300dpi.{PHENOTYPE}.png"), + saveargs={"dpi": 300}, + verbose=True, + ) + +# Perform Quality Control if required +# if perform_qc == "YES": +# gwas_data_sumstats_qc = gwas_data_sumstats.filter_value( +# '(EAF>=0.01 & EAF<0.99 & DF>=1) & (DAF<0.12 & DAF>-0.12) & (CAVEAT=="None")' +# ) + +# Perform Quality Control if required +if perform_qc == "YES": + gwas_data_sumstats_qc = gwas_data_sumstats.filter_value( + '(EAF>=0.01 & EAF<0.99) & (DAF<0.12 & DAF>-0.12) & (CAVEAT=="None")' + ) + + gl.dump_pickle( + gwas_data_sumstats_qc, + os.path.join(GWASCatalog_loc, f"{PHENOTYPE}.b37.gwaslab.qc.pkl"), + overwrite=True, + ) + + gwas_data_sumstats_qc.log.show() + + gwas_data_sumstats_qc.log.save( + os.path.join(GWASCatalog_loc, f"{PHENOTYPE}.b37.gwaslab.qc.log") + ) + + gwas_data_sumstats_qc.to_format( + os.path.join(GWASCatalog_loc, f"{PHENOTYPE}.b37.gwaslab.qc"), + fmt="ssf", + build="19", + ) + + # Generate plots if required + if make_plots == "YES": + gwas_data_sumstats_qc.plot_mqq( + skip=2, + cut=10, + sig_line=True, + sig_level=5e-8, + anno="GENENAME", + anno_style="right", + windowsizekb=500, + arm_offset=2, + repel_force=0.02, # default 0.01 + use_rank=True, + build="19", + stratified=True, + drop_chr_start=True, + save=os.path.join(PLOTS_loc, f"manhattan.500kb.300dpi.{PHENOTYPE}.qc.png"), + saveargs={"dpi": 300}, + verbose=True, + ) + +gwas_data_sumstats_leads = gwas_data_sumstats.get_lead(anno=True, windowsizekb=0, sig_level=5e-8, verbose=True, gls=True) + + +gwas_data_sumstats_leads.to_format( + os.path.join(GWASCatalog_loc + PHENOTYPE + ".b37.gwaslab.significant_snps"), + fmt="ssf", + build="19", +) \ No newline at end of file diff --git a/SCRIPTS/pipeline.parser.R b/SCRIPTS/pipeline.parser.R new file mode 100644 index 0000000..17d87dd --- /dev/null +++ b/SCRIPTS/pipeline.parser.R @@ -0,0 +1,718 @@ +#!/hpc/local/Rocky8/dhl_ec/software/R-3.6.3/bin/Rscript --vanilla + +### Mac OS X version +### #!/usr/local/bin/Rscript --vanilla + +### Linux version +### ##!/hpc/local/Rocky8/dhl_ec/software/R-3.6.3/bin/Rscript --vanilla + + +cat("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +GWAS Parser -- MetaGWASToolKit +\n +* Version: v1.3.0 +* Last edit: 2023-01-05 +* Created by: Sander W. van der Laan | s.w.vanderlaan@gmail.com +* Edits by: Emma J.A. Smulders +\n +* Description: Results parsing of GWAS summary statistics files used for a downstream meta-analysis of GWAS. +The script should be usuable on both any Linux distribution with R 3+ installed, Mac OS X and Windows. + +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n") + +### Usage: ./gwas.parser.R -p projectdir -d datagwas -o outputdir [OPTIONAL: -v verbose (DEFAULT) -q quiet] +### ./gwas.parser.R --projectdir projectdir --datagwas datagwas --outputdir outputdir [OPTIONAL: --verbose verbose (DEFAULT) -quiet quiet] + +cat("\n* Clearing the environment...\n\n") +### CLEAR THE BOARD +rm(list=ls()) + +cat("\n* Loading function to install packages...\n\n") +### Prerequisite: 'optparse'-library +### * Manual: http://cran.r-project.org/web/packages/optparse/optparse.pdf +### * Vignette: http://www.icesi.edu.co/CRAN/web/packages/optparse/vignettes/optparse.pdf + +### Don't say "Loading required package: optparse"... +###suppressPackageStartupMessages(require(optparse)) +###require(optparse) + +### The part of installing (and loading) packages via Rscript doesn't properly work. +### FUNCTION TO INSTALL PACKAGES +install.packages.auto <- function(x) { + x <- as.character(substitute(x)) + if(isTRUE(x %in% .packages(all.available = TRUE))) { + eval(parse(text = sprintf("require(\"%s\")", x))) + } else { + # Update installed packages - this may mean a full upgrade of R, which in turn + # may not be warrented. + #update.install.packages.auto(ask = FALSE) + eval(parse(text = sprintf("install.packages(\"%s\", dependencies = TRUE, repos = \"https://cloud.r-project.org/\")", x))) + } + if(isTRUE(x %in% .packages(all.available = TRUE))) { + eval(parse(text = sprintf("require(\"%s\")", x))) + } else { + if (!requireNamespace("BiocManager")) + install.packages("BiocManager") + BiocManager::install() # this would entail updating installed packages, which in turned may not be warrented + + # Code for older versions of R (<3.5.0) + # source("http://bioconductor.org/biocLite.R") + # Update installed packages - this may mean a full upgrade of R, which in turn + # may not be warrented. + # biocLite(character(), ask = FALSE) + eval(parse(text = sprintf("BiocManager::install(\"%s\")", x))) + eval(parse(text = sprintf("require(\"%s\")", x))) + } +} + +cat("\n* Checking availability of required packages and installing if needed...\n\n") +### INSTALL PACKAGES WE NEED +install.packages.auto("optparse") +install.packages.auto("tools") +install.packages.auto("dplyr") +install.packages.auto("tidyr") +install.packages.auto("data.table") +install.packages.auto("R.utils") + +cat("\nDone! Required packages installed and loaded.\n\n") + +cat("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n") + +### OPTION LISTING +option_list = list( + make_option(c("-p", "--projectdir"), action="store", default=NA, type='character', + help="Path to the project directory."), + make_option(c("-d", "--datagwas"), action="store", default=NA, type='character', + help="Path to the GWAS data, relative to the project directory; can be tab, comma, space or semicolon delimited, as well as gzipped."), + make_option(c("-o", "--outputdir"), action="store", default=NA, type='character', + help="Path to the output directory."), + make_option(c("-v", "--verbose"), action="store_true", default=TRUE, + help="Should the program print extra stuff out? [logical (FALSE or TRUE); default %default]"), + make_option(c("-s", "--silent"), action="store_false", dest="verbose", + help="Make the program not be verbose.") + #make_option(c("-c", "--cvar"), action="store", default="this is c", + # help="a variable named c, with a default [default %default]") +) +opt = parse_args(OptionParser(option_list=option_list)) + +#-------------------------------------------------------------------------- + +# ### FOR LOCAL DEBUGGING +# ### MacBook Pro +##MACDIR="/Users/swvanderlaan/PLINK/analyses" +# ### Mac Pro +# MACDIR="/Volumes/EliteProQx2Media/PLINK/analyses" +# +# opt$projectdir=paste0(MACDIR, "/meta_gwasfabp4") +# ### original +# #opt$datagwas=paste0(MACDIR, "/meta_gwasfabp4/DATA_UPLOAD_FREEZE/1000G/AEGS.WHOLE.FABP4.20150125.txt.gz") +# #opt$datagwas=paste0(MACDIR, "/meta_gwasfabp4/DATA_UPLOAD_FREEZE/1000G/EPICNL.WHOLE.FABP4.20160629.txt.gz") +# opt$datagwas=paste0(MACDIR, "/meta_gwasfabp4/DATA_UPLOAD_FREEZE/1000G/SORBS.WHOLE.FABP4.20141117.edit.txt.gz") +# #opt$datagwas=paste0(MACDIR, "/meta_gwasfabp4/DATA_UPLOAD_FREEZE/1000G/CHS.FABP4.20140827.txt.gz") +# +# opt$outputdir="test_environment" +# +# ### FOR LOCAL DEBUGGING + +#-------------------------------------------------------------------------- + +if (opt$verbose) { + ### You can use either the long or short name; so opt$a and opt$avar are the same. + ### Show the user what the variables are. + cat("\n++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n") + cat("* Checking the settings as given through the flags.") + cat("\n - The project directory....................: ") + cat(opt$projectdir) + cat("\n - The GWAS data ...........................: ") + cat(opt$datagwas) + cat("\n - The output directory.....................: ") + cat(opt$outputdir) + cat("\n\n") +} +cat("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n") +cat("Starting \"GWAS Parser\".") + +### START OF THE PROGRAM +### main point of program is here, do this whether or not "verbose" is set +if(!is.na(opt$projectdir) & !is.na(opt$datagwas) & !is.na(opt$outputdir)) { + cat(paste("\n\nWe are going to parse the GWAS data, by parsing and doing some initial quality control of the data. + \nAnalysing these results...............: '",basename(opt$datagwas),"' + Parsed results will be saved here.....: '", opt$outputdir, "'.\n",sep='')) + + ### GENERAL SETUP + Today=format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d") + cat(paste("\nToday's date is: ", Today, ".\n", sep = '')) + + #### DEFINE THE LOCATIONS OF DATA + ROOT_loc = opt$projectdir # argument 1 + + cat("\nChecking existence of output directory and creating it if necessary.\n") + OUT_loc = opt$outputdir # argument 3 + + if (file.exists(paste(ROOT_loc, OUT_loc, "/", sep = "/", collapse = "/"))) { + cat(paste0("* '", OUT_loc,"' exists in '", ROOT_loc, "' and is a directory...")) + } else if (file.exists(paste(ROOT_loc, OUT_loc, sep = "/", collapse = "/"))) { + cat(paste0("* '", OUT_loc,"' exists in '", ROOT_loc, "' but is a file...")) + # you will probably want to handle this separately + } else { + cat(paste0("* '", OUT_loc,"' does not exist in '", ROOT_loc, "' - creating it now...")) + dir.create(file.path(ROOT_loc, OUT_loc)) + } + + if (file.exists(paste(ROOT_loc, OUT_loc, "/", sep = "/", collapse = "/"))) { + # By this point, the directory either existed or has been successfully created + setwd(file.path(ROOT_loc, OUT_loc)) + } else { + cat(paste0("*** ERROR *** '", OUT_loc,"' does not exist - you likely have a 'rights' issue...")) + # Handle this error as appropriate + } + + cat("\n++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n") + + ### LOADING GWAS RESULTS FILES + cat("\nLoading GWAS data.\n") + ### Location of is set by 'opt$datagwas' # argument 2 + ### Checking file type -- is it gzipped or not? + #projectdir <- normalizePath(opt$projectdir, mustWork = FALSE) + + # Join the projectdir and datagwas with the appropriate file separator + #datagwas_path <- file.path(opt$projectdir, opt$datagwas) + #datagwas_connection <- file(opt$datagwas) + datagwas_connection <- file(paste0(opt$projectdir, "/", opt$datagwas)) + filetype <- summary(datagwas_connection)$class + TESTDELIMITER <- readLines(datagwas_connection, n = 1) + close(datagwas_connection) + if(filetype == "gzfile"){ + cat("\n* The file appears to be gzipped, checking delimiter now...") + cat("\n* Data header looks like this:\n") + print(TESTDELIMITER) + if(grepl(",", TESTDELIMITER) == TRUE){ + cat("\n* Data is comma-seperated, loading...\n") + GWASDATA_RAW = fread(paste0(opt$projectdir, "/", opt$datagwas), header = TRUE, sep = ",", + dec = ".", na.strings = c("", "NA", "na", "Na", + "NaN", "Nan", ".", + "N/A","n/a", "N/a"), + blank.lines.skip = TRUE) + + } else if(grepl(";", TESTDELIMITER) == TRUE) { + cat("\n\n* Data is semicolon-seperated, loading...\n") + GWASDATA_RAW = fread(paste0(opt$projectdir, "/", opt$datagwas), header = TRUE, sep = ";", + dec = ".", na.strings = c("", "NA", "na", "Na", + "NaN", "Nan", ".", + "N/A","n/a", "N/a"), + blank.lines.skip = TRUE) + + } else if(grepl("\\t", TESTDELIMITER) == TRUE) { + cat("\n\n* Data is tab-seperated, loading...\n") + GWASDATA_RAW = fread(paste0(opt$projectdir, "/", opt$datagwas), header = TRUE, sep ="\t", + dec = ".", na.strings = c("", "NA", "na", "Na", + "NaN", "Nan", ".", + "N/A","n/a", "N/a"), + blank.lines.skip = TRUE) + + } else if(grepl("\\s", TESTDELIMITER) == TRUE) { + cat("\n\n* Data is space-seperated, loading...\n") + GWASDATA_RAW = fread(paste0(opt$projectdir, "/", opt$datagwas), header = TRUE, sep =" ", + dec = ".", na.strings = c("", "NA", "na", "Na", + "NaN", "Nan", ".", + "N/A","n/a", "N/a"), + blank.lines.skip = TRUE) + + } else if(grepl("[:blank:]", TESTDELIMITER) == TRUE) { + cat("\n\n* Data is blankspace-seperated, loading...\n") + GWASDATA_RAW = fread(paste0(opt$projectdir, "/", opt$datagwas), header = TRUE, sep =" ", + dec = ".", na.strings = c("", "NA", "na", "Na", + "NaN", "Nan", ".", + "N/A","n/a", "N/a"), + blank.lines.skip = TRUE) + + } else { + cat ("\n\n*** ERROR *** Something is rotten in the City of Gotham. The GWAS data is neither comma, + tab, space, nor semicolon delimited. Double back, please.\n\n", + file=stderr()) # print error messages to stder + } + } else if(filetype != "gzfile") { + cat("\n* The file appears not to be gzipped, checking delimiter now...") + cat("\n* Data header looks like this:\n") + print(TESTDELIMITER) + if(grepl(",", TESTDELIMITER) == TRUE){ + cat("\n* Data is comma-seperated, loading...\n") + GWASDATA_RAW = fread(paste0(opt$projectdir, "/", opt$datagwas), header = TRUE, sep = ",", + dec = ".", na.strings = c("", "NA", "na", "Na", + "NaN", "Nan", ".", + "N/A","n/a", "N/a"), + blank.lines.skip = TRUE) + + } else if(grepl(";", TESTDELIMITER) == TRUE) { + cat("\n\n* Data is semicolon-seperated, loading...\n") + GWASDATA_RAW = fread(paste0(opt$projectdir, "/", opt$datagwas), header = TRUE, sep = ";", + dec = ".", na.strings = c("", "NA", "na", "Na", + "NaN", "Nan", ".", + "N/A","n/a", "N/a"), + blank.lines.skip = TRUE) + + } else if(grepl("\\t", TESTDELIMITER) == TRUE) { + cat("\n\n* Data is tab-seperated, loading...\n") + GWASDATA_RAW = fread(paste0(opt$projectdir, "/", opt$datagwas), header = TRUE, sep ="\t", + dec = ".", na.strings = c("", "NA", "na", "Na", + "NaN", "Nan", ".", + "N/A","n/a", "N/a"), + blank.lines.skip = TRUE) + + } else if(grepl("\\s", TESTDELIMITER) == TRUE) { + cat("\n\n* Data is space-seperated, loading...\n") + GWASDATA_RAW = fread(paste0(opt$projectdir, "/", opt$datagwas), header = TRUE, sep =" ", + dec = ".", na.strings = c("", "NA", "na", "Na", + "NaN", "Nan", ".", + "N/A","n/a", "N/a"), + blank.lines.skip = TRUE) + + } else if(grepl("[:blank:]", TESTDELIMITER) == TRUE) { + cat("\n\n* Data is blankspace-seperated, loading...\n") + GWASDATA_RAW = fread(paste0(opt$projectdir, "/", opt$datagwas), header = TRUE, sep =" ", + dec = ".", na.strings = c("", "NA", "na", "Na", + "NaN", "Nan", ".", + "N/A","n/a", "N/a"), + blank.lines.skip = TRUE) + + } else { + cat ("\n\n*** ERROR *** Something is rotten in the City of Gotham. The GWAS data is neither comma, + tab, space, nor semicolon delimited. Double back, please.\n\n", + file=stderr()) # print error messages to stder + } + } else { + cat ("\n\n*** ERROR *** Something is rotten in the City of Gotham. We can't determine the file type + of the GWAS data. Double back, please.\n\n", + file=stderr()) # print error messages to stder + } + + ### Change column names case to all 'lower cases' + names(GWASDATA_RAW) <- tolower(names(GWASDATA_RAW))# +# print(head(GWASDATA_RAW)) + ### Selecting the columns we want + cat("\n* Selecting required columns, and creating them if not present...") + + VectorOfColumnsWeWant <- c("^marker$", "^snp$", "^rsid$", "^snpid$", "^id$", # variant ID + "^chr$", "^chrom$", "^chromosome$", # chromosome + "^position$", "^bp$", "^genpos$","^pos$", # Variant position on chromosomes + "^effect[_]allele$", "^minor[_]allele$", "^risk[_]allele$", "^coded[_]allele$", # Effect/Minor allele + "^effectallele$", "^minorallele$", "^riskallele$", "^codedallele$", "^allele1$", # Effect/Minor allele + "^other[_]allele$", "^major[_]allele$", "^non[_]effect[_]allele$", "^non[_]coded[_]allele$", # Other/Major allele + "^otherallele$", "^majorallele$", "^noneffectallele$", "^noncodedallele$", "^noncoded_allele$", "^allele0$", # Other/Major allele + "^strand$", # chromosomal strand + "^beta$", "^effect[_]size$", "^effectsize$", "^beta_fixed$", # effect size estimates, should be beta + "^se.$", "^se$", "^se_fixed$", # Standard error + "^p.value$", "^p$", "^p.val$", "^pvalue$", "^pval$", "^p_bolt_lmm_inf$", "^p_fixed$", # p-value + "^[remc]af$", "^a1freq$", "^coded_allele_freq$", # effect/minor allele frequency + "^hwe.value$", "^hwe$", "^hwe.val$", # Hardy-Weinberg Equilibrium p-value + "^n$", "^samplesize$", "^n_eff$", # Number of samples analysed + "^n[_]case.$", "^ncase$", "^n[_]control.$","^ncontrol$", "^n_cntrl.$", # Number of samples analysed in GWAS in case-control set up + "^imputed$", # Imputed or genotyped? + "^info$", "^qual[_]score$", # Imputation quality + "^df$", #degrees of freedom + "^caveat$") #caveat + matchExpression <- paste(VectorOfColumnsWeWant, collapse = "|") + # print(head(GWASDATA_RAW)) + str(GWASDATA_RAW) +# + GWASDATA_RAWSELECTION <- GWASDATA_RAW %>% select(matches(matchExpression, ignore.case = TRUE)) +# + # print(names(GWASDATA_RAWSELECTION)) + + cat("\n* Renaming columns where necessary...") + ### Rename columns + ### - variant column will become "Marker" + ### - chromosome & bp columns will become "CHR" and "BP" + ### - if MAF/minor/major available, thus effect size must be relative to minor, so: + ### - MAF = CAF = RAF = EAF -- will be coded as "MAF" + ### - minor = coded = effect = risk -- will be coded as "MinorAllele" + ### - major = noncoded = noneffect = nonrisk = other -- will be coded as "MajorAllele" + ### - if MAF/[coded/effect/risk]/[noncoded/noneffect/nonrisk/other], thus the effect + ### size must be relative to [coded/effect/risk], so: + ### - MAF = CAF = RAF = EAF -- will be coded as "MAF" + ### - coded = effect = risk -- will be coded as "[Coded/Effect/Risk]Allele" + ### - noncoded = noneffect = nonrisk = other -- will be coded as "OtherAllele" + ### Set these three accordingly, other wise set these to CAF/coded/other + ### +# + ### Rename columns -- strand + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, Strand = matches("^strand$"), everything()) + + ### Rename columns -- imputation + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, Info = matches("^info$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, Info = matches("^qual[_]score$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, Imputed = matches("^imputed$"), everything()) + + ### Rename columns -- n cases and controls + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, N_controls = matches("^n_control.$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, N_controls = matches("^ncontrol$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, N_controls = matches("^n_ctrl.$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, N_cases = matches("^n_case.$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, N_cases = matches("^ncase$"), everything()) + + + + ### Rename columns -- sample size + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, N = matches("^n$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, N = matches("^samplesize$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, N = matches("^n_eff$"), everything()) + + ### Rename columns -- HWE p-value + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, HWE_P = matches("^hwe.value$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, HWE_P = matches("^hwe$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, HWE_P = matches("^hwe.val$"), everything()) + + ### Rename columns -- p-value + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, P = matches("^p.value$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, P = matches("^p$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, P = matches("^p.val$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, P = matches("^pvalue$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, P = matches("^pval$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, P = matches("^p_bolt_lmm_inf$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, P = matches("^p_fixed$"), everything()) + + ### Rename columns -- standard error + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, SE = matches("^se.$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, SE = matches("^se$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, SE = matches("^se_fixed$"), everything()) + + + ### Rename columns -- beta/effect size + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, Beta = matches("^beta$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, Beta = matches("^beta_fixed$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, Beta = matches("^effect[_]size$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, Beta = matches("^effectsize$"), everything()) + + ### Rename columns -- allele frequency + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, RAF = matches("^raf$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, EAF = matches("^eaf$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, MAF = matches("^maf$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, EAF = matches("^caf$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, EAF = matches("^a1freq$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, EAF = matches("^coded_allele_freq$"), everything()) + + ### Rename columns -- non effect allele + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, OtherAllele = matches("^non[_]effect[_]allele$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, OtherAllele = matches("^noneffectallele$"), everything()) + + ### Rename columns -- other allele + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, OtherAllele = matches("^other[_]allele$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, OtherAllele = matches("^otherallele$"), everything()) + + ### Rename columns -- non coded allele + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, OtherAllele = matches("^non[_]coded[_]allele$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, OtherAllele = matches("^noncodedallele$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, OtherAllele = matches("^noncoded_allele$"), everything()) + + ### Rename columns -- other allele (allele0) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, OtherAllele = matches("^allele0$"), everything()) + + ### Rename columns -- major allele + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, MajorAllele = matches("^major[_]allele$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, MajorAllele = matches("^majorallele$"), everything()) + + #### Rename columns -- coded allele + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, EffectAllele = matches("^coded[_]allele$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, EffectAllele = matches("^codedallele$"), everything()) + + ### Rename columns -- effect allele + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, EffectAllele = matches("^effect[_]allele$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, EffectAllele = matches("^effectallele$"), everything()) + + ### Rename columns -- risk allele + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, RiskAllele = matches("^risk[_]allele$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, RiskAllele = matches("^riskallele$"), everything()) + + ### Rename columns -- Effect allele (allele1) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, EffectAllele = matches("^allele1$"), everything()) + + ### Rename columns -- minor allele + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, MinorAllele = matches("^minor[_]allele$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, MinorAllele = matches("^minorallele$"), everything()) + + ### Rename columns -- base pair position + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, BP = matches("^position$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, BP = matches("^bp$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, BP = matches("^pos$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, BP = matches("^genpos$"), everything()) + + ### Rename columns -- chromosome + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, CHR = matches("^chr$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, CHR = matches("^chrom$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, CHR = matches("^chromosome$"), everything()) + + ### Rename columns -- marker name + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, MarkerOriginal = matches("^marker$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, MarkerOriginal = matches("^snp$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, MarkerOriginal = matches("^rsid$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, MarkerOriginal = matches("^snpid$"), everything()) + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, MarkerOriginal = matches("^id$"), everything()) + + + ### Rename columns -- caveats + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, DF = matches("^df$"), everything()) + + GWASDATA_RAWSELECTION <- select(GWASDATA_RAWSELECTION, CAVEAT = matches("^caveat$"), everything()) + + ### Rename columns -- removing leading 'zeros' + cat("\n* Removing leading 'zeros' from chromosome number...") + GWASDATA_RAWSELECTION$CHR <- gsub("(? 23 + ### Y Y chromosome <-> 24 + ### XY Pseudo-autosomal region of X <-> 25 + ### MT Mitochondrial <-> 26 + + ### Rename chromosomes + GWASDATA_RAWSELECTION$CHR[GWASDATA_RAWSELECTION$CHR == "x" | GWASDATA_RAWSELECTION$CHR == "23"] <- "X" + GWASDATA_RAWSELECTION$CHR[GWASDATA_RAWSELECTION$CHR == "y" | GWASDATA_RAWSELECTION$CHR == "24"] <- "Y" + GWASDATA_RAWSELECTION$CHR[GWASDATA_RAWSELECTION$CHR == "xY" | GWASDATA_RAWSELECTION$CHR == "Xy" | + GWASDATA_RAWSELECTION$CHR == "xy" | GWASDATA_RAWSELECTION$CHR == "xy"] <- "XY" + GWASDATA_RAWSELECTION$CHR[GWASDATA_RAWSELECTION$CHR == "Mt" | GWASDATA_RAWSELECTION$CHR == "mT" | + GWASDATA_RAWSELECTION$CHR == "mt"] <- "MT" + + #print(tail(GWASDATA_RAWSELECTION)) + + # Manually calculate the N + # print(colnames(GWASDATA_RAWSELECTION)) + if(!("N" %in% colnames(GWASDATA_RAWSELECTION))) { + if("N_cases" %in% colnames(GWASDATA_RAWSELECTION)){ + if("N_controls" %in% colnames(GWASDATA_RAWSELECTION)){ + print("Manually calculated N from n cases and controls!") + GWASDATA_RAWSELECTION$N <- (GWASDATA_RAWSELECTION$N_cases+GWASDATA_RAWSELECTION$N_controls) + } + } + } + + ### set 'chromosome' and 'bp' column to integer + # GWASDATA_RAWSELECTION <- mutate(GWASDATA_RAWSELECTION, CHR = as.integer(CHR)) # convert to numeric + GWASDATA_RAWSELECTION <- mutate(GWASDATA_RAWSELECTION, BP = as.integer(BP)) # convert to numeric + + #print(head(GWASDATA_RAWSELECTION)) + + ### Calculating general statistics if not available + cat("\n* Calculating 'allele frequencies'...") + ### calculate MAF -- *only* if MAF/minor allele/major allele *not* present + ### the effect size must be relative to the effect/coded allele and EAF + ### calculate EAF -- *only* if MAF/minor allele/major allele *is* present - + ### if they are, the effect size must be relative to the minor + + if("MAF" %in% colnames(GWASDATA_RAWSELECTION)) { + cat("\n* Minor allele frequency is present, checking for minor/major allele...") + + if("MinorAllele" %in% colnames(GWASDATA_RAWSELECTION)) { + cat("\n- minor allele is present, checking for major allele...") + + if("MajorAllele" %in% colnames(GWASDATA_RAWSELECTION)) { + cat("\n- minor/major allele is also present, setting effect/other allele, + and calculating effect allele frequency...") # we will only set the effect/other alleles here + GWASDATA_RAWSELECTION$EAF <- GWASDATA_RAWSELECTION$MAF + GWASDATA_RAWSELECTION$EffectAllele <- GWASDATA_RAWSELECTION$MinorAllele + GWASDATA_RAWSELECTION$OtherAllele <- GWASDATA_RAWSELECTION$MajorAllele + GWASDATA_RAWSELECTION$BetaMinor <- GWASDATA_RAWSELECTION$Beta + + } else { + cat("\n\n*** ERROR *** Something is rotten in the City of Gotham. If there's a 'minor allele', + a 'major allele' must be present as well.", file=stderr()) # print error messages to stder + } } } else if("OtherAllele" %in% colnames(GWASDATA_RAWSELECTION)) { + cat("\n* Other alleles are present, calculating minor allele frequency...") # we only care for MAF + + if("EAF" %in% colnames(GWASDATA_RAWSELECTION)) { + cat("\n- calculating 'MAF' using 'effect allele frequency'...") + GWASDATA_RAWSELECTION$MAF <- ifelse(GWASDATA_RAWSELECTION$EAF < 0.50, + GWASDATA_RAWSELECTION$EAF, 1-GWASDATA_RAWSELECTION$EAF) + GWASDATA_RAWSELECTION$MinorAllele <- ifelse(GWASDATA_RAWSELECTION$EAF < 0.50, + GWASDATA_RAWSELECTION$EffectAllele, GWASDATA_RAWSELECTION$OtherAllele) + GWASDATA_RAWSELECTION$MajorAllele <- ifelse(GWASDATA_RAWSELECTION$EAF > 0.50, + GWASDATA_RAWSELECTION$EffectAllele, GWASDATA_RAWSELECTION$OtherAllele) + GWASDATA_RAWSELECTION$BetaMinor <- ifelse(GWASDATA_RAWSELECTION$EAF < 0.50, + GWASDATA_RAWSELECTION$Beta, -1*GWASDATA_RAWSELECTION$Beta) + + } else if("RAF" %in% colnames(GWASDATA_RAWSELECTION)) { + cat("\n- calculating 'MAF' using 'risk allele frequency'...") + GWASDATA_RAWSELECTION$MAF <- ifelse(GWASDATA_RAWSELECTION$RAF < 0.50, + GWASDATA_RAWSELECTION$RAF, 1-GWASDATA_RAWSELECTION$RAF) + GWASDATA_RAWSELECTION$MinorAllele <- ifelse(GWASDATA_RAWSELECTION$RAF < 0.50, + GWASDATA_RAWSELECTION$RiskAllele, GWASDATA_RAWSELECTION$OtherAllele) + GWASDATA_RAWSELECTION$MajorAllele <- ifelse(GWASDATA_RAWSELECTION$RAF > 0.50, + GWASDATA_RAWSELECTION$RiskAllele, GWASDATA_RAWSELECTION$OtherAllele) + GWASDATA_RAWSELECTION$BetaMinor <- ifelse(GWASDATA_RAWSELECTION$RAF < 0.50, + GWASDATA_RAWSELECTION$Beta, -1*GWASDATA_RAWSELECTION$Beta) + colnames(GWASDATA_RAWSELECTION)[colnames(GWASDATA_RAWSELECTION) == "RAF"] <- "EAF" + + } else if("CAF" %in% colnames(GWASDATA_RAWSELECTION)) { + cat("\n- calculating 'MAF' using 'coded allele frequency'...") + GWASDATA_RAWSELECTION$MAF <- ifelse(GWASDATA_RAWSELECTION$CAF < 0.50, + GWASDATA_RAWSELECTION$CAF, 1-GWASDATA_RAWSELECTION$CAF) + GWASDATA_RAWSELECTION$MinorAllele <- ifelse(GWASDATA_RAWSELECTION$CAF < 0.50, + GWASDATA_RAWSELECTION$CodedAllele, GWASDATA_RAWSELECTION$OtherAllele) + GWASDATA_RAWSELECTION$MajorAllele <- ifelse(GWASDATA_RAWSELECTION$CAF > 0.50, + GWASDATA_RAWSELECTION$CodedAllele, GWASDATA_RAWSELECTION$OtherAllele) + GWASDATA_RAWSELECTION$BetaMinor <- ifelse(GWASDATA_RAWSELECTION$CAF < 0.50, + GWASDATA_RAWSELECTION$Beta, -1*GWASDATA_RAWSELECTION$Beta) + colnames(GWASDATA_RAWSELECTION)[colnames(GWASDATA_RAWSELECTION) == "CAF"] <- "EAF" + + } else { + cat("\n\n*** ERROR *** Something is rotten in the City of Gotham. 'MAF', EAF', 'RAF', nor 'CAF' is present. Double back, please.", file=stderr()) # print error messages to stder + } + + } else { + cat("\n\n*** ERROR *** Something is rotten in the City of Gotham. There's something wrong with the allele frequencies. Double back, please.", file=stderr()) # print error messages to stder + + } + + # print(head(GWASDATA_RAWSELECTION)) + # +# ### Calculate MAC +# cat("\n* Calculating 'minor allele count' (MAC)...") +# GWASDATA_RAWSELECTION$MAC <- (GWASDATA_RAWSELECTION$MAF*GWASDATA_RAWSELECTION$N*2) + + # print(head(GWASDATA_RAWSELECTION)) + + cat("\n* Creating the final parsed dataset.") + + cat("\n- making empty dataframe...") + col.Classes = c("character", "character", "integer", "integer", "character", + "character", "character", "character", "character", + "numeric", "numeric", "numeric", "numeric", "numeric", + "numeric", "numeric", "numeric", "numeric", + "integer", "integer", "integer", + "character", "character", "numeric") + col.Names = c("Marker", "MarkerOriginal", "CHR", "BP", "Strand", + "EffectAllele", "OtherAllele", "MinorAllele", "MajorAllele", + "EAF", "MAF", "MAC", "HWE_P", "Info", + "Beta", "BetaMinor", "SE", "P", + "N", "N_cases", "N_controls", + "Imputed", "CAVEAT", "DF") + num_rows = length(GWASDATA_RAWSELECTION$MarkerOriginal) + num_cols = length(col.Names) +# + ### Function to create empty table + create_empty_table <- function(num_rows, num_cols) { + GWASDATA_PARSED <- data.frame(matrix(NA, nrow = num_rows, ncol = num_cols)) + + return(GWASDATA_PARSED) + } + + GWASDATA_PARSED <- create_empty_table(num_rows, num_cols) + colnames(GWASDATA_PARSED) <- col.Names + + cat("\n- adding data to dataframe...") + cat("\n > adding the new markers; these will have the form [ chr<#>:<#>:MinorAllele_MajorAllele] ...\n") + GWASDATA_PARSED$Marker <- as.character(paste("chr",GWASDATA_RAWSELECTION$CHR,":", + GWASDATA_RAWSELECTION$BP,":", + GWASDATA_RAWSELECTION$MinorAllele,"_", + GWASDATA_RAWSELECTION$MajorAllele, + sep = "")) +# + cat("\n > adding the original markers...") + GWASDATA_PARSED$MarkerOriginal <- GWASDATA_RAWSELECTION$MarkerOriginal + + # print(head(GWASDATA_RAWSELECTION)) + # print(head(GWASDATA_PARSED)) + + cat("\n > changing NA to '0' for Chr...") + GWASDATA_PARSED$CHR <- GWASDATA_RAWSELECTION$CHR + GWASDATA_PARSED <- GWASDATA_PARSED %>% mutate(CHR = ifelse(is.na(CHR),0,CHR)) # unknown chromosomes are set to '0' + + cat("\n > changing NA to '0' for BP...") + GWASDATA_PARSED$BP <- GWASDATA_RAWSELECTION$BP + GWASDATA_PARSED <- GWASDATA_PARSED %>% mutate(BP = ifelse(is.na(BP),0,BP))# unknown base pair positions are set to '0' +# + cat("\n > adding strand information...") + GWASDATA_PARSED$Strand <- ifelse(("Strand" %in% colnames(GWASDATA_RAWSELECTION)) == TRUE, + GWASDATA_RAWSELECTION$Strand, "+") # we always assume that the +-strand was used + + cat("\n > adding alleles...") + GWASDATA_PARSED$EffectAllele <- ifelse(GWASDATA_RAWSELECTION$EffectAllele != "NA", GWASDATA_RAWSELECTION$EffectAllele, "NA") + GWASDATA_PARSED$OtherAllele <- ifelse(GWASDATA_RAWSELECTION$OtherAllele != "NA", GWASDATA_RAWSELECTION$OtherAllele, "NA") + GWASDATA_PARSED$MinorAllele <- ifelse(GWASDATA_RAWSELECTION$MinorAllele != "NA", GWASDATA_RAWSELECTION$MinorAllele, "NA") + GWASDATA_PARSED$MajorAllele <- ifelse(GWASDATA_RAWSELECTION$MajorAllele != "NA", GWASDATA_RAWSELECTION$MajorAllele, "NA") + + cat("\n > adding allele statistics...") + GWASDATA_PARSED$EAF <- ifelse(GWASDATA_RAWSELECTION$EAF != "NA", GWASDATA_RAWSELECTION$EAF, "NA") + GWASDATA_PARSED$MAF <- ifelse(GWASDATA_RAWSELECTION$MAF != "NA", GWASDATA_RAWSELECTION$MAF, "NA") + # GWASDATA_PARSED$MAC <- ifelse(GWASDATA_RAWSELECTION$MAC != "NA", GWASDATA_RAWSELECTION$MAC, "NA") + + if(("HWE_P" %in% colnames(GWASDATA_RAWSELECTION)) == TRUE){ + GWASDATA_PARSED$HWE_P <- ifelse(GWASDATA_RAWSELECTION$HWE_P != "NA", GWASDATA_RAWSELECTION$HWE_P, "1") # this is not always present, hence we set it at "1" + } else { + GWASDATA_PARSED$HWE_P <- "1" # this is not always present, hence we set it at "1" + } + + if(("Info" %in% colnames(GWASDATA_RAWSELECTION)) == TRUE){ + GWASDATA_PARSED$Info <- ifelse(GWASDATA_RAWSELECTION$Info != "NA", GWASDATA_RAWSELECTION$Info, "1") # in case "NA" we set it to 1 + } else { + GWASDATA_PARSED$Info <- "1" # in case of genotyped data + } + + cat("\n > adding test statistics...") + GWASDATA_PARSED$Beta <- ifelse(GWASDATA_RAWSELECTION$Beta != "NA", GWASDATA_RAWSELECTION$Beta, "NA") # Note that beta is relative to the EffectAllele + GWASDATA_PARSED$BetaMinor <- ifelse(GWASDATA_RAWSELECTION$BetaMinor != "NA", GWASDATA_RAWSELECTION$BetaMinor, "NA") # Note that betaminor is relative to the MinorAllele + GWASDATA_PARSED$SE <- ifelse(GWASDATA_RAWSELECTION$SE != "NA", GWASDATA_RAWSELECTION$SE, "NA") + GWASDATA_PARSED$P <- ifelse(GWASDATA_RAWSELECTION$P != "NA", GWASDATA_RAWSELECTION$P, "NA") + + cat("\n > adding sample information statistics...") + GWASDATA_PARSED$N <- ifelse(GWASDATA_RAWSELECTION$N != "NA", GWASDATA_RAWSELECTION$N, "NA") + + if(("N_cases" %in% colnames(GWASDATA_RAWSELECTION)) == TRUE){ + GWASDATA_PARSED$N_cases <- ifelse(GWASDATA_RAWSELECTION$N_cases != "NA", GWASDATA_RAWSELECTION$N_cases, "NA") + } else { + GWASDATA_PARSED$N_cases <- "NA" # in case of quantitative trait analyses + } + + if(("N_controls" %in% colnames(GWASDATA_RAWSELECTION)) == TRUE){ + GWASDATA_PARSED$N_controls <- ifelse(GWASDATA_RAWSELECTION$N_controls != "NA", GWASDATA_RAWSELECTION$N_controls, "NA") + } else { + GWASDATA_PARSED$N_controls <- "NA" # in case of quantitative trait analyses + } + + if(("Imputed" %in% colnames(GWASDATA_RAWSELECTION)) == TRUE){ + GWASDATA_PARSED$Imputed <- ifelse(GWASDATA_RAWSELECTION$Imputed != "NA", GWASDATA_RAWSELECTION$Imputed, "2") + } else { + GWASDATA_PARSED$Imputed <- "2" # 2 = no information, 1 = imputed, 0 = genotyped + } + if(("DF" %in% colnames(GWASDATA_RAWSELECTION)) == TRUE){ + GWASDATA_PARSED$DF <- ifelse(GWASDATA_RAWSELECTION$DF != "NA", GWASDATA_RAWSELECTION$DF, "NA") + } else { + GWASDATA_PARSED$DF <- "NA" + } + if(("CAVEAT" %in% colnames(GWASDATA_RAWSELECTION)) == TRUE){ + GWASDATA_PARSED$CAVEAT <- ifelse(GWASDATA_RAWSELECTION$CAVEAT != "NA", GWASDATA_RAWSELECTION$CAVEAT, "NA") + } else { + GWASDATA_PARSED$CAVEAT <- "NA" + } + cat("\n* All done creating the final parsed dataset.") + ### SAVE NEW DATA ### + cat("\n\nSaving parsed data...\n") + fwrite(GWASDATA_PARSED, + paste0(ROOT_loc, "/", OUT_loc, "/", + basename(opt$datagwas) + ), + quote = FALSE , row.names = FALSE, col.names = TRUE, + sep = "\t", na = "NA", dec = ".") + + ### CLOSING MESSAGE + cat(paste("\nAll done parsing [",file_path_sans_ext(basename(opt$datagwas), compression = TRUE),"].\n")) + cat(paste("\nToday's date is: ", Today, ".\n", sep = '')) + + } else { + cat("\n\n\n\n++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n") + cat("\n*** ERROR *** You didn't specify all variables:\n + - --p/projectdir : Path to the project directory. + - --d/datagwas : Path to the GWAS data, relative to the project directory; + can be tab, comma, space or semicolon delimited, as well as gzipped. + - --o/outputdir : Path to output directory.", + file=stderr()) # print error messages to stderr + } +# +# cat("\n++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n") +# +# ### SAVE ENVIRONMENT | FOR DEBUGGING +# save.image(paste0(ROOT_loc, "/", OUT_loc, "/", Today,"_", basename(opt$datagwas),"_DEBUG_GWAS_PARSER.RData")) diff --git a/SCRIPTS/polyfun/LICENSE b/SCRIPTS/polyfun/LICENSE new file mode 100644 index 0000000..baa885d --- /dev/null +++ b/SCRIPTS/polyfun/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2019 Omer Weissbrod + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/SCRIPTS/polyfun/README.md b/SCRIPTS/polyfun/README.md new file mode 100644 index 0000000..c026872 --- /dev/null +++ b/SCRIPTS/polyfun/README.md @@ -0,0 +1,145 @@ +# PolyFun, PolyLoc and PolyPred +**PolyFun** (POLYgenic FUNctionally-informed fine-mapping) +
+**PolyLoc** (POLYgenic LOCalization of complex trait heritability) +
+**PolyPred** (POLYgenic Prediction of complex traits) + +[![CI](https://github.com/omerwe/polyfun/actions/workflows/ci.yml/badge.svg)](https://github.com/omerwe/polyfun/actions/workflows/ci.yml) + +This repo contains the code of the methods **PolyFun** for functionally-informed fine-mapping, **PolyLoc** for polygenic localization of complex trait heritability, and **PolyPred** for complex trait prediction. **PolyFun** and **PolyLoc** are described in [Weissbrod et al. 2020 Nat Genet](https://www.nature.com/articles/s41588-020-00735-5). **PolyPred** is described in [Weissbrod*, Kanai*, Shi* et al. 2022 Nat Genet](https://www.nature.com/articles/s41588-022-01036-9). +

+**PolyFun** estimates prior causal probabilities for SNPs, which can then be used by fine-mapping methods like [SuSiE](https://github.com/stephenslab/susieR) or [FINEMAP](http://www.christianbenner.com/). Unlike previous methods for functionally-informed fine-mapping, **PolyFun** can aggregate polygenic data from across the entire genome and hundreds of functional annotations. +

+**PolyLoc** generalizes fine-mapping by constructing minimal sets of SNPs that causally explain a given proportion (e.g. 50%) of SNP heritability. +

+**PolyPred** exploits fine-mapping to improve cross-population polygenic risk scores, by predicting using causal effect estimates intead of tagging effect estimates. + +We also provide a script called **finemapper** that facilitates fine-mapping with methods like SuSiE, saving many of the preprocessing steps often required to perform fine-mapping (e.g. handling allelic flips between the summary statistics and reference genotypes). + +The files in the `ldstore` directory are an adaptation of the [ldstore package](https://pypi.org/project/ldstore/) (written by [Christian Benner](http://www.christianbenner.com)) to Python 3. + +
+
+ + +# Manual +We provide a detailed manual of PolyFun, PolyLoc and PolyPred in the [Wiki page](https://github.com/omerwe/polyfun/wiki). If you run into any issues, please check [the FAQ](https://github.com/omerwe/polyfun/wiki/7.-FAQ) first. + + + +

+# Installation + +We provide several installation options. + +## Install option 1: Create an Anaconda environment +The easiest way to install polyfun is by creating a dedicated environment through the [Anaconda Python distribution](https://www.anaconda.com/download). To do this, please install Anaconda on your machine and then type the following commands: +``` +git clone https://github.com/omerwe/polyfun +cd polyfun +conda env create -f polyfun.yml +conda activate polyfun +``` + âš¡ **Note**: You can speed up the installation by ~100x by [installing mamba](https://github.com/mamba-org/mamba) and then replacing mamba with conda in the commands above. + + +This will install all the dependencies except for [FINEMAP](http://www.christianbenner.com) and [LDstore](http://www.christianbenner.com) +This will allow you to perform fine-mapping using SuSiE, but not using FINEMAP. Please see installation instructions for FINEMAP and LDstore below. + +After the installation, you can always invoke the PolyFun environment with the command `conda activate polyfun`. +We recommend that you frequently make sure you have the latest version of polyfun installed by going to the polyfun directory and typing `git pull`. + +If you have any trouble with the conda environment you created, you can instead try installing the dependencies using the conda lockfile `polyfun.yml.lock`. +This installs software versions that are known to work, but they will be outdated compared to installing a fresh conda environment. + +``` +mamba create --name polyfun-lock --file polyfun.yml.lock +conda activate polyfun-lock +``` + +## Install option 2: Manually install packages +PolyFun and PolyLoc are designed for Python >=3.6 and require the following freely available Python packages: +* [numpy](http://www.numpy.org/) and [scipy](http://www.scipy.org/) +* [scikit-learn](http://scikit-learn.org/stable/) +* [pandas](https://pandas.pydata.org/getpandas.html) (version >=0.25.0) +* [tqdm](https://github.com/tqdm/tqdm) +* [pyarrow](https://arrow.apache.org/docs/python/install.html) +* [bitarray](https://github.com/ilanschnell/bitarray) +* [networkx](https://github.com/networkx/networkx) (only required for HESS-based estimation of effect size variance) +* [pandas-plink](https://github.com/limix/pandas-plink) +* [r-susier](https://anaconda.org/conda-forge/r-susier) + +It is recommended (but not required) to also install the following: +* [rpy2](https://rpy2.bitbucket.io/) (a Python package) +* [R version 3.5.1 or higher](https://www.r-project.org/) +* [Ckmeans.1d.dp](https://cran.r-project.org/web/packages/Ckmeans.1d.dp/index.html) (a package for R, that will be invoked from python via the rpy2 package). + +If rpy2 or Ckmeans.1d.dp are not installed, PolyFun and PolyLoc will fallback to suboptimal clustering via scikit-learn. +If you'd like to use FINEMAP instead of SuSiE for fine-mappping, you will also require: +1. [FINEMAP v1.4.1](http://www.christianbenner.com). +2. (optional) The program [LDstore 2.0](http://www.christianbenner.com) for computing LD directly from .bgen files (imputed genotypes) + +Please see installation instructions for these packages below. + +We recommend running PolyFun/PolyLoc via the [Anaconda Python distribution](https://www.anaconda.com/download/). In Anaconda, you can install all the Python packages with the command "conda install \". Alternatively, the Python packages can be installed with the command "pip install --user \". + +Once all the prerequisite packages are installed, you can install PolyFun/PolyLoc on a git-enabled machine by typing: +``` +git clone https://github.com/omerwe/polyfun +``` +We recommend that you frequently make sure you have the latest version of polyfun installed by going to the polyfun directory and typing `git pull`. + + + +## Installing FINEMAP v1.4.1 +To install FINEMAP v1.4.1, please type one of the following two commands: +
+If you use Linux: +``` +wget http://christianbenner.com/finemap_v1.4.1_x86_64.tgz +tar xvf finemap_v1.4.1_x86_64.tgz +``` +If you use Mac OS X : +``` +wget http://christianbenner.com/finemap_v1.4.1_MacOSX.tgz +tar xvf finemap_v1.4.1_MacOSX.tgz +``` + +## Installing LDstore 2.0 +To install LDstore, please type one of the following two commands: +
+If you use Linux: +``` +wget http://www.christianbenner.com/ldstore_v2.0_x86_64.tgz +tar xzvf ldstore_v2.0_x86_64.tgz +``` +If you use Mac OS X : +``` +wget http://www.christianbenner.com/ldstore_v2.0_MacOSX.tgz +tar xzvf ldstore_v2.0_MacOSX.tgz +``` + + +

+ +## Testing the installation +We recommend testing PolyFun by invoking the script: +``` +python test_polyfun.py --python3 +``` +where `python3_exe` (optional) is the command you type to start a python3 session (default is `python`). If the script completes without an error, everything is fine. If you see any errors, please consult the [FAQ](https://github.com/omerwe/polyfun/wiki/7.-FAQ). + +To test FINEMAP integration, provide the path to the executable when invoking the script (if not provided, the script will not test FINEMAP integration): +``` +python test_polyfun.py --python3 --finemap-exe +``` + + + +

+# Contact +For questions and comments, please open a Github issue. + + + diff --git a/SCRIPTS/polyfun/_archive/polyfun.run.genius.sh b/SCRIPTS/polyfun/_archive/polyfun.run.genius.sh new file mode 100644 index 0000000..4a8da9c --- /dev/null +++ b/SCRIPTS/polyfun/_archive/polyfun.run.genius.sh @@ -0,0 +1,184 @@ +#!/bin/bash + +# SLURM settings +##SBATCH --array=1-49 # create 23 jobs from this +#SBATCH --job-name=polyfun # Job name +#SBATCH --mail-type=FAIL # Mail events (NONE, BEGIN, END, FAIL, ALL) +#SBATCH --mail-user=e.j.a.smulders-2@umcutrecht.nl # Where to send mail +#SBATCH --nodes=1 # Run on one node +#SBATCH --ntasks=1 # Run on a single CPU +#SBATCH --mem=128G # Job memory request +#SBATCH --time=24:00:00 # Time limit hrs:min:sec +#SBATCH --output=polyfun.out # Standard output and error log + + +# SuSIE +# https://github.com/omerwe/polyfun +# make conda env +# CHR - chromosome +# BP - base pair position (in hg19 coordinates) +# A1 - The effect allele (i.e., the sign of the effect size is with respect to A1) +# A2 - the second allele + +# A PolyFun workflow + +# change env location accordingly +source /hpc/local/Rocky8/dhl_ec/software/mambaforge3/bin/activate polyfun + +#ROOT="/Users/esmulde2" + + +python3_exe="/hpc/local/Rocky8/dhl_ec/software/mambaforge3/envs/polyfun/bin/python3" + +DATA="" +REF="" +#DATA="/Volumes/MacExtern/UMC/Werk/GENIUS-CHD" +# DATA="/Volumes/MacExtern/UMC/Werk/cIMT/polyfun" +#REF="/Volumes/MacExtern/UMC/Werk/references" +# Define PHENOTYPES and their respective max sample sizes +PHENOTYPE="" +SAMPLESIZE= +# SAMPLESIZE=(46018 45589) # Corresponding sample sizes + +# Default values for parameters +default_min_maf=0.005 +default_max_num_causal=1 +default_pip_cutoff=0.50 +default_phenotype_file="" + +# Function to display usage information +usage() { + echo "Usage: $0 [Options]" 1>&2 + echo "Options:" 1>&2 + echo " -p : Specify the PHENOTYPE file (default: $default_phenotype_file)." 1>&2 + echo " -d : Specify the map for data (default: $DATA)." 1>&2 + echo " -m : Specify the minimum MAF (default: $default_min_maf)." 1>&2 + echo " -x : Specify the maximum number of causal SNPs (default: $default_max_num_causal)." 1>&2 + echo " -c : Specify the PIP cutoff (default: $default_pip_cutoff)." 1>&2 + exit 1 +} + +# Parsing command-line options +while getopts ":p:s:d:j:l:m:x:c:" opt; do + case ${opt} in + p) PHENOTYPE=$OPTARG ;; + s) SAMPLESIZE=$OPTARG ;; + d) DATA=$OPTARG ;; + j) PROJECTNAME=$OPTARG ;; + l) REF=$OPTARG ;; + m) min_maf=$OPTARG ;; + x) max_num_causal=$OPTARG ;; + c) pip_cutoff=$OPTARG ;; + :) echo "Option -$OPTARG requires an argument." >&2; exit 1 ;; + \?) echo "Invalid option: -$OPTARG" >&2; exit 1 ;; + esac +done +#cd /hpc/dhl_ec/esmulders/polyfun +cd ${DATA}/polyfun + +#echo "${DATA}/polyfun" +#echo "$SAMPLESIZE" + +# Use default values if not set +min_maf=${min_maf:-$default_min_maf} +max_num_causal=${max_num_causal:-$default_max_num_causal} +pip_cutoff=${pip_cutoff:-$default_pip_cutoff} +PHENOTYPE_FILE=${PHENOTYPE_FILE:-$default_phenotype_file} + +mkdir -p ${DATA}/${PROJECTNAME}/${PHENOTYPE}/polyfun +#mkdir -p ${DATA}/${PROJECTNAME}/${PHENOTYPE}/polyfun/input +mkdir -p ${DATA}/${PROJECTNAME}/${PHENOTYPE}/polyfun/data +mkdir -p ${DATA}/${PROJECTNAME}/${PHENOTYPE}/polyfun/finemapping +mkdir -p ${DATA}/${PROJECTNAME}/${PHENOTYPE}/polyfun/annotations + + +# if [ -n "$PHENOTYPE_FILE" ]; then +# PHENOTYPES=() +# SAMPLESIZES=() +# while IFS=',' read -r phenotype sample_size; do +# PHENOTYPES+=("$phenotype") +# SAMPLESIZES+=("$sample_size") +# done < "$PHENOTYPE_FILE" +# if [ ${#PHENOTYPES[@]} -eq 0 ]; then +# echo "Phenotype file is empty or not properly formatted." +# exit 1 +# fi +# else +# echo "Phenotype file (-p) is required." +# exit 1 +# fi + +# for (( i=0; i<${#PHENOTYPES[@]}; i++ )); do +# PHENOTYPE="${PHENOTYPES[i]}" +# SAMPLESIZE="${SAMPLESIZES[i]}" +# echo "Processing PHENOTYPE with PolyFun: $PHENOTYPE" + + + +# # # # File formatting +echo "CHR BP A1 A2 BETA SE MAF P RSID N" > ${DATA}/${PROJECTNAME}/${PHENOTYPE}/input/${PHENOTYPE}.qc.polyfun.txt +gzip -dc ${DATA}/${PROJECTNAME}/gwaslab/${PHENOTYPE}.b37.qc.gwaslab.ssf.tsv.gz | awk '{ print $1, $2, $3, $4, $5, $6, $7, $8, $9, $11}' | tail -n +2 >> ${DATA}/${PROJECTNAME}/${PHENOTYPE}/input/${PHENOTYPE}.qc.polyfun.txt +gzip -vf ${DATA}/${PROJECTNAME}/${PHENOTYPE}/input/${PHENOTYPE}.qc.polyfun.txt +# +# # 1. lead snps make parquet +$python3_exe ${DATA}/polyfun/munge_polyfun_sumstats.py --sumstats ${DATA}/${PROJECTNAME}/${PHENOTYPE}/input/${PHENOTYPE}.qc.polyfun.txt.gz --out ${DATA}/${PROJECTNAME}/${PHENOTYPE}/polyfun/data/${PHENOTYPE}.lead_snps.parquet --min-info 0 --min-maf ${min_maf} +# # +# # # 2. extract snpvar +$python3_exe ${DATA}/polyfun/extract_snpvar.py --sumstats ${DATA}/${PROJECTNAME}/${PHENOTYPE}/polyfun/data/${PHENOTYPE}.lead_snps.parquet --out ${DATA}/${PROJECTNAME}/${PHENOTYPE}/polyfun/data/${PHENOTYPE}.snps_with_var.gz --allow-missing +# # # +# # # 3. finemapper jobs +$python3_exe ${DATA}/polyfun/create_finemapper_jobs.py \ + --sumstats ${DATA}/${PROJECTNAME}/${PHENOTYPE}/polyfun/data/${PHENOTYPE}.snps_with_var.gz \ + --n ${SAMPLESIZE} \ + --method susie \ + --pvalue-cutoff 5e-8 \ + --max-num-causal ${max_num_causal} \ + --memory 128 \ + --verbose \ + --out-prefix ${DATA}/${PROJECTNAME}/${PHENOTYPE}/polyfun/finemapping/${PHENOTYPE} \ + --jobs-file ${DATA}/${PROJECTNAME}/${PHENOTYPE}/polyfun/finemapping/${PHENOTYPE}.polyfun_all_jobs.sh \ + --allow-missing +# # # +# # 4. Run finemapping if jobs file is not empty +if [ -s ${DATA}/${PROJECTNAME}/${PHENOTYPE}/polyfun/finemapping/${PHENOTYPE}.polyfun_all_jobs.sh ]; then + bash ${DATA}/${PROJECTNAME}/${PHENOTYPE}/polyfun/finemapping/${PHENOTYPE}.polyfun_all_jobs.sh +else + echo "No finemapping jobs to run. Skipping finemapping step." +fi +# # +# # # 5. Aggregate results if there are files in the finemap folder +finemap_folder=${DATA}/${PROJECTNAME}/${PHENOTYPE}/polyfun/finemapping +if [ -d "$finemap_folder" ] && [ "$(ls -A $finemap_folder)" ]; then + $python3_exe ${DATA}/polyfun/aggregate_finemapper_results.py \ + --out-prefix ${DATA}/${PROJECTNAME}/${PHENOTYPE}/polyfun/finemapping/${PHENOTYPE} \ + --sumstats ${DATA}/${PROJECTNAME}/${PHENOTYPE}/polyfun/data/${PHENOTYPE}.snps_with_var.gz \ + --out ${DATA}/${PROJECTNAME}/${PHENOTYPE}/polyfun/data/${PHENOTYPE}.polyfun_agg.txt.gz \ + --allow-missing-jobs +else + echo "No files found in the finemap folder. Skipping aggregation." +fi +# +# # # 6. Extract annotations for each significant locus +finemap_folder=${DATA}/polyfun/output/${PHENOTYPE}/finemap +if [ -d "$finemap_folder" ] && [ "$(ls -A $finemap_folder)" ]; then + for file in ${DATA}/${PROJECTNAME}/${PHENOTYPE}/polyfun/finemapping/*.gz; do + if [[ $file =~ ${PHENOTYPE}.chr([0-9]+).([0-9]+)_([0-9]+).gz ]]; then + chr=${BASH_REMATCH[1]} + start=${BASH_REMATCH[2]} + end=${BASH_REMATCH[3]} + annot_file=${REF}/baselineLF2.2.UKB/baselineLF2.2.UKB.${chr}.annot.parquet + output_file=${DATA}/${PROJECTNAME}/${PHENOTYPE}/polyfun/annotations/top_annot.${chr}.${start}_${end}.txt.gz + $python3_exe ${DATA}/polyfun/extract_annotations.py \ + --pips $file \ + --annot $annot_file \ + --pip-cutoff ${pip_cutoff} \ + --out $output_file + else + echo "Skipping file: $file (does not match pattern or no PIP > 0.5)." + fi + done +else + echo "No files found in the finemap folder. Skipping extracting annotations." + fi + +#done diff --git a/SCRIPTS/polyfun/aggregate_finemapper_results.py b/SCRIPTS/polyfun/aggregate_finemapper_results.py new file mode 100644 index 0000000..ae30d21 --- /dev/null +++ b/SCRIPTS/polyfun/aggregate_finemapper_results.py @@ -0,0 +1,114 @@ +import numpy as np; np.set_printoptions(precision=4, linewidth=200) +import pandas as pd; pd.set_option('display.width', 200) +import os +import logging +import scipy.stats as stats +from tqdm import tqdm +from polyfun import configure_logger, check_package_versions +from polyfun_utils import set_snpid_index +from pyarrow import ArrowIOError +from pyarrow.lib import ArrowInvalid +from polyfun_utils import DEFAULT_REGIONS_FILE + + +def main(args): + + #read sumstats file + try: + df_sumstats = pd.read_parquet(args.sumstats) + except (ArrowIOError, ArrowInvalid): + df_sumstats = pd.read_table(args.sumstats, sep='\s+') + + #compute p-values if needed + if args.pvalue_cutoff is not None: + df_sumstats['P'] = stats.chi2(1).sf(df_sumstats['Z']**2) + + + #read regions file + df_regions = pd.read_table(args.regions_file) + if args.chr is not None: + df_regions = df_regions.query('CHR==%d'%(args.chr)) + if df_regions.shape[0]==0: raise ValueError('no SNPs found in chromosome %d'%(args.chr)) + df_regions_keep = df_regions.apply(lambda r: np.sum((df_sumstats['CHR']==r['CHR']) & (df_sumstats['BP'].between(r['START'], r['END']))) > 1, axis=1) + df_regions = df_regions.loc[df_regions_keep] + + #aggregate outputs + df_sumstats_list = [] + logging.info('Aggregating results...') + for _, r in tqdm(df_regions.iterrows()): + chr_num, start, end, url_prefix = r['CHR'], r['START'], r['END'], r['URL_PREFIX'] + + #apply p-value filter if needed + if args.pvalue_cutoff is not None: + df_sumstats_r = df_sumstats.query('CHR==%d & %d <= BP <= %d'%(chr_num, start, end)) + if np.all(df_sumstats_r['P'] > args.pvalue_cutoff): continue + + output_file_r = '%s.chr%s.%s_%s.gz'%(args.out_prefix, chr_num, start, end) + if not os.path.exists(output_file_r): + err_msg = 'output file for chromosome %d bp %d-%d doesn\'t exist'%(chr_num, start, end) + if args.allow_missing_jobs: + logging.warning(err_msg) + continue + else: + raise IOError(err_msg + '.\nTo override this error, please provide the flag --allow-missing-jobs') + df_sumstats_r = pd.read_table(output_file_r) + + #add the current region to the credible set + df_sumstats_r['CREDIBLE_SET'] = 'chr%s:%s-%s:'%(chr_num, start, end) + df_sumstats_r['CREDIBLE_SET'].astype(str) + + #mark distance from center + middle = (start+end)//2 + df_sumstats_r['DISTANCE_FROM_CENTER'] = np.abs(df_sumstats_r['BP'] - middle) + df_sumstats_list.append(df_sumstats_r) + if len(df_sumstats_list)==0: + raise ValueError('no output files found') + + + #keep only the most central result for each SNP + df_sumstats = pd.concat(df_sumstats_list, axis=0) + df_sumstats.sort_values('DISTANCE_FROM_CENTER', inplace=True, ascending=True) + df_sumstats = set_snpid_index(df_sumstats, allow_duplicates=True) + df_sumstats = df_sumstats.loc[~df_sumstats.index.duplicated(keep='first')] + del df_sumstats['DISTANCE_FROM_CENTER'] + df_sumstats.sort_values(['CHR', 'BP'], inplace=True, ascending=True) + + #write output file + if args.adjust_beta_freq: + df_sumstats['BETA_MEAN'] /= np.sqrt(2*df_sumstats['MAF']*(1-df_sumstats['MAF'])) + df_sumstats['BETA_SD'] /= np.sqrt(2*df_sumstats['MAF']*(1-df_sumstats['MAF'])) + df_sumstats.to_csv(args.out, sep='\t', index=False) + logging.info('Wrote aggregated results to %s'%(args.out)) + + + +if __name__ == '__main__': + import argparse + parser = argparse.ArgumentParser() + + #general parameters + parser.add_argument('--sumstats', required=True, help='Name of sumstats file') + parser.add_argument('--out-prefix', required=True, help='prefix of output files') + parser.add_argument('--out', required=True, help='name of the aggregated output files') + parser.add_argument('--allow-missing-jobs', default=False, action='store_true', help='whether to allow missing jobs') + parser.add_argument('--regions-file', default=DEFAULT_REGIONS_FILE, help='name of file of regions and their URLs') + parser.add_argument('--chr', default=None, type=int, help='Target chromosome (if not provided, all chromosomes will be considered)') + parser.add_argument('--pvalue-cutoff', type=float, default=None, help='only consider regions that have at least one SNP with a p-value greater than this cutoff') + parser.add_argument('--adjust-beta-freq', default=False, action='store_true', help='If specified, the posterior estimates of the SNP effect sizes will be on per-allele scale rather than a per-standardized genotype scale') + + + #check package versions + check_package_versions() + + #extract args + args = parser.parse_args() + + #check that the output directory exists + if len(os.path.dirname(args.out))>0 and not os.path.exists(os.path.dirname(args.out)): + raise ValueError('output directory %s doesn\'t exist'%(os.path.dirname(args.out))) + + #configure logger + configure_logger(args.out_prefix) + + #invoke main function + main(args) + diff --git a/SCRIPTS/polyfun/compute_ldscores.py b/SCRIPTS/polyfun/compute_ldscores.py new file mode 100644 index 0000000..3937164 --- /dev/null +++ b/SCRIPTS/polyfun/compute_ldscores.py @@ -0,0 +1,179 @@ +import numpy as np +import pandas as pd +import os +import sys +import time +from ldsc_polyfun import ldscore, parse +import logging +from pandas.api.types import is_numeric_dtype +from polyfun_utils import configure_logger, set_snpid_index, SNP_COLUMNS +from pyarrow import ArrowIOError +from pyarrow.lib import ArrowInvalid + + + +def __filter__(fname, noun, verb, merge_obj): + merged_list = None + if fname: + f = lambda x,n: x.format(noun=noun, verb=verb, fname=fname, num=n) + x = parse.FilterFile(fname) + c = 'Read list of {num} {noun} to {verb} from {fname}' + print(f(c, len(x.IDList))) + merged_list = merge_obj.loj(x.IDList) + len_merged_list = len(merged_list) + if len_merged_list > 0: + c = 'After merging, {num} {noun} remain' + print(f(c, len_merged_list)) + else: + error_msg = 'No {noun} retained for analysis' + raise ValueError(f(error_msg, 0)) + + return merged_list + + + +def compute_ldscores(args): + + #read bim/snp + array_snps = parse.PlinkBIMFile(args.bfile+'.bim') + df_bim = array_snps.df + if len(df_bim['CHR'].unique()) > 1: + raise ValueError('plink file includes multiple chromosomes. Please specify a plink file with a single chromosome') + df_bim = set_snpid_index(df_bim) + + #read annotations + keep_snps = None + if args.annot is not None: + + try: + df_annot = pd.read_parquet(args.annot) + except (ArrowIOError, ArrowInvalid): + df_annot = pd.read_table(args.annot, sep='\s+') + + #Remove annotations of SNPs that are not in the .bim file + df_annot = set_snpid_index(df_annot) + df_annot = df_annot.loc[df_annot.index.isin(df_bim.index)] + + #make sure that all SNPs have annotations + if np.any(~df_bim.index.isin(df_annot.index)): + error_msg = 'Not all SNPs have annotation values' + if args.allow_missing: + is_good_snp = df_bim.index.isin(df_annot.index) + if not np.any(is_good_snp): + raise ValueError('No SNPs have annotations') + keep_snps = np.where(is_good_snp)[0] + logging.warning(error_msg) + logging.warning('Keeping only %d/%d SNPs that have annotations'%(is_good_snp.sum(), len(is_good_snp))) + else: + raise ValueError(error_msg + '. If you wish to omit the missing SNPs, please use the flag --allow-missing') + + #make sure that all of the annotations are numeric + for c in df_annot.columns: + if c in SNP_COLUMNS: continue + if not is_numeric_dtype(df_annot[c]): + raise ValueError('Annotation %s does not have numeric values'%(c)) + + #find #individuals in bfile + fam_file = args.bfile+'.fam' + df_fam = pd.read_table(fam_file, header=None, usecols=[5], sep='\s+') + n = df_fam.shape[0] + + #find keep_indivs + if args.keep is None: + keep_indivs= None + else: + array_indivs = parse.PlinkFAMFile(args.bfile+'.fam') + keep_indivs = __filter__(args.keep, 'individuals', 'include', array_indivs) + logging.info('after applying --keep, %d individuals remain'%(len(keep_indivs))) + + #read plink file + bed_file = args.bfile+'.bed' + geno_array = ldscore.PlinkBEDFile(bed_file, n, array_snps, keep_snps=keep_snps, + keep_indivs=keep_indivs, mafMin=None) + + #remove omitted SNPs from df_bim + if len(geno_array.kept_snps) < df_bim.shape[0]: + assert np.all(np.array(geno_array.kept_snps) == np.sort(np.array(geno_array.kept_snps))) + assert geno_array.kept_snps[-1] < df_bim.shape[0] + df_bim = df_bim.iloc[geno_array.kept_snps] + + #rearrange annotations to match the order of SNPs in the plink file + if args.annot is not None: + assert df_annot.shape[0] >= df_bim.shape[0] + if (df_annot.shape[0] > df_bim.shape[0]) or np.any(df_annot.index != df_bim.index): + assert np.all(df_bim.index.isin(df_annot.index)) + df_annot = df_annot.loc[df_bim.index] + + # determine block widths + num_wind_args = np.array((args.ld_wind_snps, args.ld_wind_kb, args.ld_wind_cm), dtype=bool) + if np.sum(num_wind_args) != 1: + raise ValueError('Must specify exactly one --ld-wind option') + if args.ld_wind_snps: + max_dist = args.ld_wind_snps + coords = np.array(list(range(geno_array.m))) + elif args.ld_wind_kb: + max_dist = args.ld_wind_kb*1000 + coords = np.array(df_bim['BP']) + if len(np.unique(coords)) == 1: + raise ValueError('bim file has no basepair data --- please use a different ld-wind option') + elif args.ld_wind_cm: + max_dist = args.ld_wind_cm + coords = np.array(df_bim['CM']) + if len(np.unique(coords)) == 1: + raise ValueError('bim file has no CM data --- please use a different ld-wind option') + + #compute LD-scores + block_left = ldscore.getBlockLefts(coords, max_dist) + if block_left[len(block_left)-1] == 0: + error_msg = 'Only a single block selected - this is probably a mistake' + raise ValueError(error_msg) + t0 = time.time() + geno_array._currentSNP = 0 + annot_values = (None if args.annot is None else df_annot.drop(columns=SNP_COLUMNS).values) + ldscores = geno_array.ldScoreVarBlocks(block_left, args.chunk_size, annot=annot_values) + + #create an ldscores df + if args.annot is None: + df_ldscores = pd.DataFrame(ldscores, columns=['base']) + else: + df_ldscores = pd.DataFrame(ldscores, columns=df_annot.drop(columns=SNP_COLUMNS).columns) + + #add SNP identifier columns + for c in SNP_COLUMNS: + df_ldscores[c] = df_bim[c].values + df_ldscores = df_ldscores[SNP_COLUMNS + list(df_ldscores.drop(columns=SNP_COLUMNS).columns)] + + return df_ldscores + + + +if __name__ == '__main__': + + import argparse + parser = argparse.ArgumentParser() + parser.add_argument('--bfile', required=True, help='Plink file') + parser.add_argument('--out', required=True, help='output file') + parser.add_argument('--annot', default=None, help='annotations file (if not specified, compute the global LD-score)') + parser.add_argument('--ld-wind-cm', type=float, default=None, help='window size to be used for estimating LD-scores in units of centiMorgans (cM).') + parser.add_argument('--ld-wind-kb', type=int, default=None, help='window size to be used for estimating LD-scores in units of Kb.') + parser.add_argument('--ld-wind-snps', type=int, default=None, help='window size to be used for estimating LD-scores in units of SNPs.') + parser.add_argument('--chunk-size', type=int, default=50, help='chunk size for LD-scores calculation') + #parser.add_argument('--extract', default=None, help='File with rsids of SNP to use') + parser.add_argument('--keep', default=None, help='File with ids of individuals to use') + parser.add_argument('--allow-missing', default=False, action='store_true', help='If specified, The script will not terminate if some SNPs with sumstats are not found in the annotations files') + args = parser.parse_args() + + configure_logger(args.out) + + if args.ld_wind_cm is None and args.ld_wind_kb is None and args.ld_wind_snps is None: + args.ld_wind_cm = 1.0 + logging.warning('no ld-wind argument specified. We will use --ld-cm 1.0') + + df_ldscores = compute_ldscores(args) + df_ldscores.to_parquet(args.out) + + + + + + \ No newline at end of file diff --git a/SCRIPTS/polyfun/compute_ldscores_from_ld.py b/SCRIPTS/polyfun/compute_ldscores_from_ld.py new file mode 100644 index 0000000..7d8a42a --- /dev/null +++ b/SCRIPTS/polyfun/compute_ldscores_from_ld.py @@ -0,0 +1,360 @@ +import numpy as np +import pandas as pd +import os +import sys +import time +import logging +from tqdm import tqdm +import urllib.request +from pyarrow import ArrowIOError +from pyarrow.lib import ArrowInvalid +import tempfile +import scipy.sparse as sparse +from pandas.api.types import is_numeric_dtype +from polyfun_utils import configure_logger, set_snpid_index, TqdmUpTo, LONG_RANGE_LD_REGIONS +from ldstore.bcor import bcor + + +UKBB_LD_URL = 'https://broad-alkesgroup-ukbb-ld.s3.amazonaws.com/UKBB_LD' +REGION_LENGTH = 3000000 +UKB_N=337545 +META_COLUMNS = ['SNP', 'CHR', 'BP', 'A1', 'A2'] + + +def read_annot(annot_file): + try: + df_annot = pd.read_parquet(annot_file) + except (ArrowIOError, ArrowInvalid): + df_annot = pd.read_table(annot_file, sep='\s+') + + assert 'CHR' in df_annot.columns + assert 'SNP' in df_annot.columns + assert 'BP' in df_annot.columns + assert 'A1' in df_annot.columns + assert 'A2' in df_annot.columns + + for c in df_annot.columns: + if c in META_COLUMNS: continue + if not is_numeric_dtype(df_annot[c]): + raise ValueError('Annotation %s does not have numeric values'%(c)) + + + df_annot = set_snpid_index(df_annot) + + return df_annot + + +def compute_R2_unbiased(R, n): + np.power(R, 2, out=R) + R *= ((n-1)/(n-2)) + R -= 1/(n-2) + return R + + + +def load_ld_npz(ld_dir, ld_prefix): + + #load the SNPs metadata + gz_file = os.path.join(ld_dir, '%s.gz'%(ld_prefix)) + try: + df_ld_snps = pd.read_table(gz_file, sep='\s+') + except (ArrowIOError, ArrowInvalid): + raise IOError('Corrupt file downloaded') + df_ld_snps.rename(columns={'rsid':'SNP', 'chromosome':'CHR', 'position':'BP', 'allele1':'A1', 'allele2':'A2'}, inplace=True, errors='ignore') + assert 'SNP' in df_ld_snps.columns + assert 'CHR' in df_ld_snps.columns + assert 'BP' in df_ld_snps.columns + assert 'A1' in df_ld_snps.columns + assert 'A2' in df_ld_snps.columns + df_ld_snps = set_snpid_index(df_ld_snps) + + #load the LD matrix + npz_file = os.path.join(ld_dir, '%s.npz'%(ld_prefix)) + logging.info('Loading LD from file %s'%(npz_file)) + t0 = time.time() + try: + R = sparse.load_npz(npz_file).toarray() + R += R.T + except ValueError: + raise IOError('Corrupt file downloaded') + logging.info('Done in %0.2f seconds'%(time.time() - t0)) + + #create df_R and return it + df_R = pd.DataFrame(R, index=df_ld_snps.index, columns=df_ld_snps.index) + return df_R, df_ld_snps + + +def get_bcor_meta(bcor_obj): + df_ld_snps = bcor_obj.getMeta() + df_ld_snps.rename(columns={'rsid':'SNP', 'position':'BP', 'chromosome':'CHR', 'allele1':'A1', 'allele2':'A2'}, inplace=True, errors='raise') + df_ld_snps['CHR'] = df_ld_snps['CHR'].astype(np.int64) + df_ld_snps['BP'] = df_ld_snps['BP'].astype(np.int64) + df_ld_snps = set_snpid_index(df_ld_snps) + return df_ld_snps + + +def load_ld_bcor(bcor_file): + if not os.path.exists(bcor_file): + raise IOError('%s not found'%(bcor_file)) + logging.info('Loading LD file %s'%(bcor_file)) + t0 = time.time() + bcor_obj = bcor(bcor_file) + df_ld_snps = get_bcor_meta(bcor_obj) + ld_arr = bcor_obj.readCorr([]) + logging.info('Done in %0.2f seconds'%(time.time() - t0)) + df_R = pd.DataFrame(ld_arr, index=df_ld_snps.index, columns=df_ld_snps.index) + return df_R, df_ld_snps + + +def load_ld(ld_file): + if not ld_file.endswith('.bcor'): + raise NotImplementedError('only .bcor files are currenty supported') + df_R, df_ld_snps = load_ld_bcor(ld_file) + chr_num = df_ld_snps['CHR'].iloc[0] + region_start = df_ld_snps['BP'].min() + region_end = df_ld_snps['BP'].max() + + return df_R, chr_num, region_start, region_end + + +def download_ukb_ld_file(chr_num, region_start, overwrite=False, ld_dir=None, no_cache=False): + region_end = region_start + REGION_LENGTH + ld_prefix = 'chr%d_%d_%d'%(chr_num, region_start, region_end) + + #if the files already exist, simply return them + gz_file = os.path.join(ld_dir, '%s.gz'%(ld_prefix)) + npz_file = os.path.join(ld_dir, '%s.npz'%(ld_prefix)) + if not overwrite and os.path.exists(gz_file) and os.path.exists(npz_file): + try: + df_R, _ = load_ld_npz(ld_dir, ld_prefix) + return df_R + except IOError: + pass + + ### if we got here, we need to download the LD files ### + + #download the region files + for suffix in ['npz', 'gz']: + + suffix_file = os.path.join(ld_dir, '%s.%s'%(ld_prefix, suffix)) + url = '%s/%s.%s'%(UKBB_LD_URL, ld_prefix, suffix) + + #special handling for long-range LD regions + try: + urllib.request.urlopen(url) + except urllib.request.HTTPError: + url += '2' + urllib.request.urlopen(url) + + with TqdmUpTo(unit='B', unit_scale=True, unit_divisor=1024, miniters=1, desc='downloading %s'%(url)) as t: + urllib.request.urlretrieve(url, filename=suffix_file, reporthook=t.update_to) + + + #load the LD matrix to memory + df_R, _ = load_ld_npz(ld_dir, ld_prefix) + + #delete the downloaded files if requested + if no_cache: + for suffix in ['npz', 'gz']: + suffix_file = os.path.join(ld_dir, '%s.%s'%(ld_prefix, suffix)) + os.remove(suffix_file) + + return df_R + + + + + +def compute_ldscores_region(df_R_region, df_annot_region, n, is_binary, chr_num, region_start, region_end): + + #sync df_R_region and df_annot_region + index_intersect = df_R_region.index.intersection(df_annot_region.index) + if len(index_intersect)==0: + raise ValueError('no SNPs in chromosome %d BP %d-%d had LD info'%(chr_num, region_start, region_end)) + if len(index_intersect) < df_R_region.shape[0]: + logging.warning('Only %d/%d SNPs in chromosome %d BP %d-%d have annotations info. This may severely down-bias the LD-scores'%(len(index_intersect), df_R_region.shape[0], chr_num, region_start, region_end)) + is_keep = df_R_region.index.isin(index_intersect) + df_R_region = df_R_region.loc[is_keep, is_keep] + if df_annot_region.shape[0] != df_R_region.shape[0] or np.any(df_annot_region.index != df_R_region.index): + df_annot_region = df_annot_region.loc[df_R_region.index] + assert np.all(df_annot_region.index == df_R_region.index) + + #compute R2 (the unbiased estimator of R squared) + R2_region = compute_R2_unbiased(df_R_region.values, n=n) + + #compute LD scores + df_annot_raw_region = df_annot_region.drop(columns=META_COLUMNS, errors='raise') + annot_region = df_annot_raw_region.values + if is_binary: + annot_sparse = sparse.csc_matrix(annot_region) + ld_scores_region = (annot_sparse.T.dot(R2_region)).T + else: + ld_scores_region = R2_region.dot(annot_region) + df_ldscores_region = pd.DataFrame(ld_scores_region, index=df_annot_raw_region.index, columns=df_annot_raw_region.columns) + df_ldscores_region = pd.concat((df_annot_region[META_COLUMNS], df_ldscores_region), axis=1) + df_ldscores_region['distance_from_center'] = np.abs(df_ldscores_region['BP'] - ((region_start+region_end)//2)) + + return df_ldscores_region + + +def compute_ldscores_chr(df_annot_chr, ld_dir=None, use_ukb=False, n=None, ld_files=None, no_cache=False): + + #create a temp output dir if required + if use_ukb: + if ld_dir is None: ld_dir = tempfile.mkdtemp() + if not os.path.exists(ld_dir): raise IOError('LD directory %s doesn\'t exist'%(ld_dir)) + + #infer chromosome number + assert len(df_annot_chr['CHR'].unique()) == 1 + chr_num = df_annot_chr['CHR'].unique()[0] + + # #remove long-range LD regions + # for r in LONG_RANGE_LD_REGIONS: + # if r['chr'] != chr_num: continue + # is_in_r = df_annot_chr['BP'].between(r['start'], r['end']) + # if not np.any(is_in_r): continue + # logging.warning('Removing %d SNPs from long-range LD region on chromosome %d BP %d-%d'%(is_in_r.sum(), r['chr'], r['start'], r['end'])) + # df_annot_chr = df_annot_chr.loc[~is_in_r] + # if df_annot_chr.shape[0]==0: + # raise ValueError('No SNPs found in chromosome %d (after removing long-range LD regions)'%(chr_num)) + + #sort the SNPs by BP if needed + if not np.all(np.diff(df_annot_chr['BP'])>=0): + df_annot_chr = df_annot_chr.sort_values('BP', ascending=True) + assert np.all(np.diff(df_annot_chr['BP'])>=0) + + #check if the data is binary + df_annot_chr_raw = df_annot_chr.drop(columns=META_COLUMNS, errors='raise') + if np.all(df_annot_chr_raw.dtypes == bool): + is_binary = True + elif np.all([len(np.unique(df_annot_chr_raw[c]))<=2 for c in df_annot_chr_raw.columns]): + is_binary = True + else: + is_binary = False + + #iterate over regions + df_ldscores_regions_list = [] + + #iterate over regions - UKB + if use_ukb: + for region_start in tqdm(range(1, df_annot_chr['BP'].max()+1, REGION_LENGTH)): + + #extract annotations for this region only + region_end = region_start+REGION_LENGTH + df_annot_region = df_annot_chr.query('%d <= BP <= %d'%(region_start, region_end)) + if df_annot_region.shape[0]==0: continue + + #skip over HLA region + if chr_num==6 and region_start in [28000001, 29000001, 30000001]: continue + + #download the LD data + df_R_region = download_ukb_ld_file(chr_num, region_start, ld_dir=ld_dir, no_cache=no_cache) + + df_ldscores_region = compute_ldscores_region(df_R_region, df_annot_region, n=UKB_N, is_binary=is_binary, + chr_num=chr_num, region_start=region_start, region_end=region_end) + df_ldscores_regions_list.append(df_ldscores_region) + + #iterate over LD files + else: + for ld_file in ld_files: + df_R_region, chr_num_ld, region_start, region_end = load_ld(ld_file) + assert chr_num_ld == chr_num + df_annot_region = df_annot_chr.query('%d <= BP <= %d'%(region_start, region_end)) + if df_annot_region.shape[0]==0: continue + df_ldscores_region = compute_ldscores_region(df_R_region, df_annot_region, n=n, is_binary=is_binary, + chr_num=chr_num, region_start=region_start, region_end=region_end) + df_ldscores_regions_list.append(df_ldscores_region) + + #keep the best ld-score for each SNP (the one closest to its region center) + df_ldscores_chr = pd.concat(df_ldscores_regions_list, axis=0) + df_ldscores_chr.sort_values('distance_from_center', ascending=True, inplace=True) + df_ldscores_chr = df_ldscores_chr.loc[~df_ldscores_chr.index.duplicated(keep='first')] + del df_ldscores_chr['distance_from_center'] + df_ldscores_chr.sort_values(['CHR', 'BP'], ascending=True, inplace=True) + + return df_ldscores_chr + + + +def compute_ldscores_main(args, df_annot): + + #iterate over chromosomes + df_ldscores_chr_list = [] + for chr_num, df_annot_chr in df_annot.groupby('CHR'): + #df_ldscores_chr = compute_ldscores_chr(args, df_annot_chr) + df_ldscores_chr = compute_ldscores_chr(df_annot_chr, ld_dir=args.ld_dir, use_ukb=args.ukb, n=args.n, ld_files=args.files, no_cache=args.no_cache) + df_ldscores_chr_list.append(df_ldscores_chr) + + df_ldscores = pd.concat((df_ldscores_chr_list), axis=0) + return df_ldscores + + +def main(args): + + #read annotations + df_annot = read_annot(args.annot) + + #comptue LD-scores + df_ldscores = compute_ldscores_main(args, df_annot) + + #save LD-scores to output file + if args.gz_out: + df_ldscores.to_csv(args.out, sep='\t', compression='gzip', index=False, float_format='%0.3f') + else: + df_ldscores.to_parquet(args.out, index=False) + + +if __name__ == '__main__': + + import argparse + parser = argparse.ArgumentParser() + parser.add_argument('--annot', required=True, help='annotations file') + parser.add_argument('--out', required=True, help='output file') + parser.add_argument('--ukb', default=False, action='store_true', help='if specified, the script will download and use UK Biobank LD files') + parser.add_argument('--n', type=int, help='Sample size used to compute ') + parser.add_argument('--gz-out', default=False, action='store_true', help='if specified, the output file will be a gzipped text file instead of a parquet file') + parser.add_argument('--ld-dir', default=None, help='the path of an LD files directory. If not provided, LD files will be downloaded to a temporary directory') + parser.add_argument('--no-cache', default=False, action='store_true', help='If this flag is specified, the LD files will be removed from the ld-dir after downloading them to save disk space') + parser.add_argument('files', nargs='*', help='bcor files') + args = parser.parse_args() + + configure_logger(args.out) + + #check input arguments + if args.ld_dir is not None and not os.path.exists(args.ld_dir): + raise ValueError('Specified LD directory %s doesn\'t exist'%(args.ld_dir)) + if len(os.path.dirname(args.out))>0 and not os.path.exists(os.path.dirname(args.out)): + raise ValueError('output directory %s doesn\'t exist'%(os.path.dirname(args.out))) + if args.ukb: + if args.n is not None: + raise ValueError('--n cannot be specified together with --ukb') + if len(args.files) > 0: + raise ValueError('input LD files cannot be provided when using --ukb') + else: + if args.n is None: + raise ValueError('--n must be specified when not using --ukb') + if args.ld_dir is not None: + raise ValueError('--ld-dir cannot be specified when not using --ukb') + if len(args.files)==0: + raise ValueError('no input LD files specified. Did you mean to provide the flag --ukb?') + + main(args) + + + + + + + + + + + + + + + + + + + diff --git a/SCRIPTS/polyfun/create_finemapper_jobs.py b/SCRIPTS/polyfun/create_finemapper_jobs.py new file mode 100644 index 0000000..cb81d78 --- /dev/null +++ b/SCRIPTS/polyfun/create_finemapper_jobs.py @@ -0,0 +1,121 @@ +import numpy as np; np.set_printoptions(precision=4, linewidth=200) +import pandas as pd; pd.set_option('display.width', 200) +import os +import logging +import scipy.stats as stats +from polyfun import configure_logger, check_package_versions +from pyarrow import ArrowIOError +from pyarrow.lib import ArrowInvalid +from polyfun_utils import DEFAULT_REGIONS_FILE + +FINEMAPPER_SCRIPT = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'finemapper.py') + + +def create_finemapper_cmd(args, chr_num, start, end, url_prefix): + + output_file = '%s.chr%s.%s_%s.gz'%(args.out_prefix, chr_num, start, end) + cmd = '%s %s --chr %s --start %s --end %s --out %s'%(args.python3, FINEMAPPER_SCRIPT, chr_num, start, end, output_file) + if args.max_num_causal>1 and args.geno is None: + cmd += ' --ld %s'%(url_prefix) + + #add command line arguments + for key, value in vars(args).items(): + if key in ['python3', 'regions_file', 'out_prefix', 'jobs_file', 'chr', 'pvalue_cutoff']: continue + key = key.replace('_', '-') + if type(value)==bool: + if value: + cmd += ' --%s'%(key) + elif value is not None: + cmd += ' --%s %s'%(key, value) + + return cmd + + +def main(args): + + #read sumstats file + try: + df_sumstats = pd.read_parquet(args.sumstats) + except (ArrowIOError, ArrowInvalid): + df_sumstats = pd.read_table(args.sumstats, sep='\s+') + + #compute p-values if needed + if args.pvalue_cutoff is not None: + df_sumstats['P'] = stats.chi2(1).sf(df_sumstats['Z']**2) + + #read regions file + df_regions = pd.read_table(args.regions_file) + if args.chr is not None: + df_regions = df_regions.query('CHR==%d'%(args.chr)) + if df_regions.shape[0]==0: raise ValueError('no SNPs found in chromosome %d'%(args.chr)) + df_regions_keep = df_regions.apply(lambda r: np.sum((df_sumstats['CHR']==r['CHR']) & (df_sumstats['BP'].between(r['START'], r['END']))) > 1, axis=1) + df_regions = df_regions.loc[df_regions_keep] + + + + #create jobs + with open(args.jobs_file, 'w') as f: + for _, r in df_regions.iterrows(): + chr_num, start, end, url_prefix = r['CHR'], r['START'], r['END'], r['URL_PREFIX'] + + #apply p-value filter if needed + if args.pvalue_cutoff is not None: + df_sumstats_r = df_sumstats.query('CHR==%d & %d <= BP <= %d'%(chr_num, start, end)) + if np.all(df_sumstats_r['P'] > args.pvalue_cutoff): continue + + #create and write the fine-mapping command for this region + cmd = create_finemapper_cmd(args, chr_num, start, end, url_prefix) + f.write(cmd + '\n') + + logging.info('Wrote fine-mapping commands to %s'%(args.jobs_file)) + + + +if __name__ == '__main__': + import argparse + parser = argparse.ArgumentParser() + + #general parameters + parser.add_argument('--method', required=True, help='Fine-mapping method (currently susie and finemap are supported)') + parser.add_argument('--sumstats', required=True, help='Name of sumstats file') + parser.add_argument('--n', required=True, type=int, help='Sample size') + parser.add_argument('--geno', default=None, help='Genotypes file (plink or bgen format)') + parser.add_argument('--chr', default=None, type=int, help='Target chromosome (if not provided, all chromosomes will be considered)') + + #LDstore related parameters + parser.add_argument('--finemap-exe', default=None, help='Path to FINEMAP v1.4 executable file') + parser.add_argument('--memory', type=int, default=1, help='Maximum amount of memory in GB to allocate to LDStore') + parser.add_argument('--threads', type=int, default=None, help='The number of CPU cores LDstore will use (if not specified, LDstore will use the max number of CPU cores available') + + parser.add_argument('--max-num-causal', required=True, type=int, help='Number of causal SNPs') + parser.add_argument('--non-funct', action='store_true', default=False, help='Perform non-functionally informed fine-mapping') + parser.add_argument('--hess', action='store_true', default=False, help='If specified, estimate causal effect variance via HESS') + parser.add_argument('--verbose', action='store_true', default=False, help='If specified, show verbose output') + parser.add_argument('--allow-missing', default=False, action='store_true', help='If specified, SNPs with sumstats that are not \ + found in the LD panel will be omitted. This is not recommended, because the omitted SNPs may be causal,\ + which could lead to false positive results') + + parser.add_argument('--regions-file', default=DEFAULT_REGIONS_FILE, help='name of file of regions and their URLs') + parser.add_argument('--python3', default='python3', help='python3 executable') + parser.add_argument('--out-prefix', required=True, help='prefix of the output files') + parser.add_argument('--jobs-file', required=True, help='name of file with fine-mapping commands') + parser.add_argument('--pvalue-cutoff', type=float, default=None, help='only consider regions that have at least one SNP with a p-value greater than this cutoff') + + #check package versions + check_package_versions() + + #extract args + args = parser.parse_args() + + #check that the output directory exists + if len(os.path.dirname(args.out_prefix))>0 and not os.path.exists(os.path.dirname(args.out_prefix)): + raise ValueError('output directory %s doesn\'t exist'%(os.path.dirname(args.out_prefix))) + if len(os.path.dirname(args.jobs_file))>0 and not os.path.exists(os.path.dirname(args.jobs_file)): + raise ValueError('output directory %s doesn\'t exist'%(os.path.dirname(args.jobs_file))) + + #configure logger + configure_logger(args.out_prefix) + + #invoke main function + main(args) + diff --git a/SCRIPTS/polyfun/example_data/RBC.sumstats.small.parquet b/SCRIPTS/polyfun/example_data/RBC.sumstats.small.parquet new file mode 100644 index 0000000..73095df Binary files /dev/null and b/SCRIPTS/polyfun/example_data/RBC.sumstats.small.parquet differ diff --git a/SCRIPTS/polyfun/example_data/annotations.1.annot.parquet b/SCRIPTS/polyfun/example_data/annotations.1.annot.parquet new file mode 100644 index 0000000..c926ce7 Binary files /dev/null and b/SCRIPTS/polyfun/example_data/annotations.1.annot.parquet differ diff --git a/SCRIPTS/polyfun/example_data/annotations.1.l2.M b/SCRIPTS/polyfun/example_data/annotations.1.l2.M new file mode 100644 index 0000000..2fb4146 --- /dev/null +++ b/SCRIPTS/polyfun/example_data/annotations.1.l2.M @@ -0,0 +1 @@ +96 213 156 312 2304 3615 14539 diff --git a/SCRIPTS/polyfun/example_data/annotations.1.l2.ldscore.parquet b/SCRIPTS/polyfun/example_data/annotations.1.l2.ldscore.parquet new file mode 100644 index 0000000..17ac2f0 Binary files /dev/null and b/SCRIPTS/polyfun/example_data/annotations.1.l2.ldscore.parquet differ diff --git a/SCRIPTS/polyfun/example_data/annotations.10.annot.parquet b/SCRIPTS/polyfun/example_data/annotations.10.annot.parquet new file mode 100644 index 0000000..c6527a4 Binary files /dev/null and b/SCRIPTS/polyfun/example_data/annotations.10.annot.parquet differ diff --git a/SCRIPTS/polyfun/example_data/annotations.10.l2.M b/SCRIPTS/polyfun/example_data/annotations.10.l2.M new file mode 100644 index 0000000..98c2fca --- /dev/null +++ b/SCRIPTS/polyfun/example_data/annotations.10.l2.M @@ -0,0 +1 @@ +51 87 90 164 1697 2438 9156 diff --git 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a/SCRIPTS/polyfun/example_data/reference.8.bed b/SCRIPTS/polyfun/example_data/reference.8.bed new file mode 100644 index 0000000..af5d59d Binary files /dev/null and b/SCRIPTS/polyfun/example_data/reference.8.bed differ diff --git a/SCRIPTS/polyfun/example_data/reference.8.bim b/SCRIPTS/polyfun/example_data/reference.8.bim new file mode 100644 index 0000000..d6d19dd --- /dev/null +++ b/SCRIPTS/polyfun/example_data/reference.8.bim @@ -0,0 +1,176 @@ +8 rs4382451 0.048612338 230019 C T +8 rs2121885 0.63783464 468609 C T +8 rs4735959 1.0745575 872081 C A +8 rs7009764 1.7291266 1216248 C T +8 rs2977187 3.0162632 1679777 T C +8 rs6558520 3.1069511 1697573 T C +8 rs10097786 3.1433811 1757547 G T +8 rs13264238 6.8865038 2912619 T C +8 rs1420827 14.251437 5207892 G A +8 rs4875544 14.389509 5279964 T C +8 rs4480139 16.51749 6073852 G A +8 rs2702933 18.812672 6760142 T C +8 rs7012395 24.200859 8538009 T C +8 rs598113 25.582843 9779793 T C +8 rs7832431 25.713783 9947661 A C +8 rs6601427 25.965138 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argparse.ArgumentParser() + parser.add_argument('--pips', required=True, help='Whitespace-delimited file with SNPs ids and PPs. Must include columns CHR,BP,A1,A2,PIP') + parser.add_argument('--annot', required=True, help='Annotations file') + parser.add_argument('--out', required=True, help='Prefix of the name of the output file') + parser.add_argument('--allow-missing', default=False, action='store_true', help='If specified, the script will not terminate if some SNPs are not found in the annotation file') + parser.add_argument('--pip-cutoff', type=float, default=0.95, help='Only extract annotations for SNPs with PIP greater than this cutoff (default is 0.95)') + args = parser.parse_args() + + #check package versions + check_package_versions() + + #configure logger + configure_logger(args.out) + + if not 0<=args.pip_cutoff<=1: + raise ValueError('--pip-cutoff must be between 0 and 1') + + #read pips file + try: + df_snps = pd.read_parquet(args.pips) + except (ArrowIOError, ArrowInvalid): + df_snps = pd.read_table(args.pips, sep='\s+') + if 'A1' not in df_snps.columns: + raise ValueError('missing column A1') + if 'A2' not in df_snps.columns: + raise ValueError('missing column A2') + if 'CHR' not in df_snps.columns: + raise ValueError('missing column CHR') + if 'BP' not in df_snps.columns: + raise ValueError('missing column BP') + if 'PIP' not in df_snps.columns: + raise ValueError('missing column PIP') + + #set index + df_snps = set_snpid_index(df_snps) + + #restrict to SNPs with a large PIP + df_snps = df_snps.query('PIP>=%s'%(args.pip_cutoff)) + if df_snps.shape[0]==0: + raise ValueError('No SNPs with PIP>=%s found'%(args.pip_cutoff)) + + #read df_annot + logging.info('Loading annotations file...') + t0 = time.time() + try: + df_annot = pd.read_parquet(args.annot) + except (ArrowIOError, ArrowInvalid): + df_annot = pd.read_table(args.annot, sep='\s+') + df_annot = set_snpid_index(df_annot) + logging.info('Done in %0.2f seconds'%(time.time() - t0)) + + #make sure that all SNPs have annotations + is_annot_missing = ~(df_snps.index.isin(df_annot.index)) + if np.any(is_annot_missing): + err_msg = 'The annotations of %d SNPs were not found in the annotation files'%(is_annot_missing.sum()) + if args.allow_missing: + logging.warning(err_msg) + df_snps = df_snps.loc[~is_annot_missing] + else: + raise ValueError(err_msg + '. To proceed without these SNPs, use the flag --allow-missing') + + #extract the annotations of the relevant SNPs + df_annot = df_snps[['PIP']].merge(df_annot, left_index=True, right_index=True) + + #write output to file + logging.info('Writing annotations to %s'%(args.out)) + if args.out.endswith('.parquet'): + df_annot.to_parquet(args.out, index=False) + else: + df_annot.to_csv(args.out, sep='\t', index=False, float_format='%0.4e') + + + + + diff --git a/SCRIPTS/polyfun/extract_snpvar.py b/SCRIPTS/polyfun/extract_snpvar.py new file mode 100644 index 0000000..06e0a8e --- /dev/null +++ b/SCRIPTS/polyfun/extract_snpvar.py @@ -0,0 +1,128 @@ +import pandas as pd +import numpy as np +import os +import logging +import time +from pyarrow import ArrowIOError +from pyarrow.lib import ArrowInvalid +from polyfun_utils import check_package_versions, configure_logger, set_snpid_index, SNP_COLUMNS + + + +if __name__ == '__main__': + + import argparse + parser = argparse.ArgumentParser() + parser.add_argument('--sumstats', required=True, help='Whitespace-delimited file with SNPs to extract. Must include columns CHR,BP,A1,A2') + parser.add_argument('--out', required=True, help='Prefix of the name of the output file') + parser.add_argument('--allow-missing', default=False, action='store_true', help='If specified, the script will not terminate if some SNPs are not found in the meta file') + parser.add_argument('--q', type=int, default=100, help='The maximum ratio between the largest and smallest prior causal probabilities') + args = parser.parse_args() + + #check package versions + check_package_versions() + + #configure logger + configure_logger(args.out) + + #read sumtats file + logging.info('Loading sumstats files...') + t0 = time.time() + try: + df_snps = pd.read_parquet(args.sumstats) + except (ArrowIOError, ArrowInvalid): + df_snps = pd.read_table(args.sumstats, sep='\s+') + if 'A1' not in df_snps.columns: + raise ValueError('missing column A1') + if 'A2' not in df_snps.columns: + raise ValueError('missing column A2') + if 'CHR' not in df_snps.columns: + raise ValueError('missing column CHR') + if 'BP' not in df_snps.columns: + raise ValueError('missing column BP') + + #set index + df_snps = set_snpid_index(df_snps) + logging.info('Done in %0.2f seconds'%(time.time() - t0)) + + #make sure there aren't any duplicated SNPs + if np.any(df_snps.index.duplicated()): + raise ValueError('duplicate SNPs found in output - please make sure there aren\'t any duplicate SNPs in your sumstats file') + + + #read df_meta + logging.info('Loading meta-analyzed per-SNP-h2 files...') + t0 = time.time() + script_dir = os.path.dirname(os.path.realpath(__file__)) + df_meta1 = pd.read_parquet(os.path.join(script_dir, 'snpvar_meta.chr1_7.parquet')) + df_meta2 = pd.read_parquet(os.path.join(script_dir, 'snpvar_meta.chr8_22.parquet')) + df_meta = pd.concat([df_meta1, df_meta2], axis=0) + del df_meta1 + del df_meta2 + df_meta = set_snpid_index(df_meta) + logging.info('Done in %0.2f seconds'%(time.time() - t0)) + + #truncate the ratio between the largest and smallest per-SNP h^2 + min_snpvar = df_meta['snpvar_bin'].max() / args.q + snpvar_sum = df_meta['snpvar_bin'].sum() + df_meta.loc[df_meta['snpvar_bin'] < min_snpvar, 'snpvar_bin'] = min_snpvar + snpvar_sum2 = df_meta['snpvar_bin'].sum() + df_meta['snpvar_bin'] *= snpvar_sum / snpvar_sum2 + snpvar_sum3 = df_meta['snpvar_bin'].sum() + assert np.isclose(snpvar_sum3, snpvar_sum) + + #Make sure there aren't any shared columns (except for SNP columns) + for c in df_snps.columns: + if c not in SNP_COLUMNS and c in df_meta.columns: + raise ValueError('Column %s appears in both the sumstats files and in the meta-analysis files'%(c)) + + #merge the dfs + logging.info('Merging sumstats with per-SNP h2 data...') + t0 = time.time() + df_meta = df_meta.loc[df_meta.index.isin(df_snps.index)] + df_snps = df_snps.rename(columns = {'A1': 'A_eff'}) + df = df_meta.merge(df_snps.drop(columns=SNP_COLUMNS, errors='ignore'), left_index=True, right_index=True) + + + #flip Z-sign if A1 of prior not match A1 of sumstats + is_flipped = df['A2'] == df['A_eff'] + if is_flipped.any() and 'Z' in df.columns: + df.loc[is_flipped, 'Z'] *= -1 + logging.info('Flipping the Z-sign of %d SNPs that A1 in sumstats = A2 in the per-SNP h2 data'%(is_flipped.sum())) + + #drop unneeded columns + df = df.drop(columns = 'A_eff') + + #report processing time + logging.info('Done in %0.2f seconds'%(time.time() - t0)) + + #If we didn't find everything, write a list of missing SNPs to an output file + if df.shape[0] < df_snps.shape[0]: + df_miss = df_snps.loc[~df_snps.index.isin(df_meta.index)] + df_miss.to_csv(args.out+'.miss.gz', sep='\t', index=False, compression='gzip') + error_message = 'Not all SNPs in the SNPs file were found in the meta file. Wrote a list of missing SNPs to %s'%(args.out+'.miss.gz') + if args.allow_missing: + logging.warning(error_message) + else: + raise ValueError(error_message) + + #normalize the prior-causal probabilities + df['SNPVAR'] = df['snpvar_bin'] #/ df['snpvar_bin'].sum() + #assert df['SNPVAR'].sum() == 1 + del df['snpvar_bin'] + + #make sure there aren't any duplicated SNPs + if np.any(df.index.duplicated()): + raise ValueError('duplicate SNPs found in output - please make sure there aren\'t any duplicate SNPs in your sumstats file') + + #write output to file + logging.info('Writing output file to %s'%(args.out)) + if args.out.endswith('.parquet'): + df.to_parquet(args.out, index=False) + else: + df.to_csv(args.out, sep='\t', index=False, float_format='%0.4e') + + + + + diff --git a/SCRIPTS/polyfun/find_max.sh b/SCRIPTS/polyfun/find_max.sh new file mode 100644 index 0000000..b2461c7 --- /dev/null +++ b/SCRIPTS/polyfun/find_max.sh @@ -0,0 +1,26 @@ +#!/bin/bash + +#awk 'NR > 1 { if ($11 > max) max = $11 } END { print max }' /Volumes/MacExtern/UMC/Werk/GENIUS-CHD/polyfun/input/META_cox_DEAD_ALL.b37.gwaslab.ssf.tsv +#/Volumes/MacExtern/UMC/Werk/GENIUS-CHD/polyfun/input/META_cox_DEAD_ALL.b37.gwaslab.ssf.tsv + + +# awk 'NR > 1 { if ($11 > max) max = $11 } END { print max }' /Volumes/MacExtern/UMC/Werk/GENIUS-CHD/polyfun/input/META_logistic_DEAD_CAD_noMI.b37.gwaslab.ssf.tsv +# +# awk 'NR > 1 { if ($11 > max) max = $11 } END { print max }' /Volumes/MacExtern/UMC/Werk/GENIUS-CHD/polyfun/input/META_logistic_DEAD_CAD_MI.b37.gwaslab.ssf.tsv +# +# awk 'NR > 1 { if ($11 > max) max = $11 } END { print max }' /Volumes/MacExtern/UMC/Werk/GENIUS-CHD/polyfun/input/META_logistic_DEAD_ALL.b37.gwaslab.ssf.tsv +# +# awk 'NR > 1 { if ($11 > max) max = $11 } END { print max }' /Volumes/MacExtern/UMC/Werk/GENIUS-CHD/polyfun/input/META_logistic_CVDEAD_ALL.b37.gwaslab.ssf.tsv +# +# awk 'NR > 1 { if ($11 > max) max = $11 } END { print max }' /Volumes/MacExtern/UMC/Werk/GENIUS-CHD/polyfun/input/META_cox_CHDDEAD_MI_CAD_MI.b37.gwaslab.ssf.tsv +# +# awk 'NR > 1 { if ($11 > max) max = $11 } END { print max }' /Volumes/MacExtern/UMC/Werk/GENIUS-CHD/polyfun/input/META_cox_CHDDEAD_MI_ALL.b37.gwaslab.ssf.tsv +# +# awk 'NR > 1 { if ($11 > max) max = $11 } END { print max }' /Volumes/MacExtern/UMC/Werk/GENIUS-CHD/polyfun/input/META_logistic_CHDDEAD_MI_ALL.b37.gwaslab.ssf.tsv +# +# awk 'NR > 1 { if ($11 > max) max = $11 } END { print max }' /Volumes/MacExtern/UMC/Werk/GENIUS-CHD/polyfun/input/META_cox_CVDEAD_ALL.b37.gwaslab.ssf.tsv + + +zcat /hpc/dhl_ec/esmulders/polyfun/input/META_cIMT_EUR.b37.qc.gwaslab.ssf.tsv.gz | awk 'NR > 1 { if ($11 > max) max = $11 } END { print max }' + +zcat /hpc/dhl_ec/esmulders/polyfun/input/META_cIMT_AFR.b37.qc.gwaslab.ssf.tsv.gz | awk 'NR > 1 { if ($11 > max) max = $11 } END { print max }' diff --git a/SCRIPTS/polyfun/finemapper.py b/SCRIPTS/polyfun/finemapper.py new file mode 100644 index 0000000..6d9415d --- /dev/null +++ b/SCRIPTS/polyfun/finemapper.py @@ -0,0 +1,1294 @@ +import numpy as np; np.set_printoptions(precision=4, linewidth=200) +import pandas as pd; pd.set_option('display.width', 200) +import os +import time +import scipy.stats as stats +import logging +import gzip +from tqdm import tqdm +import tempfile +import shutil +import glob +import subprocess +from importlib import reload +from polyfun_utils import set_snpid_index, TqdmUpTo +from pyarrow import ArrowIOError +from pyarrow.lib import ArrowInvalid +from ldstore.bcor import bcor +import scipy.sparse as sparse +from pandas_plink import read_plink +from sklearn.impute import SimpleImputer +from polyfun import configure_logger, check_package_versions +import urllib.request +from urllib.parse import urlparse +from packaging.version import Version + + +def splash_screen(): + print('*********************************************************************') + print('* Fine-mapping Wrapper') + print('* Version 1.0.0') + print('* (C) 2019-2024 Omer Weissbrod') + print('*********************************************************************') + print() + + + + +def uri_validator(x): + ''' + code taken from: https://stackoverflow.com/questions/7160737/python-how-to-validate-a-url-in-python-malformed-or-not + ''' + try: + result = urlparse(x) + return all([result.scheme, result.netloc, result.path]) + except: + return False + + + +def load_ld_npz(ld_prefix): + + logging.info('Loading LD file %s'%(ld_prefix)) + t0 = time.time() + + #load SNPs info + snps_filename_parquet = ld_prefix+'.parquet' + snps_filename_gz = ld_prefix+'.gz' + if os.path.exists(snps_filename_parquet): + df_ld_snps = pd.read_parquet(snps_filename_parquet) + elif os.path.exists(snps_filename_gz): + df_ld_snps = pd.read_table(snps_filename_gz, sep='\s+') + df_ld_snps.rename(columns={'allele1':'A1', 'allele2':'A2', 'position':'BP', 'chromosome':'CHR', 'rsid':'SNP'}, inplace=True, errors='ignore') + else: + raise ValueError('couldn\'t find SNPs file %s or %s'%(snps_filename_parquet, snps_filename_gz)) + + #load LD matrix + R_filename = ld_prefix+'.npz' + if not os.path.exists(R_filename): + raise IOError('%s not found'%(R_filename)) + ld_arr = sparse.load_npz(R_filename).toarray() + ld_arr = ld_arr+ld_arr.T + assert np.allclose(np.diag(ld_arr), 1.0) + assert np.all(~np.isnan(ld_arr)) + + #sanity checks + assert ld_arr.shape[0] == ld_arr.shape[1] + if ld_arr.shape[0] != df_ld_snps.shape[0]: + raise ValueError('LD matrix has a different number of SNPs than the SNPs file') + + logging.info('Done in %0.2f seconds'%(time.time() - t0)) + return ld_arr, df_ld_snps + + + + +def get_bcor_meta(bcor_obj): + df_ld_snps = bcor_obj.getMeta() + df_ld_snps.rename(columns={'rsid':'SNP', 'position':'BP', 'chromosome':'CHR', 'allele1':'A1', 'allele2':'A2'}, inplace=True, errors='raise') + ###df_ld_snps['CHR'] = df_ld_snps['CHR'].astype(np.int64) + df_ld_snps['BP'] = df_ld_snps['BP'].astype(np.int64) + return df_ld_snps + + +def load_ld_bcor(ld_prefix): + bcor_file = ld_prefix+'.bcor' + if not os.path.exists(bcor_file): + raise IOError('%s not found'%(bcor_file)) + logging.info('Loading LD file %s'%(bcor_file)) + t0 = time.time() + bcor_obj = bcor(bcor_file) + df_ld_snps = get_bcor_meta(bcor_obj) + ld_arr = bcor_obj.readCorr([]) + assert np.all(~np.isnan(ld_arr)) + logging.info('Done in %0.2f seconds'%(time.time() - t0)) + return ld_arr, df_ld_snps + + +def read_ld_from_file(ld_file): + #if ld_file is a prefix, make it into a full file name + if not ld_file.endswith('.bcor') and not ld_file.endswith('.npz'): + if os.path.exists(ld_file+'.npz'): + ld_file = ld_file + '.npz' + elif os.path.exists(ld_file+'.bcor'): + ld_file = ld_file + '.bcor' + else: + raise IOError('No suitable LD file found') + + #read the LD file + if ld_file.endswith('.bcor'): + ld_arr, df_ld_snps = load_ld_bcor(ld_file[:-5]) + elif ld_file.endswith('.npz'): + ld_arr, df_ld_snps = load_ld_npz(ld_file[:-4]) + else: + raise ValueError('unknown LD format') + assert np.all(~np.isnan(ld_arr)) + return ld_arr, df_ld_snps + + + +def download_ld_file(url_prefix): + temp_dir = tempfile.mkdtemp() + filename_prefix = os.path.join(temp_dir, 'ld') + for suffix in ['npz', 'gz']: + url = url_prefix + '.' + suffix + suffix_file = filename_prefix + '.' + suffix + with TqdmUpTo(unit='B', unit_scale=True, unit_divisor=1024, miniters=1, desc='downloading %s'%(url)) as t: + try: + urllib.request.urlretrieve(url, filename=suffix_file, reporthook=t.update_to) + except urllib.error.HTTPError as e: + if e.code == 404: + raise ValueError('URL %s wasn\'t found'%(url)) + else: + raise + + return filename_prefix + + +def run_executable(cmd, description, good_returncode=0, measure_time=True, check_errors=True, show_output=False, show_command=False): + proc = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + logging.info('Running %s...'%(description)) + if show_command: + logging.info('Command: %s'%(' '.join(cmd))) + t0 = time.time() + stdout = [] + if show_output: + for line in proc.stdout: + if len(line.strip()) > 0: + line_str = line.strip().decode("utf-8") + stdout.append(line_str) + print(line_str) + print() + stdout = '\n'.join(stdout) + _, stderr = proc.communicate() + else: + stdout, stderr = proc.communicate() + if stdout is not None: + stdout = stdout.decode('ascii') + if len(stdout)==0: stdout=None + if stderr is not None: + stderr = stderr.decode('ascii') + if len(stderr)==0: stderr=None + + #if (stderr is not None or proc.returncode != good_returncode): + if proc.returncode != good_returncode: + if stderr is not None: + logging.error('stderr:\n%s'%(stderr)) + if stdout is not None and not show_output: + logging.error('stdout:\n%s'%(stdout)) + raise RuntimeError('%s error'%(description)) + if measure_time: + logging.info('done in %0.2f seconds'%(time.time() - t0)) + + if check_errors and stdout is not None: + for l in stdout.split('\n'): + if 'error' in l.lower(): + logging.error(l) + raise RuntimeError('%s reported an error'%(description)) + if check_errors and stderr is not None: + for l in stderr.split('\n'): + if 'error' in l.lower(): + logging.error(l) + raise RuntimeError('%s reported an error'%(description)) + + return stdout, stderr + + +def save_ld_to_npz(ld_arr, df_ld_snps, npz_file): + + assert npz_file.endswith('.npz') + logging.info('Saving LD file %s'%(npz_file)) + t0 = time.time() + + #save meta file + meta_file = npz_file[:-4] + '.gz' + df_ld_snps.to_csv(meta_file, sep='\t', index=False) + + #save .npz file + R = np.tril(ld_arr).astype(np.float64) + np.fill_diagonal(R, np.diag(R)/2.0) + R = sparse.coo_matrix(R) + sparse.save_npz(npz_file, R, compressed=True) + logging.info('Done in %0.2f seconds'%(time.time() - t0)) + + + + +class Fine_Mapping(object): + def __init__(self, genotypes_file, sumstats_file, n, chr_num, ldstore_exe, + sample_file=None, incl_samples=None, cache_dir=None, n_threads=None, memory=None, + allow_swapped_indel_alleles=False): + + #check that data is valid + if genotypes_file is not None: + if genotypes_file.endswith('.bgen'): + if sample_file is None: + raise IOError('sample-file must be provided with a bgen file') + + + #read sumstats and filter to target chromosome only + logging.info('Loading sumstats file...') + t0 = time.time() + try: + df_sumstats = pd.read_parquet(sumstats_file) + except (ArrowIOError, ArrowInvalid): + df_sumstats = pd.read_table(sumstats_file, sep='\s+') + if not np.any(df_sumstats['CHR'] == chr_num): + raise IOError('sumstats file does not include any SNPs in chromosome %s'%(chr_num)) + if np.any(df_sumstats['CHR'] != chr_num): + df_sumstats = df_sumstats.query('CHR==%s'%(chr_num)).copy() + df_sumstats = set_snpid_index(df_sumstats, allow_swapped_indel_alleles=allow_swapped_indel_alleles) + if 'P' not in df_sumstats.columns: + df_sumstats['P'] = stats.chi2(1).sf(df_sumstats['Z']**2) + logging.info('Loaded sumstats for %d SNPs in %0.2f seconds'%(df_sumstats.shape[0], time.time()-t0)) + + + #save class members + self.genotypes_file = genotypes_file + self.n = n + self.sample_file = sample_file + self.df_sumstats = df_sumstats + self.incl_samples = incl_samples + self.ldstore_exe = ldstore_exe + self.cache_dir = cache_dir + self.n_threads = n_threads + self.chr = chr_num + self.memory = memory + self.allow_swapped_indel_alleles = allow_swapped_indel_alleles + + + def sync_ld_sumstats(self, ld_arr, df_ld_snps, allow_missing=False): + df_ld_snps = set_snpid_index(df_ld_snps, allow_swapped_indel_alleles=self.allow_swapped_indel_alleles) + + if ld_arr is None: + df_ld = pd.DataFrame(np.zeros(len(df_ld_snps.index), dtype=np.int64), index=df_ld_snps.index, columns=['dummy']) + else: + assert ld_arr.shape[0] == df_ld_snps.shape[0] + assert ld_arr.shape[0] == ld_arr.shape[1] + df_ld = pd.DataFrame(ld_arr, index=df_ld_snps.index, columns=df_ld_snps.index) + + #make sure that all SNPs in the sumstats file are in the LD file + snps_in_ld_file = self.df_sumstats_locus.index.isin(df_ld.index) + if not np.all(snps_in_ld_file): + # Could the missing SNPs be due to mismatched indel alleles? + df_sumstats_missing = self.df_sumstats_locus.loc[~snps_in_ld_file] + num_missing_is_indel = np.sum((df_sumstats_missing['A1'].str.len()>1) | (df_sumstats_missing['A2'].str.len()>1)) + if allow_missing: + num_missing = np.sum(~snps_in_ld_file) + logging.warning('%d variants with sumstats were not found in the LD file and will be omitted (please note that this may lead to false positives if the omitted SNPs are causal!)'%(num_missing)) + if num_missing_is_indel > 0 and not self.allow_swapped_indel_alleles: + logging.warning('%d of the missing variants were indels. Check that the allele order (A1/A2) matches between the sumstats and the LD file. Also see the flag --allow-swapped-indel-alleles'%(num_missing_is_indel)) + self.df_sumstats_locus = self.df_sumstats_locus.loc[snps_in_ld_file] + assert np.all(self.df_sumstats_locus.index.isin(df_ld.index)) + else: + error_msg = ('not all SNPs in the sumstats file were found in the LD matrix!' + ' You could drop the missing SNPs with the flag --allow-missing, but please note that' + ' these omitted SNPs may be causal, in which case you may get false positive results...' + ' If there should be no missing SNPs (e.g. you are using insample LD), see the flag --allow-swapped-indel-alleles') + raise ValueError(error_msg) + + #make sure that df_sumstats_locus is not empty + assert self.df_sumstats_locus.shape[0] > 0, 'no SNPs found in df_sumstats_locus. Please double-check that the SNPs in the LD file and in the sumstats file have the exact same positions' + + #filter LD to only SNPs found in the sumstats file + assert not np.any(self.df_sumstats_locus.index.duplicated()) + if df_ld.shape[0] != self.df_sumstats_locus.shape[0] or np.any(df_ld.index != self.df_sumstats_locus.index): + if ld_arr is None: + df_ld = df_ld.loc[self.df_sumstats_locus.index] + else: + df_ld = df_ld.loc[self.df_sumstats_locus.index, self.df_sumstats_locus.index] + df_ld_snps = df_ld_snps.loc[df_ld.index] + + #do a final verification that we're synced + assert np.all(df_ld.index == self.df_sumstats_locus.index) + assert np.all(df_ld_snps.index == self.df_sumstats_locus.index) + + #add leading zero to sumstats CHR column if needed + if np.any(df_ld_snps['CHR'].astype(str).str.startswith('0')): + self.df_sumstats_locus = self.df_sumstats_locus.copy() + self.df_sumstats_locus['CHR'] = self.df_sumstats_locus['CHR'].astype(str) + is_1digit = self.df_sumstats_locus['CHR'].str.len()==1 + self.df_sumstats_locus.loc[is_1digit, 'CHR'] = '0' + self.df_sumstats_locus.loc[is_1digit, 'CHR'] + + #update self.df_ld + self.df_ld = df_ld + self.df_ld_snps = df_ld_snps + assert self.df_ld.notnull().all().all() + + + + def find_cached_ld_file(self, locus_start, locus_end, need_bcor=False): + + #if there's no cache dir, return None + if self.cache_dir is None: + return None + + if self.incl_samples is None: + fname_pattern = '%s.%d'%(os.path.basename(self.genotypes_file), self.chr) + else: + fname_pattern = '%s.%s.%d'%(os.path.basename(self.genotypes_file), os.path.basename(self.incl_samples), self.chr) + + #search for suitable LD files + bcor_files = glob.glob(os.path.join(self.cache_dir, fname_pattern+'*.bcor')) + npz_files = glob.glob(os.path.join(self.cache_dir, fname_pattern+'*.npz')) + if need_bcor: + ld_files = bcor_files + npz_files + else: + ld_files = npz_files + bcor_files + + for ld_file in ld_files: + if os.stat(ld_file).st_size==0: + os.remove(ld_file) + continue + ld_basename = os.path.basename(ld_file) + bp1 = int(ld_basename.split('.')[-3]) + bp2 = int(ld_basename.split('.')[-2]) + assert bp1 < bp2 + if (bp1 > locus_start) or (bp2 < locus_end): continue + + #get the list of SNPs in the LD file + if ld_file.endswith('.npz'): + meta_file = ld_file[:-4] + '.gz' + if not os.path.exists(meta_file): continue + df_ld_snps = pd.read_table(meta_file) + elif ld_file.endswith('.bcor'): + bcor_obj = bcor(ld_file) + df_ld_snps = bcor_obj.getMeta() + del bcor_obj + df_ld_snps.rename(columns={'rsid':'SNP', 'position':'BP', 'chromosome':'CHR', 'allele1':'A1', 'allele2':'A2'}, inplace=True, errors='raise') + ###df_ld_snps['CHR'] = df_ld_snps['CHR'].astype(np.int64) + df_ld_snps['BP'] = df_ld_snps['BP'].astype(np.int64) + else: + raise IOError('unknown file extension') + df_ld_snps = set_snpid_index(df_ld_snps, allow_swapped_indel_alleles=self.allow_swapped_indel_alleles) + + #make sure that the LD file includes data for all the SNPs in the locus + if not np.all(self.df_sumstats_locus.index.isin(df_ld_snps.index)): + logging.warning('The available cached LD file was ignored because it does not contain data for all the SNPs in the locus') + continue + + #if we got here than we found a suitable d file + logging.info('Found a cached LD file containing all SNPs with sumstats in chromosome %d BP %d-%d: %s'%(self.chr, locus_start, locus_end, ld_file)) + return ld_file + + + def get_ld_output_file_prefix(self, locus_start, locus_end, output_dir=None): + if self.cache_dir is None: + if output_dir is None: + output_dir = tempfile.mkdtemp() + output_prefix = os.path.join(output_dir, 'ld') + else: + if self.incl_samples is None: + output_prefix = os.path.join(self.cache_dir, '%s.%d.%d.%d'%(os.path.basename(self.genotypes_file), self.chr, locus_start, locus_end)) + else: + output_prefix = os.path.join(self.cache_dir, '%s.%s.%d.%d.%d'%(os.path.basename(self.genotypes_file), os.path.basename(self.incl_samples), self.chr, locus_start, locus_end)) + + return output_prefix + + + def compute_ld_bgen(self, locus_start, locus_end, verbose=False): + + #create df_z + df_z = self.df_sumstats_locus[['SNP', 'CHR', 'BP', 'A1', 'A2']].copy() + + #add leading zeros to chromosome numbers if needed + try: + import bgen + except (ImportError, ModuleNotFoundError): + raise ValueError('\n\nPlease install the bgen package (using "pip install bgen")') + from bgen.reader import BgenFile + bfile = BgenFile(self.genotypes_file) + bgen_chromosomes = bfile.chroms() + if bgen_chromosomes[0].startswith('0'): + df_z['CHR'] = '0' + df_z['CHR'].astype(str) + + + #sync the order of the alleles between the sumstats and the bgen file + list_bgen = [] + #rsids = bfile.rsids() + #small change reduces the time for bgen processing + #the previous implementation would iterate through all the SNPs in the bgen file + #this implementation loops over just the snps in the locus + rsids = bfile.fetch(self.chr, locus_start, locus_end) + for snp_i, rsid in enumerate(rsids): +# if rsid not in df_z['SNP'].values: continue +# snp_alleles = bfile[snp_i].alleles +# snp_chrom = bfile[snp_i].chrom +# snp_pos = bfile[snp_i].pos + if rsid.rsid not in df_z['SNP'].values: continue + snp_alleles = rsid.alleles + snp_chrom = rsid.chrom + snp_pos = rsid.pos + assert len(snp_alleles) == 2, 'cannot handle SNPs with more than two alleles' + df_snp = df_z.query('SNP == "%s"'%(rsid)) + assert df_snp.shape[0]==1 + a1, a2 = df_snp['A1'].iloc[0], df_snp['A2'].iloc[0] + snp_a1, snp_a2 = snp_alleles[0], snp_alleles[1] + if set([a1,a2]) != set([snp_a1, snp_a2]): + raise ValueError('The alleles for SNP %s are different in the sumstats and in the bgen file:\n \ + bgen: A1=%s A2=%s\n \ + sumstats: A1=%s A2=%s \ + '%(rsid, snp_alleles[0], snp_alleles[1], a1, a2)) + d = {'SNP':rsid, 'CHR':snp_chrom, 'BP':snp_pos, 'A1':snp_a1, 'A2':snp_a2} + list_bgen.append(d) + df_bgen = pd.DataFrame(list_bgen) + df_bgen = set_snpid_index(df_bgen, allow_swapped_indel_alleles=self.allow_swapped_indel_alleles) + df_z = set_snpid_index(df_z, allow_swapped_indel_alleles=self.allow_swapped_indel_alleles) + df_z = df_z[[]].merge(df_bgen, left_index=True, right_index=True) + df_z = df_z[['SNP', 'CHR', 'BP', 'A1', 'A2']] + + + #rename columns + df_z.rename(columns={'SNP':'rsid', 'CHR':'chromosome', 'BP':'position', 'A1':'allele1', 'A2':'allele2'}, inplace=True) + + #Create LDstore input files + temp_dir = tempfile.mkdtemp() + incl_file = os.path.join(temp_dir, 'incl.incl') + master_file = os.path.join(temp_dir, 'master.master') + z_file = os.path.join(temp_dir, 'chr%s.%s_%s.z'%(self.chr, locus_start, locus_end)) + dose_file = os.path.join(temp_dir, 'dosages.bdose') + df_z.to_csv(z_file, sep=' ', index=False) + + #find number of samples + if self.incl_samples is None: + num_samples = pd.read_table(self.sample_file).shape[0]-1 + else: + num_samples = pd.read_table(self.incl_samples, header=None).shape[0] + + #get output file name + bcor_file = self.get_ld_output_file_prefix(locus_start, locus_end, temp_dir) + '.bcor' + + #Create LDstore master file + df_master = pd.DataFrame(columns=['z','bgen','bgi','bcor','dose','sample','n_samples']) + df_master['z'] = [z_file] + df_master['bgen'] = [self.genotypes_file] + df_master['bgi'] = [self.genotypes_file+'.bgi'] + df_master['bcor'] = [bcor_file] + df_master['bdose'] = [dose_file] + df_master['sample'] = [self.sample_file] + df_master['n_samples'] = num_samples + if self.incl_samples is not None: + df_master['incl'] = self.incl_samples + df_master.to_csv(master_file, sep=';', header=True, index=False) + + #run LDstore + ldstore_cmd = [self.ldstore_exe, '--in-files', master_file, '--write-bcor', '--write-bdose', '--bdose-version', '1.0'] + if self.memory is not None: + ldstore_cmd += ['--memory', str(self.memory)] + if self.n_threads is not None: + ldstore_cmd += ['--n-threads', str(self.n_threads)] + run_executable(ldstore_cmd, 'LDStore', measure_time=True, show_output=verbose, show_command=verbose) + + if not os.path.exists(bcor_file): + raise IOError('Could not find output BCOR file') + + return bcor_file + + + def read_plink_genotypes(self, bed): + X = bed.compute().astype(np.float64) + if np.any(np.isnan(X)): + imp = SimpleImputer(missing_values=np.nan, strategy='mean', copy=False) + imp.fit(X) + X = imp.transform(X) + X -= X.mean(axis=0) + assert not np.any(np.isnan(X)) + is_polymorphic = X.std(axis=0)>0 + X[:, is_polymorphic] /= X[:, is_polymorphic].std(axis=0) + return X + + + def compute_ld_plink(self, locus_start, locus_end, verbose): + logging.info('Computing LD from plink fileset %s chromosome %s region %s-%s'%(self.genotypes_file, self.chr, locus_start, locus_end)) + t0 = time.time() + + #read the plink file + df_bim, df_fam, bed = read_plink(self.genotypes_file) + df_bim.rename(columns={'snp':'SNP', 'pos':'BP', 'chrom':'CHR', 'a0':'A2', 'a1':'A1'}, inplace=True) + df_bim['A1'] = df_bim['A1'].astype('str') + df_bim['A2'] = df_bim['A2'].astype('str') + df_bim['CHR'] = df_bim['CHR'].astype(np.int64) + del df_bim['i'] + del df_bim['cm'] + bed = bed.T + + #zoom in on target locus + is_snp_in_region = (df_bim['BP'].between(locus_start, locus_end)) & (df_bim['CHR']==self.chr) + df_bim = df_bim.loc[is_snp_in_region] + df_ld_snps = df_bim + bed = bed[:, is_snp_in_region.values] + assert bed.shape[1]>0, 'No SNPs found in the target region' + + #compute chunk size, using the formula MEM = bed.shape[0] * chunk_size * 4 / 2**30 + if self.memory is None: + mem_limit = 1 + else: + mem_limit = self.memory + chunk_size = np.int64((np.float64(mem_limit) * 0.8) / bed.shape[0] / 4 * (2**30)) + if chunk_size==0: chunk_size=1 + if chunk_size > bed.shape[1]: chunk_size = bed.shape[1] + num_chunks = np.int64(np.ceil(bed.shape[1] / chunk_size)) + if num_chunks>1: + assert chunk_size * (num_chunks-2) < bed.shape[1]-1 + if chunk_size * (num_chunks-1) >= bed.shape[1]: + num_chunks-=1 + + #compute LD in chunks + logging.info('Found %d SNPs in target region. Computing LD in %d chunks...'%(bed.shape[1], num_chunks)) + ld_arr = np.empty((bed.shape[1], bed.shape[1]), dtype=np.float64) + for chunk_i in tqdm(range(num_chunks)): + chunk_i_start = chunk_i*chunk_size + chunk_i_end = np.minimum(chunk_i_start+chunk_size, bed.shape[1]) + X_i = self.read_plink_genotypes(bed[:, chunk_i_start:chunk_i_end]) + ld_arr[chunk_i_start:chunk_i_end, chunk_i_start:chunk_i_end] = X_i.T.dot(X_i) / X_i.shape[0] + for chunk_j in range(chunk_i+1, num_chunks): + chunk_j_start = chunk_j*chunk_size + chunk_j_end = np.minimum(chunk_j_start+chunk_size, bed.shape[1]) + X_j = self.read_plink_genotypes(bed[:, chunk_j_start:chunk_j_end]) + ld_arr[chunk_i_start:chunk_i_end, chunk_j_start:chunk_j_end] = X_i.T.dot(X_j) / X_i.shape[0] + ld_arr[chunk_j_start:chunk_j_end, chunk_i_start:chunk_i_end] = ld_arr[chunk_i_start:chunk_i_end, chunk_j_start:chunk_j_end].T + ld_arr = np.nan_to_num(ld_arr, copy=False) + ld_diag = np.diag(ld_arr).copy() + if np.any(np.isclose(ld_diag, 0.0)): + ld_diag[np.isclose(ld_diag, 0.0)] = 1.0 + np.fill_diagonal(ld_arr, ld_diag) + + logging.info('Done in %0.2f seconds'%(time.time() - t0)) + return ld_arr, df_ld_snps + + + + def set_locus(self, locus_start, locus_end): + + #update self.df_sumstats_locus + self.df_sumstats_locus = self.df_sumstats.query('%d <= BP <= %d'%(locus_start, locus_end)) + num_snps = self.df_sumstats_locus.shape[0] + if num_snps < 2: + raise ValueError('%d SNP(s) found in sumstats file in the BP range %d-%d'%(num_snps, locus_start, locus_end)) + + + + def get_ld_data(self, locus_start, locus_end, need_bcor=False, verbose=False): + + ld_arr, df_ld_snps, ld_file = None, None, None + + #check if we already have a suitable LD file in the cache dir + ld_file = self.find_cached_ld_file(locus_start, locus_end, need_bcor=need_bcor) + + #compute LD if we couldn't find a suitable LD file + if ld_file is None: + if self.genotypes_file.endswith('.bgen'): + if not os.path.exists(self.genotypes_file): + raise IOError('%s doesn\'t exist'%(self.genotypes_file)) + ld_file = self.compute_ld_bgen(locus_start, locus_end, verbose=verbose) + elif os.path.exists(self.genotypes_file+'.bed'): + ld_arr, df_ld_snps = self.compute_ld_plink(locus_start, locus_end, verbose=verbose) + else: + raise ValueError('no suitable file found for: %s'%(self.genotypes_file)) + + #arrange the LD data + assert ld_file is None or (ld_arr is None and df_ld_snps is None) + + #if there is no LD file, return the LD data directly + if ld_file is None: + #cache output if possible + if self.cache_dir is not None: + npz_file = self.get_ld_output_file_prefix(locus_start, locus_end) + '.npz' + save_ld_to_npz(ld_arr, df_ld_snps, npz_file) + return ld_arr, df_ld_snps + + #if we have an LD file, return it if it's a bcor and we want a bcor, or return the LD data directly otherwise + else: + if ld_file.endswith('.bcor'): + if need_bcor: + return ld_file + else: + ld_arr, df_ld_snps = read_ld_from_file(ld_file) + + #cache output if possible + if self.cache_dir is not None and ld_file.endswith('.bcor'): + npz_file = self.get_ld_output_file_prefix(locus_start, locus_end) + '.npz' + save_ld_to_npz(ld_arr, df_ld_snps, npz_file) + return ld_arr, df_ld_snps + + else: + ld_arr, df_ld_snps = read_ld_from_file(ld_file) + return ld_arr, df_ld_snps + + + + def finemap(self): + raise NotImplementedError() + + def estimate_h2_hess(self, prop_keep=0.005, R_cutoff=0.99, pvalue_bound=None): + ''' + prop_keep: Proportion of SNPs to use in the estimation (only the ones with the smallest p-values) + R_cutoff: Exclude one of each pair of SNPs with with magnitude of correlation greater than this value + pvalue_bound: An upper bound on the p-value cutoff (i.e., SNPs with P greater than this cutoff will never be used in the estimation) + + The modified HESS equation implemented below is + + $$ \frac{n \alpha R^{-1} \alpha - m}{n} = \alpha R^{-1} \alpha - \frac{m}{n} $$ + + where $\alpha$ is a vector of marginal effect size estimates for $m$ standardized SNPs, + $R$ is a matrix of summary LD information, and $n$ is the sample size. + + This is a biased estimator (denominator of $n$) with a smaller estimation variance compared + with the unbiased estimator (denominator of $n - m$) used in the original HESS publication + (Shi et al., 2014; https://doi.org/10.1016/j.ajhg.2016.05.013). + ''' + + #keep only potential causal SNPs + pvalue_cutoff = self.df_sumstats_locus['P'].quantile(prop_keep) + if pvalue_cutoff==0: + pvalue_cutoff = np.min(self.df_sumstats_locus['P'].loc[lambda p:p>0]) + if pvalue_bound is not None and pvalue_cutoff>pvalue_bound: + pvalue_cutoff = pvalue_bound + is_potential_csnp = self.df_sumstats_locus['P'].valuesR_cutoff) + np.fill_diagonal(R_pot_csnp,1) + inds = np.sort(nx.maximal_independent_set(G)) + + #estimate h2 using HESS + R_subset = R_pot_csnp[np.ix_(inds, inds)] + alpha_subset = self.df_sumstats_locus.loc[is_potential_csnp, 'Z'].iloc[inds].values / np.sqrt(self.n) + h2_hess = alpha_subset.dot(np.linalg.solve(R_subset, alpha_subset)) - R_subset.shape[0]/self.n + + return h2_hess + + + def estimate_h2_hess_wrapper(self, prop_keep=0.005, R_cutoff=0.99, min_h2=None, num_samples=100): + ''' + prop_keep: Proprtion of SNPs to use in the estimation (only the ones with the smallest p-values) + R_cutoff: Exclude one of each pair of SNPs with with magnitude of correlation greater than this value + min_h2: Exclude SNPs that tag less than this amount of heritability + num_samples: Number of random samples of indepdendent SNPs to draw + ''' + + if min_h2 is None: + pvalue_bound = None + else: + assert min_h2 > 0 and min_h2 < 1, \ + 'The minimum proportion of heritability to exclude SNPs from HESS estimation must be between 0 and 1' + pvalue_bound = stats.chi2(1).sf(min_h2 * self.n) + + assert num_samples > 0, 'Number of random samples must be a positive integer' + + h2_hess_list = [self.estimate_h2_hess(prop_keep=prop_keep, R_cutoff=R_cutoff, pvalue_bound=pvalue_bound) \ + for try_num in range(num_samples)] + h2_hess = np.mean(h2_hess_list) + return h2_hess + + + + + + +class SUSIE_Wrapper(Fine_Mapping): + + def __init__(self, genotypes_file, sumstats_file, n, chr_num, ldstore_exe, sample_file=None, + incl_samples=None, cache_dir=None, n_threads=None, memory=None, + allow_swapped_indel_alleles=False): + + super(SUSIE_Wrapper, self).__init__(genotypes_file, sumstats_file, n, chr_num, ldstore_exe=ldstore_exe, sample_file=sample_file, incl_samples=incl_samples, cache_dir=cache_dir, n_threads=n_threads, memory=memory, allow_swapped_indel_alleles=allow_swapped_indel_alleles) + + #load SuSiE R package + import rpy2 + import rpy2.robjects.numpy2ri as numpy2ri + import rpy2.robjects as ro + ro.conversion.py2ri = numpy2ri + numpy2ri.activate() + from rpy2.robjects.packages import importr + self.susieR = importr('susieR') + self.R_null = ro.rinterface.NULL + #self.RNULLType = rpy2.rinterface.RNULLType + + + + + def finemap(self, locus_start, locus_end, num_causal_snps, use_prior_causal_prob=True, prior_var=None, residual_var=None, residual_var_init=None, hess_resvar=False, hess=False, hess_iter=100, hess_min_h2=None, susie_max_iter=100, verbose=False, ld_file=None, debug_dir=None, allow_missing=False, susie_outfile=None, finemap_dir=None): + + #check params + if use_prior_causal_prob and 'SNPVAR' not in self.df_sumstats.columns: + raise ValueError('SNPVAR column not found in sumstats file') + if hess_resvar: + assert hess, 'hess_resvar cannot be specified if hess is FALSE' + + #set locus + self.set_locus(locus_start, locus_end) + + #download LD file if it's a url + if uri_validator(ld_file): + ld_file = download_ld_file(ld_file) + delete_ld_files_on_exit = True + else: + delete_ld_files_on_exit = False + + #Load LD data into memory if num_causal_snps>1 + if num_causal_snps==1: + if hess: + raise ValueError('Cannot use HESS-based variance estimator when assuming a single causal SNP per locus') + self.df_ld = pd.DataFrame(np.eye(self.df_sumstats_locus.shape[0]), index=self.df_sumstats_locus.index, columns=self.df_sumstats_locus) + self.df_ld_snps = self.df_sumstats_locus + else: + if ld_file is None: + ld_arr, df_ld_snps = self.get_ld_data(locus_start, locus_end, need_bcor=False, verbose=verbose) + else: + ld_arr, df_ld_snps = read_ld_from_file(ld_file) + assert np.all(~np.isnan(ld_arr)) + self.sync_ld_sumstats(ld_arr, df_ld_snps, allow_missing=allow_missing) + del ld_arr + del df_ld_snps + + #define prior causal probabilities + if use_prior_causal_prob: + prior_weights = self.df_sumstats_locus['SNPVAR'].copy().values + prior_weights /= prior_weights.sum() + assert np.isclose(prior_weights.sum(), 1) + + #flip effect sizes if needed + assert np.all(self.df_ld_snps['BP'] == self.df_sumstats_locus['BP']) + is_flipped = self.df_ld_snps['A1'] == self.df_sumstats_locus['A2'] + is_not_flipped = self.df_ld_snps['A1'] == self.df_sumstats_locus['A1'] + assert np.all(is_flipped | is_not_flipped) + bhat = self.df_sumstats_locus['Z'].values.copy() + if np.any(is_flipped): + bhat[is_flipped.values] *= -1 + logging.info('Flipping the effect-sign of %d SNPs that are flipped compared to the LD panel'%(is_flipped.sum())) + + + #Use HESS to estimate causal effect sizes + if hess: + if prior_var is not None: + raise ValueError('cannot specify both hess and a custom prior_var') + if self.n < 20000: + logging.warning('HESS method is intended for studies with large sample sizes (i.e. >20K)') + if hess_min_h2 is None: + logging.warning('For best results, you should consider setting --hess-min-h2 to exclude SNPs with low heritability from the HESS estimation. You will need to experiment with your data to find a suitable heritability threshold. To start, try --hess-min-h2 1e-4') + else: + logging.info('Excluding SNPs with heritability less than %0.4e from the HESS estimation'%(hess_min_h2)) + h2_hess = self.estimate_h2_hess_wrapper(min_h2=hess_min_h2, num_samples=hess_iter) + logging.info('Average local SNP heritability estimated by modified HESS over %d iterations: %0.4e'%(hess_iter, h2_hess)) + if h2_hess > 10: + logging.warning('The HESS estimator is unconstrained, and the estimate is an order of magnitude greater than the expected max of 1. Use with caution') + prior_var = h2_hess / num_causal_snps + if prior_var <= 0: + raise ValueError('HESS estimates that the locus causally explains zero heritability') + if prior_var >= 1: + raise ValueError('HESS-estimated prior-var >1. The HESS estimator cannot be used in this locus.') + logging.info('HESS estimated causal effect size variance: %0.4e'%(prior_var)) + + if hess_resvar: + residual_var = 1 - h2_hess + logging.info('Residual variance using the HESS estimate: %0.4e'%(residual_var)) + assert residual_var>=0 + + #rpy2 bug fix + import rpy2.robjects.numpy2ri as numpy2ri + reload(numpy2ri) + numpy2ri.activate() + + #run SuSiE + t0 = time.time() + m = self.df_sumstats_locus.shape[0] + logging.info('Starting %s SuSiE fine-mapping for chromosome %d BP %d-%d (%d SNPs)'%( + ('functionally-informed' if use_prior_causal_prob else 'non-functionally informed'), + self.chr, + locus_start, + locus_end, + self.df_ld.shape[0] + )) + + #save variables to debug dir if needed + if debug_dir is not None: + os.makedirs(debug_dir, exist_ok=True) + logging.info('Saving debug info to: %s'%(debug_dir)) + self.df_sumstats_locus.to_csv(os.path.join(debug_dir, 'df_sumstats_locus.txt'), index=False, sep='\t') + np.savetxt(os.path.join(debug_dir, 'bhat.txt'), bhat) + #np.savez_compressed(os.path.join(debug_dir, 'R.npz'), R=self.df_ld.values) + np.savetxt(os.path.join(debug_dir, 'n.txt'), [self.n]) + np.savetxt(os.path.join(debug_dir, 'L.txt'), [num_causal_snps]) + np.savetxt(os.path.join(debug_dir, 'residual_var.txt'), [np.nan] if (residual_var is None) else [residual_var]) + np.savetxt(os.path.join(debug_dir, 'prior_var.txt'), [np.nan] if (prior_var is None) else [prior_var]) + np.savetxt(os.path.join(debug_dir, 'prior_weights.txt'), prior_weights if use_prior_causal_prob else [np.nan]) + + #create a zipped debug file + import zipfile + debug_files = glob.glob(os.path.join(debug_dir, '*.txt')) + zip_file = os.path.join(debug_dir, 'debug.zip') + zf = zipfile.ZipFile(zip_file, mode='w') + for debug_file in debug_files: + zf.write(debug_file, os.path.basename(debug_file), compress_type=zipfile.ZIP_DEFLATED) + + + assert self.df_ld.notnull().all().all() + if residual_var is not None: residual_var_init = residual_var + + if hasattr(self.susieR, 'susie_suff_stat'): + logging.info('Using susieR::susie_suff_stat()') + susie_obj = self.susieR.susie_suff_stat( + bhat=bhat.reshape((m,1)), + shat=np.ones((m,1)), + R=self.df_ld.values, + n=self.n, + L=num_causal_snps, + scaled_prior_variance=(0.0001 if (prior_var is None) else prior_var), + estimate_prior_variance=(prior_var is None), + residual_variance=(self.R_null if (residual_var_init is None) else residual_var_init), + estimate_residual_variance=(residual_var is None), + max_iter=susie_max_iter, + verbose=verbose, + prior_weights=(prior_weights.reshape((m,1)) if use_prior_causal_prob else self.R_null) + ) + elif hasattr(self.susieR, 'susie_bhat'): + logging.info('Using susieR::susie_bhat()') + susie_obj = self.susieR.susie_bhat( + bhat=bhat.reshape((m,1)), + shat=np.ones((m,1)), + R=self.df_ld.values, + n=self.n, + L=num_causal_snps, + scaled_prior_variance=(0.0001 if (prior_var is None) else prior_var), + estimate_prior_variance=(prior_var is None), + residual_variance=(self.R_null if (residual_var is None) else residual_var), + estimate_residual_variance=(residual_var is None), + max_iter=susie_max_iter, + verbose=verbose, + prior_weights=(prior_weights.reshape((m,1)) if use_prior_causal_prob else self.R_null) + ) + else: + raise NotImplementedError('Only susie_suff_stat() and susie_bhat() are supported. Check your version of susieR') + susie_time = time.time()-t0 + logging.info('Done in %0.2f seconds'%(susie_time)) + + #extract pip and beta_mean + pip = np.array(self.susieR.susie_get_pip(susie_obj)) + beta_mean = np.array(self.susieR.coef_susie(susie_obj)[1:]) + assert np.allclose(beta_mean, np.sum(np.array(susie_obj.rx2('mu')) * np.array(susie_obj.rx2('alpha')), axis=0) / np.array(susie_obj.rx2('X_column_scale_factors'))) + + #compute the posterior mean of beta^2 + s_alpha = np.array(susie_obj.rx2('alpha')) + s_mu = np.array(susie_obj.rx2('mu')) + s_mu2 = np.array(susie_obj.rx2('mu2')) + s_X_column_scale_factors = np.array(susie_obj.rx2('X_column_scale_factors')) + beta_var = np.sum(s_alpha*s_mu2 - (s_alpha*s_mu)**2, axis=0) / (s_X_column_scale_factors**2) + assert np.all(beta_var>=0) + + #create output df + df_susie = self.df_sumstats_locus.copy() + df_susie['PIP'] = pip + df_susie['BETA_MEAN'] = beta_mean + # flip back the finemap BETA, as the alleles are in original order + df_susie.loc[is_flipped, 'BETA_MEAN'] *= (-1) + df_susie['BETA_SD'] = np.sqrt(beta_var) + + #add distance from center + start = df_susie['BP'].min() + end = df_susie['BP'].max() + middle = (start+end)//2 + df_susie['DISTANCE_FROM_CENTER'] = np.abs(df_susie['BP'] - middle) + + #mark causal sets + import rpy2 + logging.info('Using rpy2 version %s'%(rpy2.__version__)) + if Version(rpy2.__version__) >= Version('3.5.9'): + snames = (susie_obj.names).tolist() + self.susie_dict = {key: np.array(susie_obj.rx2(key), dtype=object) for key in snames} + else: + self.susie_dict = {key:np.array(susie_obj.rx2(key), dtype=object) for key in list(susie_obj.names)} + df_susie['CREDIBLE_SET'] = 0 + susie_sets = self.susie_dict['sets'][0] + #if type(susie_sets) != self.RNULLType: + try: + for set_i, susie_set in enumerate(susie_sets): + is_in_set = np.zeros(df_susie.shape[0], dtype=bool) + is_in_set[np.array(susie_set)-1] = True + is_in_set[df_susie['CREDIBLE_SET']>0] = False + df_susie.loc[is_in_set, 'CREDIBLE_SET'] = set_i+1 + except TypeError: + pass + + + #save SuSiE object if requested + if susie_outfile is not None: + from rpy2.robjects.packages import importr + R_base = importr('base', robject_translations = {'print.me': 'print_dot_me', 'print_me': 'print_uscore_me'}) + R_base.saveRDS(susie_obj, file=susie_outfile) + logging.info('Saved SuSiE object to RDS file: %s'%(susie_outfile)) + + + #delete the LD file if needed + if delete_ld_files_on_exit: + ld_file_dir = os.path.dirname(ld_file) + if os.path.exists(ld_file_dir): shutil.rmtree(ld_file_dir) + + return df_susie + + + + + + + +class FINEMAP_Wrapper(Fine_Mapping): + + def __init__(self, genotypes_file, sumstats_file, n, chr_num, finemap_exe, ldstore_exe, sample_file=None, + incl_samples=None, cache_dir=None, n_threads=None, memory=None, + allow_swapped_indel_alleles=False): + + super(FINEMAP_Wrapper, self).__init__(genotypes_file, sumstats_file, n, chr_num, ldstore_exe=ldstore_exe, sample_file=sample_file, incl_samples=incl_samples, cache_dir=cache_dir, n_threads=n_threads, memory=memory, allow_swapped_indel_alleles=allow_swapped_indel_alleles) + self.finemap_exe = finemap_exe + + + + def finemap(self, locus_start, locus_end, num_causal_snps, use_prior_causal_prob=True, prior_var=None, residual_var=None, hess=False, hess_iter=100, hess_min_h2=None, susie_max_iter=100, verbose=False, ld_file=None, debug_dir=None, allow_missing=False, susie_outfile=None, residual_var_init=None, hess_resvar=False, finemap_dir=None): + + #check params + if use_prior_causal_prob and 'SNPVAR' not in self.df_sumstats.columns: + raise ValueError('SNPVAR column not found in sumstats file') + if hess: + raise ValueError('FINEMAP cannot be used with a HESS-based variance estimator') + if residual_var is not None: + raise ValueError('cannot specify residual_var for FINEMAP') + if debug_dir is not None: + raise NotImplementedError('FINEMAP object does not support --debug-dir') + if hess_resvar: + raise NotImplementedError('FINEMAP object does not support --susie-resvar-hess') + if residual_var_init is not None: + raise NotImplementedError('FINEMAP object does not support --susie-resvar-init') + # if allow_missing: + # raise ValueError('FINEMAP object does not support --allow-missing') + + #download LD file if it's a url + if uri_validator(ld_file): + ld_file = download_ld_file(ld_file) + + #create prefix of output files + if finemap_dir is None: + finemap_dir = tempfile.mkdtemp() + else: + os.makedirs(finemap_dir, exist_ok=True) + logging.info('Saving FINEMAP files to directory: %s'%(finemap_dir)) + assert os.path.isdir(finemap_dir) + finemap_output_prefix = os.path.join(finemap_dir, 'finemap') + + + #set locus + self.set_locus(locus_start, locus_end) + + #find or create a suitable ld_file + if num_causal_snps==1: + if ld_file is not None: + raise ValueError('cannot specify an ld file when assuming a single causal SNP per locus') + ld_file = finemap_output_prefix+'.ld' + np.savetxt(ld_file, np.eye(self.df_sumstats_locus.shape[0], dtype=np.int64), fmt='%s') + else: + if ld_file is None: + ld_data = self.get_ld_data(locus_start, locus_end, need_bcor=True, verbose=verbose) + if isinstance(ld_data, str): + ld_file = ld_data + assert ld_file.endswith('.bcor') + assert os.path.exists(ld_file) + elif isinstance(ld_data, tuple): + assert len(ld_data)==2 + ld_arr, df_ld_snps = ld_data[0], ld_data[1] + self.sync_ld_sumstats(ld_arr, df_ld_snps, allow_missing=allow_missing) + del ld_arr, df_ld_snps + ld_file = finemap_output_prefix + '.ld' + np.savetxt(ld_file, self.df_ld.values, fmt='%0.5f') + elif ld_file.endswith('.bcor'): + pass + elif ld_file.endswith('.npz') or os.path.exists(ld_file+'.npz'): + ld_arr, df_ld_snps = read_ld_from_file(ld_file) + self.sync_ld_sumstats(ld_arr, df_ld_snps, allow_missing=allow_missing) + del ld_arr, df_ld_snps + ld_file = finemap_output_prefix + '.ld' + np.savetxt(ld_file, self.df_ld.values, fmt='%0.5f') + else: + raise ValueError('unknown LD file format for file: %s'%(ld_file)) + + + #define file names + master_file = finemap_output_prefix+'.master' + snp_filename = finemap_output_prefix+'.snp' + config_filename = finemap_output_prefix+'.config' + cred_filename = finemap_output_prefix+'.cred' + log_filename = finemap_output_prefix+'.log' + z_filename = finemap_output_prefix+'.z' + + #flip some of the alleles + if num_causal_snps == 1: + is_flipped = np.zeros(self.df_sumstats_locus.shape[0], dtype=bool) + else: + if ld_file.endswith('.bcor'): + bcor_obj = bcor(ld_file) + df_ld_snps = get_bcor_meta(bcor_obj) + self.sync_ld_sumstats(None, df_ld_snps, allow_missing=allow_missing) + del df_ld_snps + assert np.all(self.df_ld_snps['BP'] == self.df_sumstats_locus['BP']) + is_flipped = self.df_ld_snps['A1'] == self.df_sumstats_locus['A2'] + is_not_flipped = self.df_ld_snps['A1'] == self.df_sumstats_locus['A1'] + assert np.all(is_flipped | is_not_flipped) + if np.any(is_flipped): + logging.info('Flipping the effect-sign of %d SNPs that are flipped compared to the LD panel'%(is_flipped.sum())) + + #create df_z and save it to disk + df_z = self.df_sumstats_locus[['SNP', 'CHR', 'BP', 'A1', 'A2', 'Z']].copy() + df_z.loc[is_flipped, 'A1'] = self.df_sumstats_locus.loc[is_flipped, 'A2'] + df_z.loc[is_flipped, 'A2'] = self.df_sumstats_locus.loc[is_flipped, 'A1'] + df_z.loc[is_flipped, 'Z'] *= (-1) + + df_z.rename(columns={'SNP':'rsid', 'CHR':'chromosome', 'BP':'position', 'A1':'allele1', 'A2':'allele2', 'Z':'beta'}, inplace=True, errors='ignore') + df_z['se'] = 1 + df_z['maf'] = 0.05 + df_z = df_z[['rsid', 'chromosome', 'position', 'allele1', 'allele2', 'maf', 'beta', 'se']] + if use_prior_causal_prob: + df_z['prob'] = self.df_sumstats_locus['SNPVAR'] / self.df_sumstats_locus['SNPVAR'].sum() + df_z.to_csv(z_filename, header=True, index=False, sep=' ', float_format='%0.5f') + + #create the master file + df_master = pd.DataFrame() + df_master['z'] = [z_filename] + df_master['snp'] = [snp_filename] + df_master['config'] = [config_filename] + df_master['cred'] = [cred_filename] + df_master['log'] = [log_filename] + df_master['n_samples'] = [self.n] + if ld_file.endswith('.bcor'): + df_master['bcor'] = [ld_file] + elif ld_file.endswith('.ld'): + df_master['ld'] = [ld_file] + else: + raise ValueError('Illegal LD file format') + df_master.to_csv(master_file, sep=';', header=True, index=False) + + #prepare the FINEMAP command + finemap_cmd = [self.finemap_exe] + finemap_cmd += ['--in-files', master_file, '--sss'] + finemap_cmd += ['--force-n-samples'] + finemap_cmd += ['--log', log_filename] + finemap_cmd += ['--n-causal-snps', str(num_causal_snps)] + finemap_cmd += ['--std-effects'] + ###finemap_cmd += ['--flip-beta'] + if self.n_threads is not None: + finemap_cmd += ['--n-threads', str(self.n_threads)] + if prior_var is not None: + finemap_cmd += ['--prior-std', str(np.sqrt(prior_var))] + if use_prior_causal_prob: + finemap_cmd += ['--prior-snps'] + + #run FINEMAP + t0 = time.time() + m = self.df_sumstats_locus.shape[0] + logging.info('Starting %s FINEMAP fine-mapping for chromosome %d BP %d-%d (%d SNPs)'%( + ('functionally-informed' if use_prior_causal_prob else 'non-functionally informed'), + self.chr, + locus_start, + locus_end, + self.df_sumstats_locus.shape[0] + )) + run_executable(finemap_cmd, 'FINEMAP', measure_time=True, show_output=verbose, show_command=verbose) + if not os.path.exists(log_filename+'_sss'): + raise IOError('FINEMAP output files not found') + + #load log file + found_post_csnps = False + with open(log_filename+'_sss') as f: + for line in f: + if line.startswith('- Post-expected # of causal SNPs'): + post_mean_num_csnps = float(line[line.index(': ')+2:-1]) + found_post_csnps = True + break + if not found_post_csnps: + raise IOError('corrupt log file found: %s'%(log_filename+'_sss')) + + #load results + df_finemap = pd.read_table(snp_filename, sep=' ', usecols=['rsid', 'chromosome', 'position', 'allele1', 'allele2', 'prob', 'mean', 'sd']) + df_finemap.rename(columns={'rsid':'SNP', 'position':'BP', 'chromosome':'CHR', 'prob':'PIP', 'mean':'BETA_MEAN', 'sd':'BETA_SD', 'allele1':'A1', 'allele2':'A2'}, inplace=True, errors='raise') + + #read log10bf + log10bf = None + with open(log_filename+'_sss', 'r') as f: + for line in f: + if line.startswith('- Log10-BF'): + log10bf = float(line.split()[-1]) + break + if log10bf is None: + raise ValueError('FINEMP did not report Log10-BF') + + #add distance from center + start = df_finemap['BP'].min() + end = df_finemap['BP'].max() + middle = (start+end)//2 + df_finemap['DISTANCE_FROM_CENTER'] = np.abs(df_finemap['BP'] - middle) + + #add causal set info + df_finemap['CREDIBLE_SET'] = 0 + cred_file = None + for m in range(num_causal_snps, 0, -1): + if os.path.exists(cred_filename+str(m)): + cred_file = cred_filename+str(m) + break + if cred_file is None: + raise IOError('cred file not found') + df_cred = pd.read_table(cred_file, sep=' ', usecols=(lambda c: c.startswith('cred')), comment='#') + df_finemap.set_index('SNP', inplace=True, drop=False) + for c_i, c in enumerate(df_cred.columns): + df_finemap.loc[df_cred[c].dropna(), 'CREDIBLE_SET'] = c_i+1 + df_finemap.reset_index(inplace=True, drop=True) + + finemap_time = time.time()-t0 + logging.info('Done in %0.2f seconds'%(finemap_time)) + + + return df_finemap + + + + + + + +if __name__ == '__main__': + + import argparse + parser = argparse.ArgumentParser() + + #general parameters + parser.add_argument('--method', required=True, help='Fine-mapping method (currently susie and finemap are supported)') + parser.add_argument('--sumstats', required=True, help='Name of sumstats file') + parser.add_argument('--chr', required=True, type=int, help='Target chromosome') + parser.add_argument('--start', required=True, type=int, help='First base-pair in the region to finemap') + parser.add_argument('--end', required=True, type=int, help='Last base-pair in the region to finemap') + parser.add_argument('--n', required=True, type=int, help='Sample size') + parser.add_argument('--geno', default=None, help='Genotypes file (plink or bgen format)') + parser.add_argument('--ld', default=None, help='prefix or fill name of an LD matrix file') + parser.add_argument('--out', required=True, help='name of the output file') + parser.add_argument('--verbose', action='store_true', default=False, help='If specified, show verbose output') + parser.add_argument('--debug-dir', default=None, help='If specified, this is a path of a directory that will include files for debugging problems') + parser.add_argument('--sample-file', default=None, help='BGEN files must be used together with a sample file') + parser.add_argument('--incl-samples', default=None, help='A single-column text file specifying the ids of individuals to include in fine-mapping') + + #fine-mapping parameters + parser.add_argument('--max-num-causal', required=True, type=int, help='Number of causal SNPs') + parser.add_argument('--non-funct', action='store_true', default=False, help='Perform non-functionally informed fine-mapping') + parser.add_argument('--allow-missing', default=False, action='store_true', help='If specified, SNPs with sumstats that are not \ + found in the LD panel will be omitted. This is not recommended, because the omitted SNPs may be causal,\ + which could lead to false positive results') + parser.add_argument('--allow-swapped-indel-alleles', default=False, action='store_true', + help='If specified, indels whose alleles are swapped between the sumstats and LD matrix \ + are kept for fine-mapping. The default behavior considers indels at the same position \ + with swapped alleles to be different variants, and thus removes them. Use with caution. \ + This is intended for use only when you are confident that the indels are identical, \ + e.g. when using insample LD') + parser.add_argument('--no-sort-pip', default=False, action='store_true', + help='Do **not** sort results by PIP. Recommended for use with --susie-outfile') + + #LDstore related parameters + parser.add_argument('--ldstore2', default=None, help='Path to an LDstore 2.0 executable file') + parser.add_argument('--finemap-exe', default=None, help='Path to FINEMAP v1.4 executable file') + parser.add_argument('--memory', type=int, default=1, help='Maximum amount of memory in GB to allocate to LDStore') + parser.add_argument('--threads', type=int, default=None, help='The number of CPU cores LDstore will use (if not specified, LDstore will use the max number of CPU cores available') + parser.add_argument('--cache-dir', default=None, help='If specified, this is a path of a directory that will cache LD matrices that have already been computed') + + # FINEMAP-specific parameters + parser.add_argument('--finemap-dir', default=None, help='If specified, the FINEMAP files will be saved to this directory') + + # SuSiE-specific parameters + parser.add_argument('--susie-outfile', default=None, help='If specified, the SuSiE object will be saved to an output file (also see --no-sort-pip for help merging with main output file)') + parser.add_argument('--susie-resvar', default=None, type=float, help='If specified, SuSiE will use this value of the residual variance') + parser.add_argument('--susie-resvar-init', default=None, type=float, help='If specified, SuSiE will use this initial value of the residual variance') + parser.add_argument('--susie-resvar-hess', default=False, action='store_true', help='If specified, SuSiE will specify the residual variance using the HESS estimate') + parser.add_argument('--susie-max-iter', default=100, type=int, help='SuSiE argument max_iter which controls the max number of IBSS iterations to perform (default: 100)') + parser.add_argument('--hess', action='store_true', default=False, help='If specified, estimate causal effect variance via HESS') + parser.add_argument('--hess-iter', type=int, default=100, help='Average HESS over this number of iterations (default: 100)') + parser.add_argument('--hess-min-h2', type=float, default=None, help='When estimating causal effect variance via HESS, exclude SNPs that tag less than this amount of heritability (default: None)') + + #check package versions + check_package_versions() + + #show splash screen + splash_screen() + + #extract args + args = parser.parse_args() + + #check that the output directory exists + if len(os.path.dirname(args.out))>0 and not os.path.exists(os.path.dirname(args.out)): + raise ValueError('output directory %s doesn\'t exist'%(os.path.dirname(args.out))) + if args.susie_outfile is not None and len(os.path.dirname(args.susie_outfile))>0 and not os.path.exists(os.path.dirname(args.susie_outfile)): + raise ValueError('output directory %s doesn\'t exist'%(os.path.dirname(args.susie_outfile))) + + #configure logger + configure_logger(args.out) + + #check params + if args.max_num_causal==1: + if args.geno is not None or args.ld is not None: + raise ValueError('When max_num_causal=1 fine-mapping please omit the flags --geno and --ld (we cannot use LD information in that setting)') + else: + if args.geno is None: + if args.ld is None: + raise ValueError('must specify either --geno or --ld') + if args.ldstore2 is not None: + raise ValueError('cannot specify both --ld and --ldstore2') + if args.geno is not None: + if args.ld is not None: + raise ValueError('cannot specify both --geno and --ld') + if args.geno.endswith('.bgen') and args.ldstore2 is None: + raise ValueError('You must specify --ldstore2 when --geno that points to a bgen file') + + if args.susie_outfile is not None and not args.no_sort_pip: + logging.warning('--susie-outfile was set but not --no-sort-pip. This will make it difficult to assign SNP names to the SuSiE R object') + + #Create a fine-mapping class member + if args.method == 'susie': + if args.finemap_dir is not None: + raise ValueError('--finemap-dir cannot be specified with susie method') + finemap_obj = SUSIE_Wrapper(genotypes_file=args.geno, sumstats_file=args.sumstats, n=args.n, chr_num=args.chr, + sample_file=args.sample_file, incl_samples=args.incl_samples, + ldstore_exe=args.ldstore2, n_threads=args.threads, + cache_dir=args.cache_dir, memory=args.memory, + allow_swapped_indel_alleles=args.allow_swapped_indel_alleles) + elif args.method == 'finemap': + if args.susie_outfile is not None: + raise ValueError('--susie-outfile cannot be specified with finemap method') + if args.finemap_exe is None: + raise ValueError('need to specify --finemap-exe') + if args.hess: + raise ValueError('FINEMAP cannot be used with --hess') + finemap_obj = FINEMAP_Wrapper(genotypes_file=args.geno, sumstats_file=args.sumstats, n=args.n, chr_num=args.chr, + sample_file=args.sample_file, incl_samples=args.incl_samples, + ldstore_exe=args.ldstore2, finemap_exe=args.finemap_exe, n_threads=args.threads, + cache_dir=args.cache_dir, memory=args.memory, + allow_swapped_indel_alleles=args.allow_swapped_indel_alleles) + else: + raise ValueError('unknown method specified in --method') + + #run fine-mapping + df_finemap = finemap_obj.finemap(locus_start=args.start, locus_end=args.end, num_causal_snps=args.max_num_causal, + use_prior_causal_prob=not args.non_funct, prior_var=None, + hess=args.hess, hess_iter=args.hess_iter, hess_min_h2=args.hess_min_h2, + verbose=args.verbose, ld_file=args.ld, debug_dir=args.debug_dir, allow_missing=args.allow_missing, + susie_outfile=args.susie_outfile, finemap_dir=args.finemap_dir, + residual_var=args.susie_resvar, residual_var_init=args.susie_resvar_init, hess_resvar=args.susie_resvar_hess, + susie_max_iter=args.susie_max_iter) + logging.info('Writing fine-mapping results to %s'%(args.out)) + if not args.no_sort_pip: + df_finemap.sort_values('PIP', ascending=False, inplace=True) + if args.out.endswith('.parquet'): + df_finemap.to_parquet(args.out, index=False) + else: + df_finemap.to_csv(args.out, sep='\t', index=False, float_format='%0.5e') + + + + diff --git a/SCRIPTS/polyfun/gold/finemap.1.46000001.49000001.gz b/SCRIPTS/polyfun/gold/finemap.1.46000001.49000001.gz new file mode 100644 index 0000000..0fe8d0f Binary files /dev/null and b/SCRIPTS/polyfun/gold/finemap.1.46000001.49000001.gz differ diff --git a/SCRIPTS/polyfun/gold/finemap.FINEMAP.1.46000001.49000001.gz b/SCRIPTS/polyfun/gold/finemap.FINEMAP.1.46000001.49000001.gz new file mode 100644 index 0000000..655de12 Binary files /dev/null and b/SCRIPTS/polyfun/gold/finemap.FINEMAP.1.46000001.49000001.gz differ diff --git a/SCRIPTS/polyfun/gold/polyloc_test.22.bins.parquet b/SCRIPTS/polyfun/gold/polyloc_test.22.bins.parquet new file mode 100644 index 0000000..496296d Binary files /dev/null and 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+78 565 50.71053 +79 591 49.83493 +80 618 49.79373 +81 644 49.61035 +82 671 66.33107 +83 713 77.11880 +84 756 74.41233 +85 799 72.83691 +86 841 71.57549 +87 884 69.52399 +88 926 68.49220 +89 969 68.59381 +90 1033 119.39731 +91 1110 112.01095 +92 1187 106.60509 +93 1265 102.19352 +94 1342 99.38704 +95 1426 121.32088 +96 1524 108.96018 +97 1622 96.51070 +98 1720 87.79911 +99 1818 79.83936 +100 2526 0.00000 diff --git a/SCRIPTS/polyfun/gold/polyloc_test.bin_h2 b/SCRIPTS/polyfun/gold/polyloc_test.bin_h2 new file mode 100644 index 0000000..3eb2705 --- /dev/null +++ b/SCRIPTS/polyfun/gold/polyloc_test.bin_h2 @@ -0,0 +1,12 @@ +BIN BIN_SIZE SUM_BINSIZE %H2 %H2_STDERR SUM_%H2 SUM_%H2_STDERR +1 10 10 0.17298 0.01021 0.17298 0.01021 +2 32 42 0.13545 0.01885 0.30843 0.01981 +3 70 112 0.12676 0.01786 0.43520 0.02329 +4 110 222 0.13693 0.01392 0.57212 0.02193 +5 193 415 0.15151 0.01396 0.72363 0.02113 +6 255 670 0.09629 0.01073 0.81992 0.01869 +7 315 985 0.07396 0.00855 0.89388 0.01589 +8 410 1395 0.05305 0.00843 0.94694 0.01276 +9 492 1887 0.05015 0.00878 0.99709 0.00788 +10 639 2526 0.00291 0.00788 1.00000 0.00000 +11 949 3475 0.00000 0.00000 1.00000 0.00000 diff --git a/SCRIPTS/polyfun/gold/snps_with_var.gz b/SCRIPTS/polyfun/gold/snps_with_var.gz new file mode 100644 index 0000000..57f3e75 Binary files /dev/null and b/SCRIPTS/polyfun/gold/snps_with_var.gz differ diff --git a/SCRIPTS/polyfun/gold/sumstats_munged.parquet b/SCRIPTS/polyfun/gold/sumstats_munged.parquet new file mode 100644 index 0000000..f2da0df Binary files /dev/null and b/SCRIPTS/polyfun/gold/sumstats_munged.parquet differ diff --git a/SCRIPTS/polyfun/gold/testrun.22.bins.parquet b/SCRIPTS/polyfun/gold/testrun.22.bins.parquet new file mode 100644 index 0000000..21fc380 Binary files /dev/null and b/SCRIPTS/polyfun/gold/testrun.22.bins.parquet differ diff --git a/SCRIPTS/polyfun/gold/testrun.22.l2.M b/SCRIPTS/polyfun/gold/testrun.22.l2.M new file mode 100644 index 0000000..f069368 --- /dev/null +++ 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b/SCRIPTS/polyfun/gold/testrun.22.snpvar_ridge_constrained.gz new file mode 100644 index 0000000..b6bc7d0 Binary files /dev/null and b/SCRIPTS/polyfun/gold/testrun.22.snpvar_ridge_constrained.gz differ diff --git a/SCRIPTS/polyfun/ldsc.py b/SCRIPTS/polyfun/ldsc.py new file mode 100644 index 0000000..0ac093c --- /dev/null +++ b/SCRIPTS/polyfun/ldsc.py @@ -0,0 +1,709 @@ +#!/usr/bin/env python +''' +(c) 2014 Brendan Bulik-Sullivan and Hilary Finucane + +LDSC is a command line tool for estimating + 1. LD Score + 2. heritability / partitioned heritability + 3. genetic covariance / correlation + +''' + +import ldsc_polyfun.ldscore as ld +import ldsc_polyfun.parse as ps +import ldsc_polyfun.sumstats as sumstats +import ldsc_polyfun.regressions as reg +import numpy as np +import pandas as pd +from subprocess import call +from itertools import product +import time, sys, traceback, argparse +from functools import reduce + +try: + x = pd.DataFrame({'A': [1, 2, 3]}) + x.sort_values(by='A') +except AttributeError: + raise ImportError('LDSC requires pandas version >= 0.17.0') + +__version__ = '1.0.0' +MASTHEAD = "*********************************************************************\n" +MASTHEAD += "* LD Score Regression (LDSC)\n" +MASTHEAD += "* Version {V}\n".format(V=__version__) +MASTHEAD += "* (C) 2014-2015 Brendan Bulik-Sullivan and Hilary Finucane\n" +MASTHEAD += "* Broad Institute of MIT and Harvard / MIT Department of Mathematics\n" +MASTHEAD += "* GNU General Public License v3\n" +MASTHEAD += "*********************************************************************\n" +pd.set_option('display.max_rows', 500) +pd.set_option('display.max_columns', 500) +pd.set_option('display.width', 1000) +pd.set_option('display.precision', 4) +pd.set_option('display.max_colwidth',1000) +np.set_printoptions(linewidth=1000) +np.set_printoptions(precision=4) + + +def sec_to_str(t): + '''Convert seconds to days:hours:minutes:seconds''' + [d, h, m, s, n] = reduce(lambda ll, b : divmod(ll[0], b) + ll[1:], [(t, 1), 60, 60, 24]) + f = '' + if d > 0: + f += '{D}d:'.format(D=d) + if h > 0: + f += '{H}h:'.format(H=h) + if m > 0: + f += '{M}m:'.format(M=m) + + f += '{S}s'.format(S=s) + return f + + +def _remove_dtype(x): + '''Removes dtype: float64 and dtype: int64 from pandas printouts''' + x = str(x) + x = x.replace('\ndtype: int64', '') + x = x.replace('\ndtype: float64', '') + return x + + +class Logger(object): + ''' + Lightweight logging. + TODO: replace with logging module + + ''' + def __init__(self, fh): + self.log_fh = open(fh, 'w') + + def log(self, msg): + ''' + Print to log file and stdout with a single command. + + ''' + print(msg, file=self.log_fh) + print(msg) + + +def __filter__(fname, noun, verb, merge_obj): + merged_list = None + if fname: + f = lambda x,n: x.format(noun=noun, verb=verb, fname=fname, num=n) + x = ps.FilterFile(fname) + c = 'Read list of {num} {noun} to {verb} from {fname}' + print(f(c, len(x.IDList))) + merged_list = merge_obj.loj(x.IDList) + len_merged_list = len(merged_list) + if len_merged_list > 0: + c = 'After merging, {num} {noun} remain' + print(f(c, len_merged_list)) + else: + error_msg = 'No {noun} retained for analysis' + raise ValueError(f(error_msg, 0)) + + return merged_list + +def annot_sort_key(s): + '''For use with --cts-bin. Fixes weird pandas crosstab column order.''' + if type(s) == tuple: + s = [x.split('_')[0] for x in s] + s = [float(x) if x != 'min' else -float('inf') for x in s] + else: # type(s) = str: + s = s.split('_')[0] + if s == 'min': + s = float('-inf') + else: + s = float(s) + + return s + +def ldscore(args, log): + ''' + Wrapper function for estimating l1, l1^2, l2 and l4 (+ optionally standard errors) from + reference panel genotypes. + + Annot format is + chr snp bp cm + + ''' + + if args.bfile: + snp_file, snp_obj = args.bfile+'.bim', ps.PlinkBIMFile + ind_file, ind_obj = args.bfile+'.fam', ps.PlinkFAMFile + array_file, array_obj = args.bfile+'.bed', ld.PlinkBEDFile + + # read bim/snp + array_snps = snp_obj(snp_file) + m = len(array_snps.IDList) + log.log('Read list of {m} SNPs from {f}'.format(m=m, f=snp_file)) + if args.annot is not None: # read --annot + try: + if args.thin_annot: # annot file has only annotations + annot = ps.ThinAnnotFile(args.annot) + n_annot, ma = len(annot.df.columns), len(annot.df) + log.log("Read {A} annotations for {M} SNPs from {f}".format(f=args.annot, + A=n_annot, M=ma)) + annot_matrix = annot.df.values + annot_colnames = annot.df.columns + keep_snps = None + else: + annot = ps.AnnotFile(args.annot) + n_annot, ma = len(annot.df.columns) - 4, len(annot.df) + log.log("Read {A} annotations for {M} SNPs from {f}".format(f=args.annot, + A=n_annot, M=ma)) + annot_matrix = np.array(annot.df.iloc[:,4:]) + annot_colnames = annot.df.columns[4:] + keep_snps = None + if np.any(annot.df.SNP.values != array_snps.df.SNP.values): + raise ValueError('The .annot file must contain the same SNPs in the same'+\ + ' order as the .bim file.') + except Exception: + log.log('Error parsing .annot file') + raise + + elif args.extract is not None: # --extract + keep_snps = __filter__(args.extract, 'SNPs', 'include', array_snps) + annot_matrix, annot_colnames, n_annot = None, None, 1 + + + elif args.cts_bin is not None and args.cts_breaks is not None: # --cts-bin + cts_fnames = sumstats._splitp(args.cts_bin) # read filenames + args.cts_breaks = args.cts_breaks.replace('N','-') # replace N with negative sign + try: # split on x + breaks = [[float(x) for x in y.split(',')] for y in args.cts_breaks.split('x')] + except ValueError as e: + raise ValueError('--cts-breaks must be a comma-separated list of numbers: ' + +str(e.args)) + + if len(breaks) != len(cts_fnames): + raise ValueError('Need to specify one set of breaks for each file in --cts-bin.') + + if args.cts_names: + cts_colnames = [str(x) for x in args.cts_names.split(',')] + if len(cts_colnames) != len(cts_fnames): + msg = 'Must specify either no --cts-names or one value for each file in --cts-bin.' + raise ValueError(msg) + + else: + cts_colnames = ['ANNOT'+str(i) for i in range(len(cts_fnames))] + + log.log('Reading numbers with which to bin SNPs from {F}'.format(F=args.cts_bin)) + + cts_levs = [] + full_labs = [] + for i,fh in enumerate(cts_fnames): + vec = ps.read_cts(cts_fnames[i], array_snps.df.SNP.values) + + max_cts = np.max(vec) + min_cts = np.min(vec) + cut_breaks = list(breaks[i]) + name_breaks = list(cut_breaks) + if np.all(cut_breaks >= max_cts) or np.all(cut_breaks <= min_cts): + raise ValueError('All breaks lie outside the range of the cts variable.') + + if np.all(cut_breaks <= max_cts): + name_breaks.append(max_cts) + cut_breaks.append(max_cts+1) + + if np.all(cut_breaks >= min_cts): + name_breaks.append(min_cts) + cut_breaks.append(min_cts-1) + + name_breaks.sort() + cut_breaks.sort() + n_breaks = len(cut_breaks) + # so that col names are consistent across chromosomes with different max vals + name_breaks[0] = 'min' + name_breaks[-1] = 'max' + name_breaks = [str(x) for x in name_breaks] + labs = [name_breaks[i]+'_'+name_breaks[i+1] for i in range(n_breaks-1)] + cut_vec = pd.Series(pd.cut(vec, bins=cut_breaks, labels=labs)) + cts_levs.append(cut_vec) + full_labs.append(labs) + + annot_matrix = pd.concat(cts_levs, axis=1) + annot_matrix.columns = cts_colnames + # crosstab -- for now we keep empty columns + annot_matrix = pd.crosstab(annot_matrix.index, + [annot_matrix[i] for i in annot_matrix.columns], dropna=False, + colnames=annot_matrix.columns) + + # add missing columns + if len(cts_colnames) > 1: + for x in product(*full_labs): + if x not in annot_matrix.columns: + annot_matrix[x] = 0 + else: + for x in full_labs[0]: + if x not in annot_matrix.columns: + annot_matrix[x] = 0 + + annot_matrix = annot_matrix[sorted(annot_matrix.columns, key=annot_sort_key)] + if len(cts_colnames) > 1: + # flatten multi-index + annot_colnames = ['_'.join([cts_colnames[i]+'_'+b for i,b in enumerate(c)]) + for c in annot_matrix.columns] + else: + annot_colnames = [cts_colnames[0]+'_'+b for b in annot_matrix.columns] + + annot_matrix = np.matrix(annot_matrix) + keep_snps = None + n_annot = len(annot_colnames) + if np.any(np.sum(annot_matrix, axis=1) == 0): + # This exception should never be raised. For debugging only. + raise ValueError('Some SNPs have no annotation in --cts-bin. This is a bug!') + + else: + annot_matrix, annot_colnames, keep_snps = None, None, None, + n_annot = 1 + + # read fam + array_indivs = ind_obj(ind_file) + n = len(array_indivs.IDList) + log.log('Read list of {n} individuals from {f}'.format(n=n, f=ind_file)) + # read keep_indivs + if args.keep: + keep_indivs = __filter__(args.keep, 'individuals', 'include', array_indivs) + else: + keep_indivs = None + + # read genotype array + log.log('Reading genotypes from {fname}'.format(fname=array_file)) + geno_array = array_obj(array_file, n, array_snps, keep_snps=keep_snps, + keep_indivs=keep_indivs, mafMin=args.maf) + + # filter annot_matrix down to only SNPs passing MAF cutoffs + if annot_matrix is not None: + annot_keep = geno_array.kept_snps + annot_matrix = annot_matrix[annot_keep,:] + + # determine block widths + x = np.array((args.ld_wind_snps, args.ld_wind_kb, args.ld_wind_cm), dtype=bool) + if np.sum(x) != 1: + raise ValueError('Must specify exactly one --ld-wind option') + + if args.ld_wind_snps: + max_dist = args.ld_wind_snps + coords = np.array(range(geno_array.m)) + elif args.ld_wind_kb: + max_dist = args.ld_wind_kb*1000 + coords = np.array(array_snps.df['BP'])[geno_array.kept_snps] + elif args.ld_wind_cm: + max_dist = args.ld_wind_cm + coords = np.array(array_snps.df['CM'])[geno_array.kept_snps] + + block_left = ld.getBlockLefts(coords, max_dist) + if block_left[len(block_left)-1] == 0 and not args.yes_really: + error_msg = 'Do you really want to compute whole-chomosome LD Score? If so, set the ' + error_msg += '--yes-really flag (warning: it will use a lot of time / memory)' + raise ValueError(error_msg) + + scale_suffix = '' + if args.pq_exp is not None: + log.log('Computing LD with pq ^ {S}.'.format(S=args.pq_exp)) + msg = 'Note that LD Scores with pq raised to a nonzero power are' + msg += 'not directly comparable to normal LD Scores.' + log.log(msg) + scale_suffix = '_S{S}'.format(S=args.pq_exp) + pq = np.matrix(geno_array.maf*(1-geno_array.maf)).reshape((geno_array.m, 1)) + pq = np.power(pq, args.pq_exp) + + if annot_matrix is not None: + annot_matrix = np.multiply(annot_matrix, pq) + else: + annot_matrix = pq + + log.log("Estimating LD Score.") + lN = geno_array.ldScoreVarBlocks(block_left, args.chunk_size, annot=annot_matrix) + col_prefix = "L2"; file_suffix = "l2" + + if n_annot == 1: + ldscore_colnames = [col_prefix+scale_suffix] + else: + ldscore_colnames = [y+col_prefix+scale_suffix for y in annot_colnames] + + # print .ldscore. Output columns: CHR, BP, RS, [LD Scores] + out_fname = args.out + '.' + file_suffix + '.ldscore' + new_colnames = geno_array.colnames + ldscore_colnames + df = pd.DataFrame.from_records(np.c_[geno_array.df, lN]) + df.columns = new_colnames + if args.print_snps: + if args.print_snps.endswith('gz'): + print_snps = pd.read_csv(args.print_snps, header=None, compression='gzip') + elif args.print_snps.endswith('bz2'): + print_snps = pd.read_csv(args.print_snps, header=None, compression='bz2') + else: + print_snps = pd.read_csv(args.print_snps, header=None) + if len(print_snps.columns) > 1: + raise ValueError('--print-snps must refer to a file with a one column of SNP IDs.') + log.log('Reading list of {N} SNPs for which to print LD Scores from {F}'.format(\ + F=args.print_snps, N=len(print_snps))) + + print_snps.columns=['SNP'] + df = df.loc[df.SNP.isin(print_snps.SNP),:] + if len(df) == 0: + raise ValueError('After merging with --print-snps, no SNPs remain.') + else: + msg = 'After merging with --print-snps, LD Scores for {N} SNPs will be printed.' + log.log(msg.format(N=len(df))) + + l2_suffix = '.gz' + log.log("Writing LD Scores for {N} SNPs to {f}.gz".format(f=out_fname, N=len(df))) + df.drop(['CM','MAF'], axis=1).to_csv(out_fname, sep="\t", header=True, index=False, + float_format='%.3f') + call(['gzip', '-f', out_fname]) + if annot_matrix is not None: + M = np.atleast_1d(np.squeeze(np.asarray(np.sum(annot_matrix, axis=0)))) + ii = geno_array.maf > 0.05 + M_5_50 = np.atleast_1d(np.squeeze(np.asarray(np.sum(annot_matrix[ii,:], axis=0)))) + else: + M = [geno_array.m] + M_5_50 = [np.sum(geno_array.maf > 0.05)] + + # print .M + fout_M = open(args.out + '.'+ file_suffix +'.M','w') + print('\t'.join(map(str,M)), file=fout_M) + fout_M.close() + + # print .M_5_50 + fout_M_5_50 = open(args.out + '.'+ file_suffix +'.M_5_50','w') + print('\t'.join(map(str,M_5_50)), file=fout_M_5_50) + fout_M_5_50.close() + + # print annot matrix + if (args.cts_bin is not None) and not args.no_print_annot: + out_fname_annot = args.out + '.annot' + new_colnames = geno_array.colnames + ldscore_colnames + annot_df = pd.DataFrame(np.c_[geno_array.df, annot_matrix]) + annot_df.columns = new_colnames + del annot_df['MAF'] + log.log("Writing annot matrix produced by --cts-bin to {F}".format(F=out_fname+'.gz')) + annot_df.to_csv(out_fname_annot, sep="\t", header=True, index=False) + call(['gzip', '-f', out_fname_annot]) + + # print LD Score summary + pd.set_option('display.max_rows', 200) + log.log('\nSummary of LD Scores in {F}'.format(F=out_fname+l2_suffix)) + t = df.iloc[:,4:].describe() + log.log( t.iloc[1:,:] ) + + np.seterr(divide='ignore', invalid='ignore') # print NaN instead of weird errors + # print correlation matrix including all LD Scores and sample MAF + log.log('') + log.log('MAF/LD Score Correlation Matrix') + log.log( df.iloc[:,4:].corr() ) + + # print condition number + if n_annot > 1: # condition number of a column vector w/ nonzero var is trivially one + log.log('\nLD Score Matrix Condition Number') + cond_num = np.linalg.cond(df.iloc[:,5:]) + log.log( reg.remove_brackets(str(np.matrix(cond_num))) ) + if cond_num > 10000: + log.log('WARNING: ill-conditioned LD Score Matrix!') + + # summarize annot matrix if there is one + if annot_matrix is not None: + # covariance matrix + x = pd.DataFrame(annot_matrix, columns=annot_colnames) + log.log('\nAnnotation Correlation Matrix') + log.log( x.corr() ) + + # column sums + log.log('\nAnnotation Matrix Column Sums') + log.log(_remove_dtype(x.sum(axis=0))) + + # row sums + log.log('\nSummary of Annotation Matrix Row Sums') + row_sums = x.sum(axis=1).describe() + log.log(_remove_dtype(row_sums)) + + np.seterr(divide='raise', invalid='raise') + + +parser = argparse.ArgumentParser() +parser.add_argument('--out', default='ldsc', type=str, + help='Output filename prefix. If --out is not set, LDSC will use ldsc as the ' + 'defualt output filename prefix.') +# Basic LD Score Estimation Flags' +parser.add_argument('--bfile', default=None, type=str, + help='Prefix for Plink .bed/.bim/.fam file') +parser.add_argument('--l2', default=False, action='store_true', + help='Estimate l2. Compatible with both jackknife and non-jackknife.') +# Filtering / Data Management for LD Score +parser.add_argument('--extract', default=None, type=str, + help='File with SNPs to include in LD Score estimation. ' + 'The file should contain one SNP ID per row.') +parser.add_argument('--keep', default=None, type=str, + help='File with individuals to include in LD Score estimation. ' + 'The file should contain one individual ID per row.') +parser.add_argument('--ld-wind-snps', default=None, type=int, + help='Specify the window size to be used for estimating LD Scores in units of ' + '# of SNPs. You can only specify one --ld-wind-* option.') +parser.add_argument('--ld-wind-kb', default=None, type=float, + help='Specify the window size to be used for estimating LD Scores in units of ' + 'kilobase-pairs (kb). You can only specify one --ld-wind-* option.') +parser.add_argument('--ld-wind-cm', default=None, type=float, + help='Specify the window size to be used for estimating LD Scores in units of ' + 'centiMorgans (cM). You can only specify one --ld-wind-* option.') +parser.add_argument('--print-snps', default=None, type=str, + help='This flag tells LDSC to only print LD Scores for the SNPs listed ' + '(one ID per row) in PRINT_SNPS. The sum r^2 will still include SNPs not in ' + 'PRINT_SNPs. This is useful for reducing the number of LD Scores that have to be ' + 'read into memory when estimating h2 or rg.' ) +# Fancy LD Score Estimation Flags +parser.add_argument('--annot', default=None, type=str, + help='Filename prefix for annotation file for partitioned LD Score estimation. ' + 'LDSC will automatically append .annot or .annot.gz to the filename prefix. ' + 'See docs/file_formats_ld for a definition of the .annot format.') +parser.add_argument('--thin-annot', action='store_true', default=False, + help='This flag says your annot files have only annotations, with no SNP, CM, CHR, BP columns.') +parser.add_argument('--cts-bin', default=None, type=str, + help='This flag tells LDSC to compute partitioned LD Scores, where the partition ' + 'is defined by cutting one or several continuous variable[s] into bins. ' + 'The argument to this flag should be the name of a single file or a comma-separated ' + 'list of files. The file format is two columns, with SNP IDs in the first column ' + 'and the continuous variable in the second column. ') +parser.add_argument('--cts-breaks', default=None, type=str, + help='Use this flag to specify names for the continuous variables cut into bins ' + 'with --cts-bin. For each continuous variable, specify breaks as a comma-separated ' + 'list of breakpoints, and separate the breakpoints for each variable with an x. ' + 'For example, if binning on MAF and distance to gene (in kb), ' + 'you might set --cts-breaks 0.1,0.25,0.4x10,100,1000 ') +parser.add_argument('--cts-names', default=None, type=str, + help='Use this flag to specify names for the continuous variables cut into bins ' + 'with --cts-bin. The argument to this flag should be a comma-separated list of ' + 'names. For example, if binning on DAF and distance to gene, you might set ' + '--cts-bin DAF,DIST_TO_GENE ') +parser.add_argument('--per-allele', default=False, action='store_true', + help='Setting this flag causes LDSC to compute per-allele LD Scores, ' + 'i.e., \ell_j := \sum_k p_k(1-p_k)r^2_{jk}, where p_k denotes the MAF ' + 'of SNP j. ') +parser.add_argument('--pq-exp', default=None, type=float, + help='Setting this flag causes LDSC to compute LD Scores with the given scale factor, ' + 'i.e., \ell_j := \sum_k (p_k(1-p_k))^a r^2_{jk}, where p_k denotes the MAF ' + 'of SNP j and a is the argument to --pq-exp. ') +parser.add_argument('--no-print-annot', default=False, action='store_true', + help='By defualt, seting --cts-bin or --cts-bin-add causes LDSC to print ' + 'the resulting annot matrix. Setting --no-print-annot tells LDSC not ' + 'to print the annot matrix. ') +parser.add_argument('--maf', default=None, type=float, + help='Minor allele frequency lower bound. Default is MAF > 0.') +# Basic Flags for Working with Variance Components +parser.add_argument('--h2', default=None, type=str, + help='Filename for a .sumstats[.gz] file for one-phenotype LD Score regression. ' + '--h2 requires at minimum also setting the --ref-ld and --w-ld flags.') +parser.add_argument('--h2-cts', default=None, type=str, + help='Filename for a .sumstats[.gz] file for cell-type-specific analysis. ' + '--h2-cts requires the --ref-ld-chr, --w-ld, and --ref-ld-chr-cts flags.') +parser.add_argument('--rg', default=None, type=str, + help='Comma-separated list of prefixes of .chisq filed for genetic correlation estimation.') +parser.add_argument('--ref-ld', default=None, type=str, + help='Use --ref-ld to tell LDSC which LD Scores to use as the predictors in the LD ' + 'Score regression. ' + 'LDSC will automatically append .l2.ldscore/.l2.ldscore.gz to the filename prefix.') +parser.add_argument('--ref-ld-chr', default=None, type=str, + help='Same as --ref-ld, but will automatically concatenate .l2.ldscore files split ' + 'across 22 chromosomes. LDSC will automatically append .l2.ldscore/.l2.ldscore.gz ' + 'to the filename prefix. If the filename prefix contains the symbol @, LDSC will ' + 'replace the @ symbol with chromosome numbers. Otherwise, LDSC will append chromosome ' + 'numbers to the end of the filename prefix.' + 'Example 1: --ref-ld-chr ld/ will read ld/1.l2.ldscore.gz ... ld/22.l2.ldscore.gz' + 'Example 2: --ref-ld-chr ld/@_kg will read ld/1_kg.l2.ldscore.gz ... ld/22_kg.l2.ldscore.gz') +parser.add_argument('--w-ld', default=None, type=str, + help='Filename prefix for file with LD Scores with sum r^2 taken over SNPs included ' + 'in the regression. LDSC will automatically append .l2.ldscore/.l2.ldscore.gz.') +parser.add_argument('--w-ld-chr', default=None, type=str, + help='Same as --w-ld, but will read files split into 22 chromosomes in the same ' + 'manner as --ref-ld-chr.') +parser.add_argument('--overlap-annot', default=False, action='store_true', + help='This flag informs LDSC that the partitioned LD Scores were generates using an ' + 'annot matrix with overlapping categories (i.e., not all row sums equal 1), ' + 'and prevents LDSC from displaying output that is meaningless with overlapping categories.') +parser.add_argument('--print-coefficients',default=False,action='store_true', + help='when categories are overlapping, print coefficients as well as heritabilities.') +parser.add_argument('--frqfile', type=str, + help='For use with --overlap-annot. Provides allele frequencies to prune to common ' + 'snps if --not-M-5-50 is not set.') +parser.add_argument('--frqfile-chr', type=str, + help='Prefix for --frqfile files split over chromosome.') +parser.add_argument('--no-intercept', action='store_true', + help = 'If used with --h2, this constrains the LD Score regression intercept to equal ' + '1. If used with --rg, this constrains the LD Score regression intercepts for the h2 ' + 'estimates to be one and the intercept for the genetic covariance estimate to be zero.') +parser.add_argument('--intercept-h2', action='store', default=None, + help = 'Intercepts for constrained-intercept single-trait LD Score regression.') +parser.add_argument('--intercept-gencov', action='store', default=None, + help = 'Intercepts for constrained-intercept cross-trait LD Score regression.' + ' Must have same length as --rg. The first entry is ignored.') +parser.add_argument('--M', default=None, type=str, + help='# of SNPs (if you don\'t want to use the .l2.M files that came with your .l2.ldscore.gz files)') +parser.add_argument('--two-step', default=None, type=float, + help='Test statistic bound for use with the two-step estimator. Not compatible with --no-intercept and --constrain-intercept.') +parser.add_argument('--chisq-max', default=None, type=float, + help='Max chi^2.') +parser.add_argument('--ref-ld-chr-cts', default=None, type=str, + help='Name of a file that has a list of file name prefixes for cell-type-specific analysis.') +parser.add_argument('--print-all-cts', action='store_true', default=False) + +parser.add_argument('--max-chi2', type=float, default=80, help='Remove SNPs with chi2 > this value (if it''s larger than 0.001*N)') + +# Flags for both LD Score estimation and h2/gencor estimation +parser.add_argument('--print-cov', default=False, action='store_true', + help='For use with --h2/--rg. This flag tells LDSC to print the ' + 'covaraince matrix of the estimates.') +parser.add_argument('--print-delete-vals', default=False, action='store_true', + help='If this flag is set, LDSC will print the block jackknife delete-values (' + 'i.e., the regression coefficeints estimated from the data with a block removed). ' + 'The delete-values are formatted as a matrix with (# of jackknife blocks) rows and ' + '(# of LD Scores) columns.') +# Flags you should almost never use +parser.add_argument('--chunk-size', default=50, type=int, + help='Chunk size for LD Score calculation. Use the default.') +parser.add_argument('--pickle', default=False, action='store_true', + help='Store .l2.ldscore files as pickles instead of gzipped tab-delimited text.') +parser.add_argument('--yes-really', default=False, action='store_true', + help='Yes, I really want to compute whole-chromosome LD Score.') +parser.add_argument('--invert-anyway', default=False, action='store_true', + help="Force LDSC to attempt to invert ill-conditioned matrices.") +parser.add_argument('--n-blocks', default=200, type=int, + help='Number of block jackknife blocks.') +parser.add_argument('--not-M-5-50', default=False, action='store_true', + help='This flag tells LDSC to use the .l2.M file instead of the .l2.M_5_50 file.') +parser.add_argument('--return-silly-things', default=False, action='store_true', + help='Force ldsc to return silly genetic correlation estimates.') +parser.add_argument('--no-check-alleles', default=False, action='store_true', + help='For rg estimation, skip checking whether the alleles match. This check is ' + 'redundant for pairs of chisq files generated using munge_sumstats.py and the ' + 'same argument to the --merge-alleles flag.') +# transform to liability scale +parser.add_argument('--samp-prev',default=None, + help='Sample prevalence of binary phenotype (for conversion to liability scale).') +parser.add_argument('--pop-prev',default=None, + help='Population prevalence of binary phenotype (for conversion to liability scale).') + +parser.add_argument('--keep-large', default=False, action='store_true', + help='If this is invoked, large chi2 SNPs will not be removed from any jackknife block') + +parser.add_argument('--skip-cond-check', default=False, action='store_true', + help='Skip checking the condition number of the LD scores') + +parser.add_argument('--evenodd-split', default=False, action='store_true', + help='Compute LOCO even/odd split tau estimates') + + +parser.add_argument('--nn', default=False, action='store_true', + help='If this is invoked, S-LDSC will only estimate non-negative taus (this only makes sense for non-overlapping annotations)') + +parser.add_argument('--nnls-exact', default=False, action='store_true', + help='If this is invoked, S-LDSC will estimate non-negative taus using an exact instead of an approximate solver (this will be slower but slightly more accurate)') + +#LOCO estimates +parser.add_argument('--loco', default=False, action='store_true', + help='Compute Ridge-regression-based leave-one-chromosome-out (LOCO) estimates') + +parser.add_argument('--num-chr-sets', type=int, default=22, + help='Number of chromosome sets for loco computations') + +parser.add_argument('--num-chr', type=int, default=22, + help='Number of chromosomes for the target organism (default is 22)') + + +parser.add_argument('--no-standardize-ridge', default=False, action='store_true', + help='disable Ridge standardization') + +#List of annotations to use +parser.add_argument('--anno', default=None, help='comma-separated list of annotations to use') + +#List of annotations to use +parser.add_argument('--ridge_lambda', default=None, help='Lambda value for ridge (selected automatically if not set)') + +parser.add_argument('--reestimate-lambdas', default=False, action='store_true', + help='If turned on, will reevaluate Ridge regression regularization parameter for each jackknife sample') +parser.add_argument('--ridge-jackknife', default=False, action='store_true', + help='If turned on, will perform jackknife when ridge is turned on (i.e., when using --loco)') + + + + +if __name__ == '__main__': + + args = parser.parse_args() + if args.out is None: + raise ValueError('--out is required.') + + log = Logger(args.out+'.log') + try: + defaults = vars(parser.parse_args('')) + opts = vars(args) + non_defaults = [x for x in opts.keys() if opts[x] != defaults[x]] + header = MASTHEAD + header += "Call: \n" + header += './ldsc.py \\\n' + options = ['--'+x.replace('_','-')+' '+str(opts[x])+' \\' for x in non_defaults] + header += '\n'.join(options).replace('True','').replace('False','') + header = header[0:-1]+'\n' + log.log(header) + log.log('Beginning analysis at {T}'.format(T=time.ctime())) + start_time = time.time() + if args.ridge_lambda: + assert args.loco, '--ridge_lambda can only be used with --loco' + + if args.n_blocks <= 1: + raise ValueError('--n-blocks must be an integer > 1.') + if args.bfile is not None: + if args.l2 is None: + raise ValueError('Must specify --l2 with --bfile.') + if args.annot is not None and args.extract is not None: + raise ValueError('--annot and --extract are currently incompatible.') + if args.cts_bin is not None and args.extract is not None: + raise ValueError('--cts-bin and --extract are currently incompatible.') + if args.annot is not None and args.cts_bin is not None: + raise ValueError('--annot and --cts-bin are currently incompatible.') + if (args.cts_bin is not None) != (args.cts_breaks is not None): + raise ValueError('Must set both or neither of --cts-bin and --cts-breaks.') + if args.per_allele and args.pq_exp is not None: + raise ValueError('Cannot set both --per-allele and --pq-exp (--per-allele is equivalent to --pq-exp 1).') + if args.per_allele: + args.pq_exp = 1 + + + ldscore(args, log) + # summary statistics + elif (args.h2 or args.rg or args.h2_cts) and (args.ref_ld or args.ref_ld_chr) and (args.w_ld or args.w_ld_chr): + if args.h2 is not None and args.rg is not None: + raise ValueError('Cannot set both --h2 and --rg.') + if args.ref_ld and args.ref_ld_chr: + raise ValueError('Cannot set both --ref-ld and --ref-ld-chr.') + if args.w_ld and args.w_ld_chr: + raise ValueError('Cannot set both --w-ld and --w-ld-chr.') + if (args.samp_prev is not None) != (args.pop_prev is not None): + raise ValueError('Must set both or neither of --samp-prev and --pop-prev.') + + if not args.overlap_annot or args.not_M_5_50: + if args.frqfile is not None or args.frqfile_chr is not None: + log.log('The frequency file is unnecessary and is being ignored.') + args.frqfile = None + args.frqfile_chr = None + if args.overlap_annot and not args.not_M_5_50: + if not ((args.frqfile and args.ref_ld) or (args.frqfile_chr and args.ref_ld_chr)): + raise ValueError('Must set either --frqfile and --ref-ld or --frqfile-chr and --ref-ld-chr') + + if args.rg: + sumstats.estimate_rg(args, log) + elif args.h2: + sumstats.estimate_h2(args, log) + elif args.h2_cts: + sumstats.cell_type_specific(args, log) + + # bad flags + else: + print(header) + print('Error: no analysis selected.') + print('ldsc.py -h describes options.') + except Exception: + #ex_type, ex, tb = sys.exc_info() + log.log( traceback.format_exc() ) + raise + finally: + log.log('Analysis finished at {T}'.format(T=time.ctime()) ) + time_elapsed = round(time.time()-start_time,2) + log.log('Total time elapsed: {T}'.format(T=sec_to_str(time_elapsed))) diff --git a/SCRIPTS/polyfun/ldsc_polyfun/__init__.py b/SCRIPTS/polyfun/ldsc_polyfun/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/SCRIPTS/polyfun/ldsc_polyfun/irwls.py b/SCRIPTS/polyfun/ldsc_polyfun/irwls.py new file mode 100644 index 0000000..b3dcaeb --- /dev/null +++ b/SCRIPTS/polyfun/ldsc_polyfun/irwls.py @@ -0,0 +1,197 @@ +''' +(c) 2015 Brendan Bulik-Sullivan and Hilary Finucane + +Iterativey re-weighted least squares. + +''' + +import numpy as np +from . import jackknife as jk +import logging + + +class IRWLS(object): + + ''' + Iteratively re-weighted least squares (FLWS). + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + update_func : function + Transforms output of np.linalg.lstsq to new weights. + n_blocks : int + Number of jackknife blocks (for estimating SE via block jackknife). + w : np.matrix with shape (n, 1) + Initial regression weights (default is the identity matrix). These should be on the + inverse CVF scale. + slow : bool + Use slow block jackknife? (Mostly for testing) + + Attributes + ---------- + est : np.matrix with shape (1, p) + IRWLS estimate. + jknife_est : np.matrix with shape (1, p) + Jackknifed estimate. + jknife_var : np.matrix with shape (1, p) + Variance of jackknifed estimate. + jknife_se : np.matrix with shape (1, p) + Standard error of jackknifed estimate, equal to sqrt(jknife_var). + jknife_cov : np.matrix with shape (p, p) + Covariance matrix of jackknifed estimate. + delete_values : np.matrix with shape (n_blocks, p) + Jackknife delete values. + + Methods + ------- + wls(x, y, w) : + Weighted Least Squares. + _weight(x, w) : + Weight x by w. + + ''' + + def __init__(self, x, y, update_func, n_blocks, w=None, slow=False, separators=None): + n, p = jk._check_shape(x, y) + if w is None: + w = np.ones_like(y) + if w.shape != (n, 1): + raise ValueError( + 'w has shape {S}. w must have shape ({N}, 1).'.format(S=w.shape, N=n)) + + jknife = self.irwls( + x, y, update_func, n_blocks, w, slow=slow, separators=separators) + self.est = jknife.est + self.jknife_se = jknife.jknife_se + self.jknife_est = jknife.jknife_est + self.jknife_var = jknife.jknife_var + self.jknife_cov = jknife.jknife_cov + self.delete_values = jknife.delete_values + self.separators = jknife.separators + + @classmethod + def irwls(cls, x, y, update_func, n_blocks, w, slow=False, separators=None): + ''' + Iteratively re-weighted least squares (IRWLS). + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + update_func: function + Transforms output of np.linalg.lstsq to new weights. + n_blocks : int + Number of jackknife blocks (for estimating SE via block jackknife). + w : np.matrix with shape (n, 1) + Initial regression weights. + slow : bool + Use slow block jackknife? (Mostly for testing) + separators : list or None + Block jackknife block boundaries (optional). + + Returns + ------- + jknife : jk.LstsqJackknifeFast + Block jackknife regression with the final IRWLS weights. + + ''' + (n, p) = x.shape + if y.shape != (n, 1): + raise ValueError( + 'y has shape {S}. y must have shape ({N}, 1).'.format(S=y.shape, N=n)) + if w.shape != (n, 1): + raise ValueError( + 'w has shape {S}. w must have shape ({N}, 1).'.format(S=w.shape, N=n)) + + w = np.sqrt(w) + for i in range(2): # update this later + new_w = np.sqrt(update_func(cls.wls(x, y, w))) + if new_w.shape != w.shape: + logging.info('IRWLS update:', new_w.shape, w.shape) + raise ValueError('New weights must have same shape.') + else: + w = new_w + + x = cls._weight(x, w) + y = cls._weight(y, w) + if slow: + jknife = jk.LstsqJackknifeSlow( + x, y, n_blocks, separators=separators) + else: + jknife = jk.LstsqJackknifeFast( + x, y, n_blocks=n_blocks, separators=separators) + + return jknife + + @classmethod + def wls(cls, x, y, w): + ''' + Weighted least squares. + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + w : np.matrix with shape (n, 1) + Regression weights (1/CVF scale). + + Returns + ------- + coef : list with four elements (coefficients, residuals, rank, singular values) + Output of np.linalg.lstsq + + ''' + (n, p) = x.shape + if y.shape != (n, 1): + raise ValueError( + 'y has shape {S}. y must have shape ({N}, 1).'.format(S=y.shape, N=n)) + if w.shape != (n, 1): + raise ValueError( + 'w has shape {S}. w must have shape ({N}, 1).'.format(S=w.shape, N=n)) + + x = cls._weight(x, w) + y = cls._weight(y, w) + coef = np.linalg.lstsq(x, y) + return coef + + @classmethod + def _weight(cls, x, w): + ''' + Weight x by w. + + Parameters + ---------- + x : np.matrix with shape (n, p) + Rows are observations. + w : np.matrix with shape (n, 1) + Regression weights (1 / sqrt(CVF) scale). + + Returns + ------- + x_new : np.matrix with shape (n, p) + x_new[i,j] = x[i,j] * w'[i], where w' is w normalized to have sum 1. + + Raises + ------ + ValueError : + If any element of w is <= 0 (negative weights are not meaningful in WLS). + + ''' + if np.any(w <= 0): + raise ValueError('Weights must be > 0') + (n, p) = x.shape + if w.shape != (n, 1): + raise ValueError( + 'w has shape {S}. w must have shape (n, 1).'.format(S=w.shape)) + + w = w / float(np.sum(w)) + x_new = x*w + return x_new diff --git a/SCRIPTS/polyfun/ldsc_polyfun/jackknife.py b/SCRIPTS/polyfun/ldsc_polyfun/jackknife.py new file mode 100644 index 0000000..1671d3f --- /dev/null +++ b/SCRIPTS/polyfun/ldsc_polyfun/jackknife.py @@ -0,0 +1,831 @@ +''' +(c) 2014 Brendan Bulik-Sullivan and Hilary Finucane + +Fast block jackknives. + +Everything in this module deals with 2D numpy arrays. 1D data are represented as arrays +with dimension (N, 1) or (1, N), to avoid bugs arising from numpy treating (N, ) as +a fundamentally different shape from (N, 1). The convention in this module is for the +first dimension to represent # of data points (or # of blocks in a block jackknife, since +a block is like a datapoint), and for the second dimension to represent the dimensionality +of the data. + +''' + + +import numpy as np +from scipy.optimize import nnls +np.seterr(divide='raise', invalid='raise') +from tqdm import tqdm +from sklearn.linear_model import Lasso +import logging +import warnings +warnings.filterwarnings('ignore', message='Coordinate descent with alpha=0 may lead to unexpected results and is discouraged.') +warnings.filterwarnings('ignore', message='Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.') + +from sklearn.metrics import r2_score + +def _check_shape(x, y): + '''Check that x and y have the correct shapes (for regression jackknives).''' + if len(x.shape) != 2 or len(y.shape) != 2: + raise ValueError('x and y must be 2D arrays.') + if x.shape[0] != y.shape[0]: + raise ValueError( + 'Number of datapoints in x != number of datapoints in y.') + if y.shape[1] != 1: + raise ValueError('y must have shape (n_snp, 1)') + n, p = x.shape + if p > n: + raise ValueError('More dimensions than datapoints.') + + return (n, p) + + +def _check_shape_block(xty_block_values, xtx_block_values): + '''Check that xty_block_values and xtx_block_values have correct shapes.''' + if xtx_block_values.shape[0:2] != xty_block_values.shape: + raise ValueError( + 'Shape of xty_block_values must equal shape of first two dimensions of xty_block_values.') + if len(xtx_block_values.shape) < 3: + raise ValueError('xtx_block_values must be a 3D array.') + if xtx_block_values.shape[1] != xtx_block_values.shape[2]: + raise ValueError( + 'Last two axes of xtx_block_values must have same dimension.') + + return xtx_block_values.shape[0:2] + + +class Jackknife(object): + + ''' + Base class for jackknife objects. Input involves x,y, so this base class is tailored + for statistics computed from independent and dependent variables (e.g., regressions). + The __delete_vals_to_pseudovalues__ and __jknife__ methods will still be useful for other + sorts of statistics, but the __init__ method will need to be overriden. + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + n_blocks : int + Number of jackknife blocks + *args, **kwargs : + Arguments for inheriting jackknives. + + Attributes + ---------- + n_blocks : int + Number of jackknife blocks + p : int + Dimensionality of the independent varianble + N : int + Number of datapoints (equal to x.shape[0]) + + Methods + ------- + jknife(pseudovalues): + Computes jackknife estimate and variance from the jackknife pseudovalues. + delete_vals_to_pseudovalues(delete_vals, est): + Converts delete values and the whole-data estimate to pseudovalues. + get_separators(): + Returns (approximately) evenly-spaced jackknife block boundaries. + ''' + + def __init__(self, x, y, n_blocks=None, separators=None): + self.N, self.p = _check_shape(x, y) + if separators is not None: + if max(separators) != self.N: + raise ValueError( + 'Max(separators) must be equal to number of data points.') + if min(separators) != 0: + raise ValueError('Max(separators) must be equal to 0.') + self.separators = sorted(separators) + self.n_blocks = len(separators) - 1 + elif n_blocks is not None: + self.n_blocks = n_blocks + self.separators = self.get_separators(self.N, self.n_blocks) + else: + raise ValueError('Must specify either n_blocks are separators.') + + if self.n_blocks > self.N: + raise ValueError('More blocks than data points.') + + @classmethod + def jknife(cls, pseudovalues): + ''' + Converts pseudovalues to jackknife estimate and variance. + + Parameters + ---------- + pseudovalues : np.matrix pf floats with shape (n_blocks, p) + + Returns + ------- + jknife_est : np.matrix with shape (1, p) + Jackknifed estimate. + jknife_var : np.matrix with shape (1, p) + Variance of jackknifed estimate. + jknife_se : np.matrix with shape (1, p) + Standard error of jackknifed estimate, equal to sqrt(jknife_var). + jknife_cov : np.matrix with shape (p, p) + Covariance matrix of jackknifed estimate. + + ''' + + n_blocks = pseudovalues.shape[0] + jknife_cov = np.atleast_2d(np.cov(pseudovalues.T, ddof=1) / n_blocks) + jknife_var = np.atleast_2d(np.diag(jknife_cov)) + jknife_se = np.atleast_2d(np.sqrt(jknife_var)) + jknife_est = np.atleast_2d(np.mean(pseudovalues, axis=0)) + return (jknife_est, jknife_var, jknife_se, jknife_cov) + + @classmethod + def delete_values_to_pseudovalues(cls, delete_values, est): + ''' + Converts whole-data estimate and delete values to pseudovalues. + + Parameters + ---------- + delete_values : np.matrix with shape (n_blocks, p) + Delete values. + est : np.matrix with shape (1, p): + Whole-data estimate. + + Returns + ------- + pseudovalues : np.matrix with shape (n_blocks, p) + Psuedovalues. + + Raises + ------ + ValueError : + If est.shape != (1, delete_values.shape[1]) + + ''' + + n_blocks, p = delete_values.shape + if est.shape != (1, p): + raise ValueError( + 'Different number of parameters in delete_values than in est.') + + return n_blocks * est - (n_blocks - 1) * delete_values + + @classmethod + def get_separators(cls, N, n_blocks): + '''Define evenly-spaced block boundaries.''' + return np.floor(np.linspace(0, N, n_blocks + 1)).astype(int) + + + + +class LstsqJackknifeSlow(Jackknife): + + ''' + Slow linear-regression block jackknife. This class computes delete values directly, + rather than forming delete values from block values. Useful for testing and for + non-negative least squares (which as far as I am aware does not admit a fast block + jackknife algorithm). + + Inherits from Jackknife class. + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + n_blocks : int + Number of jackknife blocks + nn: bool + Non-negative least-squares? + + Attributes + ---------- + est : np.matrix with shape (1, p) + FWLS estimate. + jknife_est : np.matrix with shape (1, p) + Jackknifed estimate. + jknife_var : np.matrix with shape (1, p) + Variance of jackknifed estimate. + jknife_se : np.matrix with shape (1, p) + Standard error of jackknifed estimate, equal to sqrt(jknife_var). + jknife_cov : np.matrix with shape (p, p) + Covariance matrix of jackknifed estimate. + delete_vals : np.matrix with shape (n_blocks, p) + Jackknife delete values. + ''' + + @classmethod + def delete_values(cls, x, y, func, s): + ''' + Compute delete values by deleting one block at a time. + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + func : function (n, p) , (n, 1) --> (1, p) + Function of x and y to be jackknived. + s : list of ints + Block separators. + + Returns + ------- + delete_values : np.matrix with shape (n_blocks, p) + Delete block values (with n_blocks blocks defined by parameter s). + + Raises + ------ + ValueError : + If x.shape[0] does not equal y.shape[0] or x and y are not 2D. + + ''' + _check_shape(x, y) + d = [] + logging.info('Starting non-negative jackknife...') + for i in tqdm(range(len(s) - 1)): + jk_est = func(np.vstack([x[0:s[i], ...], x[s[i + 1]:, ...]]), np.vstack([y[0:s[i], ...], y[s[i + 1]:, ...]])) + d.append(jk_est) + + return np.concatenate(d, axis=0) + + + def __init__(self, x, y, is_large_chi2=False, n_blocks=None, nn=False, separators=None, chr_num=None, evenodd_split=False, nnls_exact=False): + + Jackknife.__init__(self, x, y, n_blocks, separators) + + #estimate taus + if nn: # non-negative least squares + if nnls_exact: + self.est = np.atleast_2d(nnls(x, np.array(y).T[0])[0]) + else: + xtx = x.T.dot(x) + lasso = Lasso(alpha=1e-100, fit_intercept=False, precompute=xtx, positive=True, max_iter=10000, random_state=0) + self.est = lasso.fit(x,y[:,0]).coef_.reshape((1, x.shape[1])) + else: + self.est = np.atleast_2d(np.linalg.lstsq(x, np.array(y).T[0])[0]) + + #move large_chi2 SNPs to the end of x and y (don't include them in the separator definition, so that they'll never get removed during jackknife) + if np.any(is_large_chi2): + x_large = x[is_large_chi2] + y_large = y[is_large_chi2] + x = x[~is_large_chi2] + y = y[~is_large_chi2] + Jackknife.__init__(self, x, y, n_blocks, separators) + x = np.concatenate((x,x_large), axis=0) + y = np.concatenate((y,y_large), axis=0) + + #jackknife + if nn: + d = [] + s = self.separators + for i in tqdm(range(len(s) - 1), disable=False): + x_noblock = np.delete(x, slice(s[i], s[i+1]), axis=0) + y_noblock = np.delete(y, slice(s[i], s[i+1]), axis=0) + + if nnls_exact: + jk_est = np.atleast_2d(nnls(x_noblock, y_noblock[:,0])[0]) + else: + x_block = x[s[i] : s[i+1]] + xtx_noblock = xtx - x_block.T.dot(x_block) + lasso_noblock = Lasso(alpha=1e-100, fit_intercept=False, precompute=xtx_noblock, positive=True, max_iter=10000, random_state=0) + jk_est = lasso_noblock.fit(x_noblock, y_noblock[:,0]).coef_.reshape((1, x.shape[1])) + ###z = nnls(x_noblock, y_noblock[:,0])[0] + ###assert np.allclose(z, jk_est[0]) + d.append(jk_est) + self.delete_values = np.concatenate(d, axis=0) + else: + self.delete_values = self.delete_values(x, y, func, self.separators) + + + self.pseudovalues = self.delete_values_to_pseudovalues( + self.delete_values, self.est) + (self.jknife_est, self.jknife_var, self.jknife_se, self.jknife_cov) =\ + self.jknife(self.pseudovalues) + + if evenodd_split: + assert y.shape[1]==1 + assert chr_num is not None + assert len(np.unique(chr_num)) > 1 + self.chr_list = np.sort(np.unique(chr_num)) + self.est_loco = np.empty((len(self.chr_list), x.shape[1]), dtype=np.float32) + for chr_i, left_out_chr in enumerate(tqdm(self.chr_list)): + is_loco = ((chr_num%2)==(left_out_chr%2)) & (chr_num != left_out_chr) + x_loco = x[is_loco] + y_loco = y[is_loco] + self.est_loco[chr_i, :] = nnls(x_loco, y_loco[:,0])[0] + + + +class LstsqJackknifeFast(Jackknife): + + def __init__(self, x, y, is_large_chi2=False, n_blocks=None, separators=None, chr_num=None, evenodd_split=False): + + #compute jackknife estimates using all SNPs + Jackknife.__init__(self, x, y, n_blocks, separators) + xty, xtx = self.block_values(x, y, self.separators) + self.est = self.block_values_to_est(xty, xtx) + + #compute xtx_tot and xty_tot + xty_tot = np.sum(xty, axis=0) + xtx_tot = np.sum(xtx, axis=0) + + #exclude large-chi2 SNPs from xtx and xty for the jackknife + if np.any(is_large_chi2): + x = x[~is_large_chi2] + y = y[~is_large_chi2] + Jackknife.__init__(self, x, y, n_blocks, separators) + xty, xtx = self.block_values(x, y, self.separators) + + self.delete_values = self.block_values_to_delete_values(xty, xtx, xtx_tot=xtx_tot, xty_tot=xty_tot) + self.pseudovalues = self.delete_values_to_pseudovalues( + self.delete_values, self.est) + (self.jknife_est, self.jknife_var, self.jknife_se, self.jknife_cov) =\ + self.jknife(self.pseudovalues) + + + if evenodd_split: + assert y.shape[1]==1 + assert chr_num is not None + assert len(np.unique(chr_num)) > 1 + + x_even = x[chr_num %2 == 0] + y_even = y[chr_num %2 == 0] + XTX_even = x_even.T.dot(x_even) + XTy_even = y_even[:,0].dot(x_even) + del x_even, y_even + + x_odd = x[chr_num %2 != 0] + y_odd = y[chr_num %2 != 0] + XTX_odd = x_odd.T.dot(x_odd) + XTy_odd = y_odd[:,0].dot(x_odd) + del x_odd, y_odd + + assert np.allclose(XTy_even + XTy_odd, y[:,0].dot(x)) + assert np.allclose(XTX_even + XTX_odd, x.T.dot(x)) + + self.chr_list = np.sort(np.unique(chr_num)) + #self.est_chr = np.empty((len(self.chr_list), x.shape[1]), dtype=np.float32) + self.est_loco = np.empty((len(self.chr_list), x.shape[1]), dtype=np.float32) + for chr_i, left_out_chr in enumerate(self.chr_list): + x_chr = x[chr_num == left_out_chr] + y_chr = y[chr_num == left_out_chr, 0] + XTX_chr = x_chr.T.dot(x_chr) + XTy_chr = y_chr.dot(x_chr) + + if left_out_chr %2 == 0: + XTX_loco = XTX_even - XTX_chr + XTy_loco = XTy_even - XTy_chr + else: + XTX_loco = XTX_odd - XTX_chr + XTy_loco = XTy_odd - XTy_chr + + self.est_loco[chr_i, :] = np.linalg.solve(XTX_loco, XTy_loco) + #self.est_chr[chr_i, :] = np.linalg.solve(XTX_chr, XTy_chr) + + + + @classmethod + def block_values_to_delete_values(cls, xty_block_values, xtx_block_values, xtx_tot, xty_tot): + n_blocks, p = _check_shape_block(xty_block_values, xtx_block_values) + delete_values = np.zeros((n_blocks, p)) + for j in range(n_blocks): + delete_xty = xty_tot - xty_block_values[j] + delete_xtx = xtx_tot - xtx_block_values[j] + delete_values[j, ...] = np.linalg.solve( + delete_xtx, delete_xty).reshape((1, p)) + + return delete_values + + + @classmethod + def block_values(cls, x, y, s): + ''' + Compute block values. + + Parameters + ---------- + x : np.matrix with shape (n, p) + Independent variable. + y : np.matrix with shape (n, 1) + Dependent variable. + n_blocks : int + Number of jackknife blocks + s : list of ints + Block separators. + + Returns + ------- + xty_block_values : np.matrix with shape (n_blocks, p) + Block values of X^T Y. + xtx_block_values : 3d np array with shape (n_blocks, p, p) + Block values of X^T X. + + Raises + ------ + ValueError : + If x.shape[0] does not equal y.shape[0] or x and y are not 2D. + + ''' + n, p = _check_shape(x, y) + n_blocks = len(s) - 1 + xtx_block_values = np.zeros((n_blocks, p, p)) + xty_block_values = np.zeros((n_blocks, p)) + for i in range(n_blocks): + xty_block_values[i, ...] = np.dot( + x[s[i]:s[i + 1], ...].T, y[s[i]:s[i + 1], ...]).reshape((1, p)) + xtx_block_values[i, ...] = np.dot( + x[s[i]:s[i + 1], ...].T, x[s[i]:s[i + 1], ...]) + + return (xty_block_values, xtx_block_values) + + + @classmethod + def block_values_to_est(cls, xty_block_values, xtx_block_values): + ''' + Converts block values to the whole-data linear regression estimate. + + Parameters + ---------- + xty_block_values : np.matrix with shape (n_blocks, p) + Block values of X^T Y. + xtx_block_values : 3D np.array with shape (n_blocks, p, p) + Block values of X^T X + + Returns + ------- + est : np.matrix with shape (1, p) + Whole data estimate. + + Raises + ------ + LinAlgError : + If design matrix is singular. + ValueError : + If the last two dimensions of xtx_block_values are not equal or if the first two + dimensions of xtx_block_values do not equal the shape of xty_block_values. + + ''' + n_blocks, p = _check_shape_block(xty_block_values, xtx_block_values) + xty = np.sum(xty_block_values, axis=0) + xtx = np.sum(xtx_block_values, axis=0) + return np.linalg.solve(xtx, xty).reshape((1, p)) + + + + +class RatioJackknife(Jackknife): + + ''' + Block jackknife ratio estimate. + + Jackknife. + + Parameters + ---------- + est : float or np.array with shape (1, p) + Whole data ratio estimate + numer_delete_values : np.matrix with shape (n_blocks, p) + Delete values for the numerator. + denom_delete_values: np.matrix with shape (n_blocks, p) + Delete values for the denominator. + + Methods + ------- + delete_vals_to_pseudovalues(est, denom, num): + Converts denominator/ numerator delete values and the whole-data estimate to + pseudovalues. + + Raises + ------ + FloatingPointError : + If any entry of denom_delete_values is zero. + + Note that it is possible for the denominator to cross zero (i.e., be both positive + and negative) and still have a finite ratio estimate and SE, for example if the + numerator is fixed to 0 and the denominator is either -1 or 1. If the denominator + is noisily close to zero, then it is unlikely that the denominator will yield zero + exactly (and therefore yield an inf or nan), but delete values will be of the form + (numerator / close to zero) and -(numerator / close to zero), i.e., (big) and -(big), + and so the jackknife will (correctly) yield huge SE. + + ''' + + def __init__(self, est, numer_delete_values, denom_delete_values): + if numer_delete_values.shape != denom_delete_values.shape: + raise ValueError( + 'numer_delete_values.shape != denom_delete_values.shape.') + if len(numer_delete_values.shape) != 2: + raise ValueError('Delete values must be matrices.') + if len(est.shape) != 2 or est.shape[0] != 1 or est.shape[1] != numer_delete_values.shape[1]: + raise ValueError( + 'Shape of est does not match shape of delete values.') + + self.n_blocks = numer_delete_values.shape[0] + self.est = est + self.pseudovalues = self.delete_values_to_pseudovalues(self.est, + denom_delete_values, numer_delete_values) + (self.jknife_est, self.jknife_var, self.jknife_se, self.jknife_cov) =\ + self.jknife(self.pseudovalues) + + @classmethod + def delete_values_to_pseudovalues(cls, est, denom, numer): + ''' + Converts delete values to pseudovalues. + + Parameters + ---------- + est : np.matrix with shape (1, p) + Whole-data ratio estimate. + denom : np.matrix with shape (n_blocks, p) + Denominator delete values. + numer : np.matrix with shape (n_blocks, p) + Numerator delete values. + + Returns + ------- + pseudovalues : + Ratio Jackknife Pseudovalues. + + Raises + ------ + ValueError : + If numer.shape != denom.shape. + + ''' + n_blocks, p = denom.shape + pseudovalues = np.zeros((n_blocks, p)) + for j in range(0, n_blocks): + pseudovalues[j, ...] = n_blocks * est - \ + (n_blocks - 1) * numer[j, ...] / denom[j, ...] + + return pseudovalues + + + + +class Jackknife_Ridge(Jackknife): + + def __init__(self, x, y, n_blocks=None, separators=None, chr_num=None, verbose=True, + num_lambdas=100, approx_ridge=False, + ridge_lambda=None, use_1se=False, has_intercept=False, standardize=True, + skip_ridge_jackknife=True, num_chr_sets=2, num_chr=22): + + #sanity checks + assert chr_num is not None + # # # chr_num[:100000]=1 + # # # chr_num[100000:]=2 + assert len(np.unique(chr_num)) > 1 + + #init stuff + Jackknife.__init__(self, x, y, n_blocks=n_blocks, separators=separators) + self.use_1se = use_1se + self.verbose=verbose + self.has_intercept = has_intercept + self.num_chr = num_chr + + ###define chromosome sets + assert num_chr_sets>1 + + if num_chr_sets == 2: + #Use the good old fashioned odd/even chromosome split + chromosomes = np.sort(np.unique(chr_num)) + self.chromosome_sets = [] + self.chromosome_sets.append(chromosomes[chromosomes%2==0]) + self.chromosome_sets.append(chromosomes[chromosomes%2!=0]) + elif num_chr_sets == self.num_chr: + self.chromosome_sets = [np.array([c]) for c in range(1, self.num_chr+1)] + else: + chr_sizes = np.bincount(chr_num)[1:] + assert num_chr_sets<=len(chr_sizes) + chr_assignments = self._divide_chromosomes_to_sets(chr_sizes, num_chr_sets) + self.chromosome_sets = [] + for set_i in range(num_chr_sets): + self.chromosome_sets.append(np.where(chr_assignments==set_i)[0]+1) + + #make sure we work with numpy arrays, not dataframes + try: x=x.values + except: pass + try: y=y.values + except: pass + try: constraints=constraints.values + except: pass + try: chr_num=chr_num.values + except: pass + + #make y look like a vector + assert y.shape[1]==1 + y = y[:,0] + + #standardize x + if standardize: + x_l2 = np.sqrt(np.einsum('ij,ij->j', x, x)) + x /= x_l2 + else: + x_l2 = None + + #Create a set of ridge lambdas to evaluate + XTX_all = x.T.dot(x) + XTy_all = y.dot(x) + mean_diag = np.mean(np.diag(XTX_all)) + self.ridge_lambdas = np.logspace(np.log10(mean_diag*1e-8), np.log10(mean_diag*1e2), num=num_lambdas) + + #find best lambda (using off-chromosome estimation) and estimate taus + if ridge_lambda is not None: + assert self.approx_ridge + best_lambda = ridge_lambda + else: + best_lambda, r2_best_lambda = self._find_best_lambda(x, y, XTX_all, XTy_all, chr_num) + self.est = np.atleast_2d(self._est_ridge(XTX_all, XTy_all, best_lambda)) + self.r2_best_lambda = r2_best_lambda + if standardize: + self.est /= x_l2 + + + #LOCO (leave one chromosome out) computations + self.est_chr_lstsq, self.est_chr_ridge, self.est_loco_lstsq, self.est_loco_ridge = \ + self._est_taus_loco(x, y, XTX_all, XTy_all, chr_num, best_lambda, standardize, x_l2) + + #run jackknife + if not skip_ridge_jackknife: + self.delete_values = np.empty((len(self.separators)-1, self.est.shape[1]), dtype=np.float32) + self.est_chr_lstsq_jk_list = [] + self.est_chr_ridge_jk_list = [] + self.est_loco_lstsq_jk_list = [] + self.est_loco_ridge_jk_list = [] + + logging.info('Running ridge jackknife...') + self.best_r2_jk_noblock = np.zeros(len(self.separators) - 1) + for block_i in tqdm(range(len(self.separators) - 1)): + + #prepare data structures + x_block = x[self.separators[block_i]:self.separators[block_i+1], ...] + y_block = y[self.separators[block_i]:self.separators[block_i+1], ...] + XTX_noblock = XTX_all - x_block.T.dot(x_block) + XTy_noblock = XTy_all - y_block.dot(x_block) + slice_block = slice(self.separators[block_i], self.separators[block_i+1]) + x_noblock = np.delete(x, slice_block, axis=0) + y_noblock = np.delete(y, slice_block, axis=0) + chr_noblock = np.delete(chr_num, slice_block, axis=0) + + #find best lambda for this jackknife block + if approx_ridge: + best_lambda_noblock = best_lambda + else: + best_lambda_noblock, r2_noblock = self._find_best_lambda(x_noblock, y_noblock, XTX_noblock, XTy_noblock, chr_noblock) + self.best_r2_jk_noblock[block_i] = r2_noblock + + #main jackknife estimation + est_block = self._est_ridge(XTX_noblock, XTy_noblock, best_lambda_noblock) + self.delete_values[block_i, ...] = est_block + + #jackknife LOCO computation + est_chr_lstsq, est_chr_ridge, est_loco_lstsq, est_loco_ridge = \ + self._est_taus_loco(x_noblock, y_noblock, XTX_noblock, XTy_noblock, + chr_noblock, best_lambda_noblock, standardize, x_l2) + self.est_chr_lstsq_jk_list.append(est_chr_lstsq) + self.est_chr_ridge_jk_list.append(est_chr_ridge) + self.est_loco_lstsq_jk_list.append(est_loco_lstsq) + self.est_loco_ridge_jk_list.append(est_loco_ridge) + if standardize: self.delete_values /= x_l2 + + + + + #compute jackknife pseudo-values + self.pseudovalues = self.delete_values_to_pseudovalues(self.delete_values, self.est) + (self.jknife_est, self.jknife_var, self.jknife_se, self.jknife_cov) = self.jknife(self.pseudovalues) + + #restore original x + if standardize: x *= x_l2 + + + def _divide_chromosomes_to_sets(self, chr_sizes, num_sets): + chr_order = np.argsort(chr_sizes)[::-1] #np.arange(len(chr_sizes)) + chr_assignments = np.zeros(self.num_chr, dtype=np.int64) - 1 + chr_assignments[chr_order[:num_sets]] = np.arange(num_sets) + set_sizes = chr_sizes[chr_order[:num_sets]].copy() + for c_i in chr_order[num_sets : len(chr_sizes)]: + smallest_set = np.argmin(set_sizes) + chr_assignments[c_i] = smallest_set + set_sizes[smallest_set] += chr_sizes[c_i] + assert set_sizes.sum() == chr_sizes.sum() + return chr_assignments + + + + def _est_taus_loco(self, x, y, XTX, XTy, chr_num, ridge_lambda, standardize, x_l2=None, reestimate_lambda=False): + chromosomes = np.sort(np.unique(chr_num)) + est_set_lstsq = np.empty((len(self.chromosome_sets), x.shape[1]), dtype=np.float32) + est_noset_lstsq = np.empty((len(self.chromosome_sets), x.shape[1]), dtype=np.float32) + est_set_ridge = np.empty((len(self.chromosome_sets), x.shape[1]), dtype=np.float32) + est_noset_ridge = np.empty((len(self.chromosome_sets), x.shape[1]), dtype=np.float32) + tqdm_chr_sets = tqdm(self.chromosome_sets) + logging.info('Estimating annotation coefficients for each chromosomes set') + for set_i, chromosome_set in enumerate(tqdm_chr_sets): + is_in_set = np.isin(chr_num, chromosome_set) + if not np.any(is_in_set): continue + x_set = x[is_in_set] + y_set = y[is_in_set] + XTX_set = x_set.T.dot(x_set) + XTy_set = y_set.dot(x_set) + XTX_noset = XTX - XTX_set + XTy_noset = XTy - XTy_set + + if (not reestimate_lambda) or (len(chromosomes) <= 2): + best_lambda_noset = ridge_lambda + best_lambda_set = ridge_lambda + else: + x_loco = x[~is_in_set] + y_loco = y[~is_in_set] + chr_loco = chr_num[~is_in_set] + best_lambda_noset, r2_noset = self._find_best_lambda(x_loco, y_loco, XTX_noset, XTy_noset, chr_loco) + if len(chromosome_set) == 1: + best_lambda_set = ridge_lambda + else: + best_lambda_set, r2_set = self._find_best_lambda(x_set, y_set, XTX_set, XTy_set, chr_num[is_in_set]) + est_set_lstsq[set_i, :] = self._est_ridge(XTX_set, XTy_set, ridge_lambda=0) + est_set_ridge[set_i, :] = self._est_ridge(XTX_set, XTy_set, best_lambda_set) + est_noset_lstsq[set_i, :] = self._est_ridge(XTX_noset, XTy_noset, ridge_lambda=0) + est_noset_ridge[set_i, :] = self._est_ridge(XTX_noset, XTy_noset, best_lambda_noset) + ###import ipdb; ipdb.set_trace() + + if standardize: + est_set_lstsq /= x_l2 + est_set_ridge /= x_l2 + est_noset_lstsq /= x_l2 + est_noset_ridge /= x_l2 + + return est_set_lstsq, est_set_ridge, est_noset_lstsq, est_noset_ridge + + + def _find_best_lambda(self, x, y, XTX, XTy, chr_num): + chromosomes = np.sort(np.unique(chr_num)) + assert len(chromosomes) > 1 + num_lambdas = len(self.ridge_lambdas) + y_pred_lambdas = np.empty((chr_num.shape[0], num_lambdas), dtype=np.float32) + if self.verbose: + y_pred_lambdas_lstsq = np.empty(chr_num.shape[0], dtype=np.float32) + logging.info('iterating over chromosomes to compute XTX, XTy...') + for chr_i, left_out_chr in enumerate(tqdm(chromosomes)): + is_chr = (chr_num == left_out_chr) + chr_inds = np.where(is_chr)[0] + assert np.all(chr_inds == np.arange(chr_inds[0], chr_inds[-1]+1)) + chr_slice = slice(chr_inds[0], chr_inds[-1]+1) + x_chr = x[chr_slice] + y_chr = y[chr_slice] + XTX_loco = XTX - x_chr.T.dot(x_chr) + XTy_loco = XTy - y_chr.dot(x_chr) + y_pred_lambdas[chr_slice, :] = self._predict_lambdas(XTX_loco, XTy_loco, x_chr) + + if self.verbose: + tau_lstsq_loco = self._est_ridge(XTX_loco, XTy_loco, 0) + y_pred_lambdas_lstsq[chr_slice] = x_chr.dot(tau_lstsq_loco) + + #Assign an r2 score to each lambda + score_lambdas = np.empty(num_lambdas, dtype=np.float32) + logging.info('Evaluating Ridge lambdas...') + for r_i in tqdm(range(num_lambdas)): + score_lambdas[r_i] = r2_score(y, y_pred_lambdas[:,r_i]) + + #choose lambda based on the 1SE rule? + if self.use_1se: + score_folds = np.empty(len(chromosomes), dtype=np.float32) + for chr_i, left_out_chr in enumerate(chromosomes): + is_chr = (chr_num == left_out_chr) + score_folds[chr_i] = r2_score(y[is_chr], y_pred_lambdas[is_chr, best_lambda_index]) + scores_std = np.std(score_folds) + best_score = score_lambdas[best_lambda_index] + assert np.isclose(best_score, np.max(score_lambdas)) + best_lambda_index = np.where(score_lambdas > best_score - scores_std)[0][-1] + else: + best_lambda_index = np.argmax(score_lambdas) + + best_lambda = self.ridge_lambdas[best_lambda_index] + if self.verbose: + score_lstsq = r2_score(y, y_pred_lambdas_lstsq) + logging.info('Selected ridge lambda: %0.4e (%d/%d) score: %0.4e score lstsq: %0.4e'%(best_lambda, + best_lambda_index+1, num_lambdas, score_lambdas[best_lambda_index], score_lstsq)) + + return best_lambda, score_lambdas[best_lambda_index] + + + def _predict_lambdas(self, XTX_train, XTy_train, X_validation): + tau_est_ridge = np.empty((XTX_train.shape[0], len(self.ridge_lambdas)), dtype=np.float32) + for r_i, r in enumerate(self.ridge_lambdas): + tau_est_ridge[:, r_i] = self._est_ridge(XTX_train, XTy_train, r) + y_pred = X_validation.dot(tau_est_ridge) + return y_pred + + + def _est_ridge(self, XTX, XTy, ridge_lambda): + I = np.eye(XTX.shape[0]) * ridge_lambda + if self.has_intercept: I[-1,-1]=0 + return np.linalg.solve(XTX+I, XTy) + + + diff --git a/SCRIPTS/polyfun/ldsc_polyfun/ldscore.py b/SCRIPTS/polyfun/ldsc_polyfun/ldscore.py new file mode 100644 index 0000000..28e6ea1 --- /dev/null +++ b/SCRIPTS/polyfun/ldsc_polyfun/ldscore.py @@ -0,0 +1,420 @@ + +import numpy as np +import bitarray as ba +from tqdm import tqdm +import logging + + +def getBlockLefts(coords, max_dist): + ''' + Converts coordinates + max block length to the a list of coordinates of the leftmost + SNPs to be included in blocks. + + Parameters + ---------- + coords : array + Array of coordinates. Must be sorted. + max_dist : float + Maximum distance between SNPs included in the same window. + + Returns + ------- + block_left : 1D np.ndarray with same length as block_left + block_left[j] := min{k | dist(j, k) < max_dist}. + + ''' + M = len(coords) + j = 0 + block_left = np.zeros(M) + for i in range(M): + while j < M and abs(coords[j] - coords[i]) > max_dist: + j += 1 + + block_left[i] = j + + return block_left + + +def block_left_to_right(block_left): + ''' + Converts block lefts to block rights. + + Parameters + ---------- + block_left : array + Array of block lefts. + + Returns + ------- + block_right : 1D np.ndarray with same length as block_left + block_right[j] := max {k | block_left[k] <= j} + + ''' + M = len(block_left) + j = 0 + block_right = np.zeros(M) + for i in range(M): + while j < M and block_left[j] <= i: + j += 1 + + block_right[i] = j + + return block_right + + +class __GenotypeArrayInMemory__(object): + ''' + Parent class for various classes containing interfaces for files with genotype + matrices, e.g., plink .bed files, etc + ''' + def __init__(self, fname, n, snp_list, keep_snps=None, keep_indivs=None, mafMin=None): + self.m = len(snp_list.IDList) + self.n = n + self.keep_snps = keep_snps + self.keep_indivs = keep_indivs + self.df = np.array(snp_list.df[['CHR', 'SNP', 'BP', 'CM']]) + self.colnames = ['CHR', 'SNP', 'BP', 'CM'] + self.mafMin = mafMin if mafMin is not None else 0 + self._currentSNP = 0 + (self.nru, self.geno) = self.__read__(fname, self.m, n) + # filter individuals + if keep_indivs is not None: + keep_indivs = np.array(keep_indivs, dtype='int') + if np.any(keep_indivs > self.n): + raise ValueError('keep_indivs indices out of bounds') + + (self.geno, self.m, self.n) = self.__filter_indivs__(self.geno, keep_indivs, self.m, + self.n) + + if self.n > 0: + logging.info('After filtering, {n} individuals remain'.format(n=self.n)) + else: + raise ValueError('After filtering, no individuals remain') + + # filter SNPs + if keep_snps is not None: + keep_snps = np.array(keep_snps, dtype='int') + if np.any(keep_snps > self.m): # if keep_snps is None, this returns False + raise ValueError('keep_snps indices out of bounds') + + (self.geno, self.m, self.n, self.kept_snps, self.freq) = self.__filter_snps_maf__( + self.geno, self.m, self.n, self.mafMin, keep_snps) + + if self.m > 0: + logging.info('After filtering, {m} SNPs remain'.format(m=self.m)) + else: + raise ValueError('After filtering, no SNPs remain') + + self.df = self.df[self.kept_snps, :] + self.maf = np.minimum(self.freq, np.ones(self.m)-self.freq) + self.sqrtpq = np.sqrt(self.freq*(np.ones(self.m)-self.freq)) + self.df = np.c_[self.df, self.maf] + self.colnames.append('MAF') + + def __read__(self, fname, m, n): + raise NotImplementedError + + def __filter_indivs__(geno, keep_indivs, m, n): + raise NotImplementedError + + def __filter_maf_(geno, m, n, maf): + raise NotImplementedError + + def ldScoreVarBlocks(self, block_left, c, annot=None): + '''Computes an unbiased estimate of L2(j) for j=1,..,M.''' + func = lambda x: self.__l2_unbiased__(x, self.n) + snp_getter = self.nextSNPs + return self.__corSumVarBlocks__(block_left, c, func, snp_getter, annot) + + def ldScoreBlockJackknife(self, block_left, c, annot=None, jN=10): + func = lambda x: np.square(x) + snp_getter = self.nextSNPs + return self.__corSumBlockJackknife__(block_left, c, func, snp_getter, annot, jN) + + def __l2_unbiased__(self, x, n): + denom = n-2 if n > 2 else n # allow n<2 for testing purposes + sq = np.square(x) + return sq - (1-sq) / denom + + # general methods for calculating sums of Pearson correlation coefficients + def __corSumVarBlocks__(self, block_left, c, func, snp_getter, annot=None): + ''' + Parameters + ---------- + block_left : np.ndarray with shape (M, ) + block_left[i] = index of leftmost SNP included in LD Score of SNP i. + if c > 1, then only entries that are multiples of c are examined, and it is + assumed that block_left[a*c+i] = block_left[a*c], except at + the beginning of the chromosome where the 0th SNP is included in the window. + + c : int + Chunk size. + func : function + Function to be applied to the genotype correlation matrix. Before dotting with + annot. Examples: for biased L2, np.square. For biased L4, + lambda x: np.square(np.square(x)). For L1, lambda x: x. + snp_getter : function(int) + The method to be used to get the next SNPs (normalized genotypes? Normalized + genotypes with the minor allele as reference allele? etc) + annot: numpy array with shape (m,n_a) + SNP annotations. + + Returns + ------- + cor_sum : np.ndarray with shape (M, num_annots) + Estimates. + + ''' + m, n = self.m, self.n + block_sizes = np.array(np.arange(m) - block_left) + block_sizes = np.ceil(block_sizes / c)*c + if annot is None: + annot = np.ones((m, 1)) + else: + annot_m = annot.shape[0] + if annot_m != self.m: + raise ValueError('Incorrect number of SNPs in annot') + + n_a = annot.shape[1] # number of annotations + cor_sum = np.zeros((m, n_a)) + # b = index of first SNP for which SNP 0 is not included in LD Score + b = np.nonzero(block_left > 0) + if np.any(b): + b = b[0][0] + else: + b = m + b = int(np.ceil(b/c)*c) # round up to a multiple of c + if b > m: + c = 1 + b = m + l_A = 0 # l_A := index of leftmost SNP in matrix A + A = snp_getter(b) + rfuncAB = np.zeros((b, c)) + rfuncBB = np.zeros((c, c)) + # chunk inside of block + logging.info('Applying initial ldscores loop') + for l_B in tqdm(range(0, b, c)): # l_B := index of leftmost SNP in matrix B + B = A[:, l_B:l_B+c] + np.dot(A.T, B / n, out=rfuncAB) + rfuncAB = func(rfuncAB) + cor_sum[l_A:l_A+b, :] += np.dot(rfuncAB, annot[l_B:l_B+c, :]) + # chunk to right of block + b0 = b + md = int(c*np.floor(m/c)) + end = md + 1 if md != m else md + logging.info('Applying main ldscores loop...') + for l_B in tqdm(range(b0, end, c)): + # check if the annot matrix is all zeros for this block + chunk + # this happens w/ sparse categories (i.e., pathways) + # update the block + old_b = b + b = int(block_sizes[l_B]) + if l_B > b0 and b > 0: + # block_size can't increase more than c + # block_size can't be less than c unless it is zero + # both of these things make sense + A = np.hstack((A[:, old_b-b+c:old_b], B)) + l_A += old_b-b+c + elif l_B == b0 and b > 0: + A = A[:, b0-b:b0] + l_A = b0-b + elif b == 0: # no SNPs to left in window, e.g., after a sequence gap + A = np.array(()).reshape((n, 0)) + l_A = l_B + if l_B == md: + c = m - md + rfuncAB = np.zeros((b, c)) + rfuncBB = np.zeros((c, c)) + if b != old_b: + rfuncAB = np.zeros((b, c)) + + B = snp_getter(c) + p1 = np.all(annot[l_A:l_A+b, :] == 0) + p2 = np.all(annot[l_B:l_B+c, :] == 0) + if p1 and p2: + continue + + np.dot(A.T, B / n, out=rfuncAB) + rfuncAB = func(rfuncAB) + cor_sum[l_A:l_A+b, :] += np.dot(rfuncAB, annot[l_B:l_B+c, :]) + cor_sum[l_B:l_B+c, :] += np.dot(annot[l_A:l_A+b, :].T, rfuncAB).T + np.dot(B.T, B / n, out=rfuncBB) + rfuncBB = func(rfuncBB) + cor_sum[l_B:l_B+c, :] += np.dot(rfuncBB, annot[l_B:l_B+c, :]) + + return cor_sum + + +class PlinkBEDFile(__GenotypeArrayInMemory__): + ''' + Interface for Plink .bed format + ''' + def __init__(self, fname, n, snp_list, keep_snps=None, keep_indivs=None, mafMin=None): + self._bedcode = { + 2: ba.bitarray('11'), + 9: ba.bitarray('10'), + 1: ba.bitarray('01'), + 0: ba.bitarray('00') + } + + __GenotypeArrayInMemory__.__init__(self, fname, n, snp_list, keep_snps=keep_snps, + keep_indivs=keep_indivs, mafMin=mafMin) + + def __read__(self, fname, m, n): + if not fname.endswith('.bed'): + raise ValueError('.bed filename must end in .bed') + + fh = open(fname, 'rb') + magicNumber = ba.bitarray(endian="little") + magicNumber.fromfile(fh, 2) + bedMode = ba.bitarray(endian="little") + bedMode.fromfile(fh, 1) + e = (4 - n % 4) if n % 4 != 0 else 0 + nru = n + e + self.nru = nru + # check magic number + if magicNumber != ba.bitarray('0011011011011000'): + raise IOError("Magic number from Plink .bed file not recognized") + + if bedMode != ba.bitarray('10000000'): + raise IOError("Plink .bed file must be in default SNP-major mode") + + # check file length + self.geno = ba.bitarray(endian="little") + self.geno.fromfile(fh) + self.__test_length__(self.geno, self.m, self.nru) + return (self.nru, self.geno) + + def __test_length__(self, geno, m, nru): + exp_len = 2*m*nru + real_len = len(geno) + if real_len != exp_len: + s = "Plink .bed file has {n1} bits, expected {n2}" + raise IOError(s.format(n1=real_len, n2=exp_len)) + + def __filter_indivs__(self, geno, keep_indivs, m, n): + n_new = len(keep_indivs) + e = (4 - n_new % 4) if n_new % 4 != 0 else 0 + nru_new = n_new + e + nru = self.nru + z = ba.bitarray(m*2*nru_new, endian="little") + z.setall(0) + for e, i in enumerate(keep_indivs): + z[2*e::2*nru_new] = geno[2*i::2*nru] + z[2*e+1::2*nru_new] = geno[2*i+1::2*nru] + + self.nru = nru_new + return (z, m, n_new) + + def __filter_snps_maf__(self, geno, m, n, mafMin, keep_snps): + ''' + Credit to Chris Chang and the Plink2 developers for this algorithm + Modified from plink_filter.c + https://github.com/chrchang/plink-ng/blob/master/plink_filter.c + + Genotypes are read forwards (since we are cheating and using endian="little") + + A := (genotype) & 1010... + B := (genotype) & 0101... + C := (A >> 1) & B + + Then + + a := A.count() = missing ct + hom major ct + b := B.count() = het ct + hom major ct + c := C.count() = hom major ct + + Which implies that + + missing ct = a - c + # of indivs with nonmissing genotype = n - a + c + major allele ct = b + c + major allele frequency = (b+c)/(2*(n-a+c)) + het ct + missing ct = a + b - 2*c + + Why does bitarray not have >> ???? + + ''' + nru = self.nru + m_poly = 0 + y = ba.bitarray() + if keep_snps is None: + keep_snps = range(m) + kept_snps = [] + freq = [] + logging.info('Applying SNPs filter...') + for j in tqdm(keep_snps): + z = geno[2*nru*j:2*nru*(j+1)] + A = z[0::2] + a = A.count() + B = z[1::2] + b = B.count() + c = (A & B).count() + major_ct = b + c # number of copies of the major allele + n_nomiss = n - a + c # number of individuals with nonmissing genotypes + f = major_ct / (2*n_nomiss) if n_nomiss > 0 else 0 + het_miss_ct = a+b-2*c # remove SNPs that are only either het or missing + if np.minimum(f, 1-f) > mafMin and het_miss_ct < n: + freq.append(f) + y += z + m_poly += 1 + kept_snps.append(j) + + return (y, m_poly, n, kept_snps, freq) + + def nextSNPs(self, b, minorRef=None): + ''' + Unpacks the binary array of genotypes and returns an n x b matrix of floats of + normalized genotypes for the next b SNPs, where n := number of samples. + + Parameters + ---------- + b : int + Number of SNPs to return. + minorRef: bool, default None + Should we flip reference alleles so that the minor allele is the reference? + (This is useful for computing l1 w.r.t. minor allele). + + Returns + ------- + X : np.array with dtype float64 with shape (n, b), where n := number of samples + Matrix of genotypes normalized to mean zero and variance one. If minorRef is + not None, then the minor allele will be the positive allele (i.e., two copies + of the minor allele --> a positive number). + + ''' + + try: + b = int(b) + if b <= 0: + raise ValueError("b must be > 0") + except TypeError: + raise TypeError("b must be an integer") + + if self._currentSNP + b > self.m: + s = '{b} SNPs requested, {k} SNPs remain' + raise ValueError(s.format(b=b, k=(self.m-self._currentSNP))) + + c = self._currentSNP + n = self.n + nru = self.nru + slice = self.geno[2*c*nru:2*(c+b)*nru] + X = np.array(slice.decode(self._bedcode), dtype="float64").reshape((b, nru)).T + X = X[0:n, :] + Y = np.zeros(X.shape) + for j in range(0, b): + newsnp = X[:, j] + ii = newsnp != 9 + avg = np.mean(newsnp[ii]) + newsnp[np.logical_not(ii)] = avg + denom = np.std(newsnp) + if denom == 0: + denom = 1 + + if minorRef is not None and self.freq[self._currentSNP + j] > 0.5: + denom = denom*-1 + + Y[:, j] = (newsnp - avg) / denom + + self._currentSNP += b + return Y diff --git a/SCRIPTS/polyfun/ldsc_polyfun/parse.py b/SCRIPTS/polyfun/ldsc_polyfun/parse.py new file mode 100644 index 0000000..7859a52 --- /dev/null +++ b/SCRIPTS/polyfun/ldsc_polyfun/parse.py @@ -0,0 +1,386 @@ +''' +(c) 2014 Brendan Bulik-Sullivan and Hilary Finucane + +This module contains functions for parsing various ldsc-defined file formats. + +''' + + +import numpy as np +import pandas as pd +import os +from tqdm import tqdm +import logging + +def series_eq(x, y): + '''Compare series, return False if lengths not equal.''' + return len(x) == len(y) and (x == y).all() + + +def read_csv(fh, **kwargs): + if fh.endswith('.hdf'): + df = pd.read_hdf(fh) + if 'usecols' in kwargs.keys(): + df = df[kwargs['usecols']] + elif fh.endswith('.parquet'): + df = pd.read_parquet(fh) + if 'usecols' in kwargs.keys(): + df = df[kwargs['usecols']] + else: + df = pd.read_csv(fh, delim_whitespace=True, na_values='.', **kwargs) + + return df + +def set_snpid_index(df): + df['A1_first'] = (df['A1'] < df['A2']) | (df['A1'].str.len()>1) | (df['A2'].str.len()>1) + df['A1s'] = df['A2'].copy() + df.loc[df['A1_first'], 'A1s'] = df.loc[df['A1_first'], 'A1'].copy() + df['A2s'] = df['A1'].copy() + df.loc[df['A1_first'], 'A2s'] = df.loc[df['A1_first'], 'A2'].copy() + df.index = df['CHR'].astype(str) + '.' + df['BP'].astype(str) + '.' + df['A1s'] + '.' + df['A2s'] + df.index.name = 'snpid' + df.drop(columns=['A1_first', 'A1s', 'A2s'], inplace=True) + return df + + + +def sub_chr(s, chr): + '''Substitute chr for @, else append chr to the end of str.''' + if '@' not in s: + s += '@' + + return s.replace('@', str(chr)) + + +def which_compression(fh): + '''Given a file prefix, figure out what sort of compression to use.''' + #import ipdb; ipdb.set_trace() + if os.access(fh + '.parquet', 4): + suffix = '.parquet' + compression = 'parquet' + elif os.access(fh + '.hdf', 4): + suffix = '.hdf' + compression = 'hdf' + elif os.access(fh + '.bz2', 4): + suffix = '.bz2' + compression = 'bz2' + elif os.access(fh + '.gz', 4): + suffix = '.gz' + compression = 'gzip' + elif os.access(fh, 4): + suffix = '' + compression = None + else: + raise IOError('Could not open {F}[./gz/bz2/.hdf/.parquet]'.format(F=fh)) + + return suffix, compression + + +def get_compression(fh): + '''Which sort of compression should we use with read_csv?''' + if fh.endswith('gz'): + compression = 'gzip' + elif fh.endswith('bz2'): + compression = 'bz2' + else: + compression = None + + return compression + + +def read_cts(fh, match_snps): + '''Reads files for --cts-bin.''' + compression = get_compression(fh) + cts = read_csv(fh, compression=compression, header=None, names=['SNP', 'ANNOT']) + if not series_eq(cts.SNP, match_snps): + raise ValueError('--cts-bin and the .bim file must have identical SNP columns.') + + return cts.ANNOT.values + + +def sumstats(fh, alleles=True, dropna=True): + '''Parses .sumstats files. See docs/file_formats_sumstats.txt.''' + dtype_dict = {'SNP': str, 'Z': float, 'N': float, 'A1': str, 'A2': str} + compression = get_compression(fh) + usecols = ['SNP', 'CHR', 'BP', 'Z', 'N'] + #if alleles: + usecols += ['A1', 'A2'] + + try: + x = read_csv(fh, usecols=usecols, dtype=dtype_dict, compression=compression) + except (AttributeError, ValueError) as e: + raise ValueError('Improperly formatted sumstats file: ' + str(e.args)) + + if dropna: + x = x.dropna(how='any') + + x = set_snpid_index(x) + x.drop(columns=['CHR', 'BP'], inplace=True) + + + return x + + +def ldscore_fromlist(flist, num=None): + '''Sideways concatenation of a list of LD Score files.''' + ldscore_array = [] + for fh_i, fh in enumerate(flist): + y = ldscore(fh, num) + if len(ldscore_array)>0: + + #make sure that all files contain the same SNPs in the same order + if ((not series_eq(y.index, ldscore_array[0].index) or not series_eq(y.SNP, ldscore_array[0].SNP))): + all_baseline_snps_found = np.all(ldscore_array[0].index.isin(y.index)) + if not all_baseline_snps_found: + raise ValueError('Some SNPs in the first set of annotations are not found in the one of the other sets of annotations') + extra_snps_found = np.any(~(y.index.isin(ldscore_array[0].index))) + if extra_snps_found: + logging.warning('some SNPs in one of the sets of annotations are not found in the first set of annotations. We will ignore these SNPs') + + #reorder the SNPs to make sure that they're in the corret order + y = y.loc[ldscore_array[0].index] + assert series_eq(y.index, ldscore_array[0].index) and series_eq(y.SNP, ldscore_array[0].SNP) + + # keep SNP and CHR column from only the first file + y = y.drop(columns=['SNP', 'CHR'], axis=1) + + new_col_dict = {c: c + '_' + str(fh_i) for c in y.columns if c not in ['SNP', 'CHR']} + y.rename(columns=new_col_dict, inplace=True) + ldscore_array.append(y) + + if len(ldscore_array)==1: + ldscores_all = ldscore_array[0] + else: + #ldscores_all = pd.concat(ldscore_array, axis=1) + ldscores_all = pd.concat(ldscore_array, axis=1) + return ldscores_all + + +def l2_parser(fh, compression): + '''Parse LD Score files''' + x = read_csv(fh, header=0, compression=compression) + if 'SNP' not in x.columns: + x.reset_index(inplace=True) + assert 'SNP' in x.columns + assert 'CHR' in x.columns + assert 'BP' in x.columns + if 'A1' in x.columns and 'A2' in x.columns: + x = set_snpid_index(x) + else: + ###x.set_index('SNP', inplace=True, drop=False) + logging.warning('%s doesn\'t have A1,A2 columns'%(fh)) + x.drop(columns=['MAF', 'CM', 'A1', 'A2'], errors='ignore', inplace=True) + return x + + +def annot_parser(fh, compression, frqfile_full=None, compression_frq=None, anno=None): + '''Parse annot files''' + df_annot = read_csv(fh, header=0, compression=compression) + df_annot.drop(columns=['SNP', 'BP', 'CM', 'CHR', 'A1', 'A2'], inplace=True, errors='ignore') + df_annot = df_annot.astype(float) + if anno is not None: + df_annot = df_annot.loc[:, [c for c in df_annot.columns if (c=='SNP' or c in anno)]] + if frqfile_full is not None: + df_frq = frq_parser(frqfile_full, compression_frq) + df_annot = df_annot[(.95 > df_frq.FRQ) & (df_frq.FRQ > 0.05)] + return df_annot + + +def frq_parser(fh, compression): + '''Parse frequency files.''' + df = read_csv(fh, header=0, compression=compression) + if 'MAF' in df.columns: + df.rename(columns={'MAF': 'FRQ'}, inplace=True) + return df[['SNP', 'FRQ']] + + +def ldscore(fh, num=None): + '''Parse .l2.ldscore files, split across num chromosomes. See docs/file_formats_ld.txt.''' + suffix = '.l2.ldscore' + if num is not None: # num files, e.g., one per chromosome + first_fh = sub_chr(fh, 1) + suffix + s, compression = which_compression(first_fh) + ###compression = None + chr_ld = [] + for i in tqdm(range(1, num+1)): + chr_ld.append(l2_parser(sub_chr(fh, i) + suffix + s, compression)) + x = pd.concat(chr_ld, axis=0) # automatically sorted by chromosome + del chr_ld + else: # just one file + s, compression = which_compression(fh + suffix) + x = l2_parser(fh + suffix + s, compression) + + #sort array if needed + is_sorted = True + for c in x['CHR'].unique(): + is_sorted = np.all(np.diff(x.loc[x['CHR']==c, 'BP']) >= 0) + if not is_sorted: break + if not is_sorted: + x.sort_values(by=['CHR', 'BP'], inplace=True) # SEs will be wrong unless sorted + + + x.drop(columns=['BP'], inplace=True) + + if x.index.name == 'snpid': + is_duplicate_snp = x.index.duplicated() + if np.any(is_duplicate_snp): + index_dup_snps = x.index[is_duplicate_snp] + index_dup_snps = index_dup_snps[~index_dup_snps.duplicated()] + error_msg = 'Duplicate SNPs were found in the input data:\n%s'%(index_dup_snps) + raise ValueError(error_msg) + else: + if np.any(x['SNP'].duplicated()): + x.drop_duplicates(subset='SNP', inplace=True) + return x + + + +def M(fh, num=None, N=2, common=False): + '''Parses .l{N}.M files, split across num chromosomes. See docs/file_formats_ld.txt.''' + parsefunc = lambda y: [float(z) for z in open(y, 'r').readline().split()] + suffix = '.l' + str(N) + '.M' + if common: + suffix += '_5_50' + + if num is not None: + x = np.sum([parsefunc(sub_chr(fh, i) + suffix) for i in range(1, num + 1)], axis=0) + else: + x = parsefunc(fh + suffix) + + return np.array(x).reshape((1, len(x))) + + +def M_fromlist(flist, num=None, N=2, common=False): + '''Read a list of .M* files and concatenate sideways.''' + return np.hstack([M(fh, num, N, common) for fh in flist]) + + +def annot(fh_list, num=None, frqfile=None, anno=None): + ''' + Parses .annot files and returns an overlap matrix. See docs/file_formats_ld.txt. + If num is not None, parses .annot files split across [num] chromosomes (e.g., the + output of parallelizing ldsc.py --l2 across chromosomes). + + ''' + annot_suffix = ['.annot' for fh in fh_list] + annot_compression = [] + if num is not None: # 22 files, one for each chromosome + for i, fh in enumerate(fh_list): + first_fh = sub_chr(fh, 1) + annot_suffix[i] + annot_s, annot_comp_single = which_compression(first_fh) + annot_suffix[i] += annot_s + annot_compression.append(annot_comp_single) + + if frqfile is not None: + frq_suffix = '.frq' + first_frqfile = sub_chr(frqfile, 1) + frq_suffix + frq_s, frq_compression = which_compression(first_frqfile) + frq_suffix += frq_s + + y = [] + M_tot = 0 + logging.info('reading .annot files to compute the overlap matrix...') + for chr in tqdm(range(1, num + 1)): + if frqfile is not None: + df_annot_chr_list = [annot_parser(sub_chr(fh, chr) + annot_suffix[i], annot_compression[i], + sub_chr(frqfile, chr) + frq_suffix, frq_compression, anno=anno) + for i, fh in enumerate(fh_list)] + else: + df_annot_chr_list = [annot_parser(sub_chr(fh, chr) + annot_suffix[i], annot_compression[i], anno=anno) + for i, fh in enumerate(fh_list)] + + if anno is not None: + list_list_c = [list(df.columns) for df in df_annot_chr_list] + list_c = [c for c_list in list_list_c for c in c_list] + for a in anno: + assert a in list_c, 'Annotation %s was not found in the annotations file'%(a) + + annot_matrix_chr_list = [np.matrix(df_annot_chr) for df_annot_chr in df_annot_chr_list] + if len(annot_matrix_chr_list)==1: + annot_matrix_chr = annot_matrix_chr_list[0] + else: + annot_matrix_chr = np.hstack(annot_matrix_chr_list) + + y.append(np.dot(annot_matrix_chr.T, annot_matrix_chr)) + M_tot += len(df_annot_chr_list[0]) + + x = sum(y) + else: # just one file + for i, fh in enumerate(fh_list): + annot_s, annot_comp_single = which_compression(fh + annot_suffix[i]) + annot_suffix[i] += annot_s + annot_compression.append(annot_comp_single) + + if frqfile is not None: + frq_suffix = '.frq' + frq_s, frq_compression = which_compression(frqfile + frq_suffix) + frq_suffix += frq_s + + df_annot_list = [annot_parser(fh + annot_suffix[i], annot_compression[i], + frqfile + frq_suffix, frq_compression, anno=anno) for i, fh in enumerate(fh_list)] + + else: + df_annot_list = [annot_parser(fh + annot_suffix[i], annot_compression[i], anno=anno) + for i, fh in enumerate(fh_list)] + + if anno is not None: + list_list_c = [list(df.columns) for df in df_annot_list] + list_c = [c for c_list in list_list_c for c in c_list] + for a in anno: + assert a in list_c, 'Annotation %s was not found in the annotations file'%(a) + + annot_matrix_list = [np.matrix(y) for y in df_annot_list] + annot_matrix = np.hstack(annot_matrix_list) + x = np.dot(annot_matrix.T, annot_matrix) + M_tot = len(df_annot_list[0]) + + return x, M_tot + + +def __ID_List_Factory__(colnames, keepcol, fname_end, header=None, usecols=None): + + class IDContainer(object): + + def __init__(self, fname): + self.__usecols__ = usecols + self.__colnames__ = colnames + self.__keepcol__ = keepcol + self.__fname_end__ = fname_end + self.__header__ = header + self.__read__(fname) + self.n = len(self.df) + + def __read__(self, fname): + end = self.__fname_end__ + if end and not fname.endswith(end): + raise ValueError('{f} filename must end in {f}'.format(f=end)) + + comp = get_compression(fname) + self.df = pd.read_csv(fname, header=self.__header__, usecols=self.__usecols__, + delim_whitespace=True, compression=comp) + + if self.__colnames__: + self.df.columns = self.__colnames__ + + if self.__keepcol__ is not None: + self.IDList = self.df.iloc[:, [self.__keepcol__]].astype('object') + + def loj(self, externalDf): + '''Returns indices of those elements of self.IDList that appear in exernalDf.''' + r = externalDf.columns[0] + l = self.IDList.columns[0] + merge_df = externalDf.iloc[:, [0]] + merge_df['keep'] = True + z = pd.merge(self.IDList, merge_df, how='left', left_on=l, right_on=r, + sort=False) + ii = z['keep'] == True + return np.nonzero(ii)[0] + + return IDContainer + + +PlinkBIMFile = __ID_List_Factory__(['CHR', 'SNP', 'CM', 'BP', 'A1', 'A2'], 1, '.bim', usecols=[0, 1, 2, 3, 4, 5]) +PlinkFAMFile = __ID_List_Factory__(['IID'], 0, '.fam', usecols=[1]) +FilterFile = __ID_List_Factory__(['ID'], 0, None, usecols=[0]) +AnnotFile = __ID_List_Factory__(None, 2, None, header=0, usecols=None) +ThinAnnotFile = __ID_List_Factory__(None, None, None, header=0, usecols=None) diff --git a/SCRIPTS/polyfun/ldsc_polyfun/regressions.py b/SCRIPTS/polyfun/ldsc_polyfun/regressions.py new file mode 100644 index 0000000..b96f13a --- /dev/null +++ b/SCRIPTS/polyfun/ldsc_polyfun/regressions.py @@ -0,0 +1,823 @@ +''' +(c) 2014 Brendan Bulik-Sullivan and Hilary Finucane + +Estimators of heritability and genetic correlation. + +Shape convention is (n_snp, n_annot) for all classes. +Last column = intercept. + +''' + +import numpy as np +import pandas as pd +from scipy.stats import norm, chi2 +from . import jackknife as jk +from .irwls import IRWLS +from scipy.stats import t as tdist +from collections import namedtuple +np.seterr(divide='raise', invalid='raise') + +s = lambda x: remove_brackets(str(np.matrix(x))) + + +def update_separators(s, ii): + '''s are separators with ii masked. Returns unmasked separators.''' + maplist = np.arange(len(ii))[np.squeeze(ii)] + mask_to_unmask = lambda i: maplist[i] + t = np.apply_along_axis(mask_to_unmask, 0, s[1:-1]) + t = np.hstack(((0), t, (len(ii)))) + return t + + +def p_z_norm(est, se): + '''Convert estimate and se to Z-score and P-value.''' + try: + Z = est / se + except (FloatingPointError, ZeroDivisionError): + Z = float('inf') + + P = chi2.sf(Z ** 2, 1, loc=0, scale=1) # 0 if Z=inf + return P, Z + + +def remove_brackets(x): + '''Get rid of brackets and trailing whitespace in numpy arrays.''' + return x.replace('[', '').replace(']', '').strip() + + +def append_intercept(x): + ''' + Appends an intercept term to the design matrix for a linear regression. + + Parameters + ---------- + x : np.matrix with shape (n_row, n_col) + Design matrix. Columns are predictors; rows are observations. + + Returns + ------- + x_new : np.matrix with shape (n_row, n_col+1) + Design matrix with intercept term appended. + + ''' + n_row = x.shape[0] + intercept = np.ones((n_row, 1), dtype=np.float32) + x_new = np.concatenate((x, intercept), axis=1) + return x_new + + +def remove_intercept(x): + '''Removes the last column.''' + n_col = x.shape[1] + return x[:, 0:n_col - 1] + + +def gencov_obs_to_liab(gencov_obs, P1, P2, K1, K2): + ''' + Converts genetic covariance on the observed scale in an ascertained sample to genetic + covariance on the liability scale in the population + + Parameters + ---------- + gencov_obs : float + Genetic covariance on the observed scale in an ascertained sample. + P1, P2 : float in (0,1) + Prevalences of phenotypes 1,2 in the sample. + K1, K2 : float in (0,1) + Prevalences of phenotypes 1,2 in the population. + + Returns + ------- + gencov_liab : float + Genetic covariance between liabilities in the population. + + Note: if a trait is a QT, set P = K = None. + + ''' + c1 = 1 + c2 = 1 + if P1 is not None and K1 is not None: + c1 = np.sqrt(h2_obs_to_liab(1, P1, K1)) + if P2 is not None and K2 is not None: + c2 = np.sqrt(h2_obs_to_liab(1, P2, K2)) + + return gencov_obs * c1 * c2 + + +def h2_obs_to_liab(h2_obs, P, K): + ''' + Converts heritability on the observed scale in an ascertained sample to heritability + on the liability scale in the population. + + Parameters + ---------- + h2_obs : float + Heritability on the observed scale in an ascertained sample. + P : float in (0,1) + Prevalence of the phenotype in the sample. + K : float in (0,1) + Prevalence of the phenotype in the population. + + Returns + ------- + h2_liab : float + Heritability of liability in the population. + + ''' + if np.isnan(P) and np.isnan(K): + return h2_obs + if K <= 0 or K >= 1: + raise ValueError('K must be in the range (0,1)') + if P <= 0 or P >= 1: + raise ValueError('P must be in the range (0,1)') + + thresh = norm.isf(K) + conversion_factor = K ** 2 * \ + (1 - K) ** 2 / (P * (1 - P) * norm.pdf(thresh) ** 2) + return h2_obs * conversion_factor + + +class LD_Score_Regression(object): + + def __init__(self, y, x, w, N, M, n_blocks, intercept=None, slow=False, step1_ii=None, + old_weights=False, + keep_large=False, + nn=False, + chr_num=None, + verbose=True, + loco=False, + ridge_lambda=None, + standardize_ridge=True, + approx_ridge=False, + skip_ridge_jackknife=True, + num_chr_sets=2, + evenodd_split=False, + nnls_exact=False, + num_chr=22 + ): + + for i in [y, x, w, M, N]: + try: + if len(i.shape) != 2: + raise TypeError('Arguments must be 2D arrays.') + except AttributeError: + raise TypeError('Arguments must be arrays.') + + #find large-chi2 SNPs to keep + if keep_large: + is_large_chi2 = (y[:,0] > 80) + else: + is_large_chi2 = False + + n_snp, self.n_annot = x.shape + if any(i.shape != (n_snp, 1) for i in [y, w, N]): + raise ValueError( + 'N, weights and response (z1z2 or chisq) must have shape (n_snp, 1).') + if M.shape != (1, self.n_annot): + raise ValueError('M must have shape (1, n_annot).') + + M_tot = float(np.sum(M)) + x_tot = np.sum(x, axis=1).reshape((n_snp, 1)) + self.constrain_intercept = intercept is not None + self.intercept = intercept + self.n_blocks = n_blocks + tot_agg = self.aggregate(y, x_tot, N, M_tot, intercept) + initial_w = self._update_weights( + x_tot, w, N, M_tot, tot_agg, intercept) + initial_w = initial_w.astype(np.float32) + Nbar = np.mean(N) # keep condition number low + self.Nbar = Nbar + if len(np.unique(N))>1: + x = np.multiply(N, x) / Nbar + if not self.constrain_intercept: + x, x_tot = append_intercept(x), append_intercept(x_tot) + yp = y + else: + yp = y - intercept + self.intercept_se = 'NA' + del y + self.twostep_filtered = None + if loco and step1_ii is not None: + raise ValueError( + 'LOCO cannot be used with a two-step estimator') + if step1_ii is not None and self.constrain_intercept: + raise ValueError( + 'twostep is not compatible with constrain_intercept.') + elif step1_ii is not None and self.n_annot > 1: + raise ValueError( + 'twostep not compatible with partitioned LD Score yet.') + elif step1_ii is not None: + n1 = np.sum(step1_ii) + self.twostep_filtered = n_snp - n1 + x1 = x[np.squeeze(step1_ii), :] + yp1, w1, N1, initial_w1 = [a[step1_ii].reshape((n1, 1)) for a in (yp, w, N, initial_w)] + update_func1 = lambda a: self._update_func( + a, x1, w1, N1, M_tot, Nbar, ii=step1_ii) + step1_jknife = IRWLS( + x1, yp1, update_func1, n_blocks, slow=slow, w=initial_w1) + step1_int, _ = self._intercept(step1_jknife) + yp = yp - step1_int + x = remove_intercept(x) + x_tot = remove_intercept(x_tot) + update_func2 = lambda a: self._update_func( + a, x_tot, w, N, M_tot, Nbar, step1_int) + s = update_separators(step1_jknife.separators, step1_ii) + step2_jknife = IRWLS( + x, yp, update_func2, n_blocks, slow=slow, w=initial_w, separators=s) + c = np.sum(np.multiply(initial_w, x)) / \ + np.sum(np.multiply(initial_w, np.square(x))) + jknife = self._combine_twostep_jknives( + step1_jknife, step2_jknife, M_tot, c, Nbar) + elif old_weights: + initial_w = np.sqrt(initial_w) + if loco and (not self.constrain_intercept): + x_mean = x.mean(axis=0) + x_mean[-1]=0.0 + xc = x - x_mean + xc = IRWLS._weight(xc, initial_w) + + x = IRWLS._weight(x, initial_w) + y = IRWLS._weight(yp, initial_w) + + if nn: + assert not loco + jknife = jk.LstsqJackknifeSlow(x, y, is_large_chi2, n_blocks, evenodd_split=evenodd_split, nn=True, chr_num=chr_num, nnls_exact=nnls_exact) + else: + jknife = jk.LstsqJackknifeFast(x, y, is_large_chi2, n_blocks, evenodd_split=evenodd_split, chr_num=chr_num) + + if loco: + assert not nn + assert not keep_large + if self.constrain_intercept: + xc = x + + self.jknife_ridge = jk.Jackknife_Ridge(xc, y, n_blocks, + chr_num=chr_num, verbose=verbose, ridge_lambda=ridge_lambda, + has_intercept=(not self.constrain_intercept), + standardize=standardize_ridge, approx_ridge=approx_ridge, + skip_ridge_jackknife=skip_ridge_jackknife, num_chr_sets=num_chr_sets, num_chr=num_chr) + + else: + assert not loco + + update_func = lambda a: self._update_func( + a, x_tot, w, N, M_tot, Nbar, intercept) + jknife = IRWLS( + x, yp, update_func, n_blocks, slow=slow, w=initial_w) + + self.coef, self.coef_cov, self.coef_se = self._coef(jknife, Nbar) + self.cat, self.cat_cov, self.cat_se =\ + self._cat(jknife, M, Nbar, self.coef, self.coef_cov) + + self.tot, self.tot_cov, self.tot_se = self._tot(self.cat, self.cat_cov) + self.prop, self.prop_cov, self.prop_se =\ + self._prop(jknife, M, Nbar, self.cat, self.tot) + + self.enrichment, self.M_prop = self._enrichment( + M, M_tot, self.cat, self.tot) + if not self.constrain_intercept: + self.intercept, self.intercept_se = self._intercept(jknife) + + self.jknife = jknife + self.tot_delete_values = self._delete_vals_tot(jknife, Nbar, M) + self.part_delete_values = self._delete_vals_part(jknife, Nbar, M) + if not self.constrain_intercept: + self.intercept_delete_values = jknife.delete_values[ + :, self.n_annot] + + self.M = M + + @classmethod + def aggregate(cls, y, x, N, M, intercept=None): + if intercept is None: + intercept = cls.__null_intercept__ + + num = M * (np.mean(y) - intercept) + denom = np.mean(np.multiply(x, N)) + return num / denom + + def _update_func(self, x, ref_ld_tot, w_ld, N, M, Nbar, intercept=None, ii=None): + raise NotImplementedError + + def _delete_vals_tot(self, jknife, Nbar, M): + '''Get delete values for total h2 or gencov.''' + n_annot = self.n_annot + tot_delete_vals = jknife.delete_values[ + :, 0:n_annot] # shape (n_blocks, n_annot) + # shape (n_blocks, 1) + tot_delete_vals = np.dot(tot_delete_vals, M.T) / Nbar + return tot_delete_vals + + def _delete_vals_part(self, jknife, Nbar, M): + '''Get delete values for partitioned h2 or gencov.''' + n_annot = self.n_annot + return jknife.delete_values[:, 0:n_annot] / Nbar + + def _coef(self, jknife, Nbar): + '''Get coefficient estimates + cov from the jackknife.''' + n_annot = self.n_annot + coef = jknife.est[0, 0:n_annot] / Nbar + coef_cov = jknife.jknife_cov[0:n_annot, 0:n_annot] / Nbar ** 2 + coef_se = np.sqrt(np.diag(coef_cov)) + return coef, coef_cov, coef_se + + def _cat(self, jknife, M, Nbar, coef, coef_cov): + '''Convert coefficients to per-category h2 or gencov.''' + cat = np.multiply(M, coef) + cat_cov = np.multiply(np.dot(M.T, M), coef_cov) + cat_se = np.sqrt(np.diag(cat_cov)) + return cat, cat_cov, cat_se + + def _tot(self, cat, cat_cov): + '''Convert per-category h2 to total h2 or gencov.''' + tot = np.sum(cat) + tot_cov = np.sum(cat_cov) + tot_se = np.sqrt(tot_cov) + return tot, tot_cov, tot_se + + def _prop(self, jknife, M, Nbar, cat, tot): + '''Convert total h2 and per-category h2 to per-category proportion h2 or gencov.''' + n_annot = self.n_annot + n_blocks = jknife.delete_values.shape[0] + numer_delete_vals = np.multiply( + M, jknife.delete_values[:, 0:n_annot]) / Nbar # (n_blocks, n_annot) + denom_delete_vals = np.sum( + numer_delete_vals, axis=1).reshape((n_blocks, 1)) + denom_delete_vals = np.dot(denom_delete_vals, np.ones((1, n_annot))) + prop = jk.RatioJackknife( + cat / tot, numer_delete_vals, denom_delete_vals) + return prop.est, prop.jknife_cov, prop.jknife_se + + def _enrichment(self, M, M_tot, cat, tot): + '''Compute proportion of SNPs per-category enrichment for h2 or gencov.''' + M_prop = M / M_tot + enrichment = np.divide(cat, M) / (tot / M_tot) + return enrichment, M_prop + + def _intercept(self, jknife): + '''Extract intercept and intercept SE from block jackknife.''' + n_annot = self.n_annot + intercept = jknife.est[0, n_annot] + intercept_se = jknife.jknife_se[0, n_annot] + return intercept, intercept_se + + def _combine_twostep_jknives(self, step1_jknife, step2_jknife, M_tot, c, Nbar=1): + '''Combine free intercept and constrained intercept jackknives for --two-step.''' + n_blocks, n_annot = step1_jknife.delete_values.shape + n_annot -= 1 + if n_annot > 2: + raise ValueError( + 'twostep not yet implemented for partitioned LD Score.') + + step1_int, _ = self._intercept(step1_jknife) + est = np.hstack( + (step2_jknife.est, np.array(step1_int).reshape((1, 1)))) + delete_values = np.zeros((n_blocks, n_annot + 1)) + delete_values[:, n_annot] = step1_jknife.delete_values[:, n_annot] + delete_values[:, 0:n_annot] = step2_jknife.delete_values -\ + c * (step1_jknife.delete_values[:, n_annot] - + step1_int).reshape((n_blocks, n_annot)) # check this + pseudovalues = jk.Jackknife.delete_values_to_pseudovalues( + delete_values, est) + jknife_est, jknife_var, jknife_se, jknife_cov = jk.Jackknife.jknife( + pseudovalues) + jknife = namedtuple('jknife', + ['est', 'jknife_se', 'jknife_est', 'jknife_var', 'jknife_cov', 'delete_values']) + return jknife(est, jknife_se, jknife_est, jknife_var, jknife_cov, delete_values) + + +class Hsq(LD_Score_Regression): + + __null_intercept__ = 1 + + def __init__(self, y, x, w, N, M, n_blocks=200, intercept=None, slow=False, + twostep=None, old_weights=False, + chr_num=None, verbose=True, + approx_ridge=False, loco=False, ridge_lambda=None, standardize_ridge=True, skip_ridge_jackknife=True, keep_large=False, + num_chr_sets=22, evenodd_split=False, nn=False, nnls_exact=False, num_chr=22): + step1_ii = None + if twostep is not None: + step1_ii = y < twostep + + LD_Score_Regression.__init__(self, y, x, w, N, M, n_blocks, intercept=intercept, + slow=slow, step1_ii=step1_ii, old_weights=old_weights, + chr_num=chr_num, + verbose=verbose, + loco=loco, + ridge_lambda=ridge_lambda, + standardize_ridge=standardize_ridge, + approx_ridge=approx_ridge, + skip_ridge_jackknife=skip_ridge_jackknife, + num_chr_sets=num_chr_sets, + evenodd_split=evenodd_split, + nn=nn, + keep_large=keep_large, + nnls_exact=nnls_exact, + num_chr=num_chr + ) + self.mean_chisq, self.lambda_gc = self._summarize_chisq(y) + if not self.constrain_intercept: + self.ratio, self.ratio_se = self._ratio( + self.intercept, self.intercept_se, self.mean_chisq) + + def _update_func(self, x, ref_ld_tot, w_ld, N, M, Nbar, intercept=None, ii=None): + ''' + Update function for IRWLS + + x is the output of np.linalg.lstsq. + x[0] is the regression coefficients + x[0].shape is (# of dimensions, 1) + the last element of x[0] is the intercept. + + intercept is None --> free intercept + intercept is not None --> constrained intercept + ''' + hsq = M * x[0][0] / Nbar + if intercept is None: + intercept = max(x[0][1]) # divide by zero error if intercept < 0 + else: + if ref_ld_tot.shape[1] > 1: + raise ValueError( + 'Design matrix has intercept column for constrained intercept regression!') + + ld = ref_ld_tot[:, 0].reshape(w_ld.shape) # remove intercept + w = self.weights(ld, w_ld, N, M, hsq, intercept, ii) + return w + + def _summarize_chisq(self, chisq): + '''Compute mean chi^2 and lambda_GC.''' + mean_chisq = np.mean(chisq) + # median and matrix don't play nice + lambda_gc = np.median(np.asarray(chisq)) / 0.4549 + return mean_chisq, lambda_gc + + def _ratio(self, intercept, intercept_se, mean_chisq): + '''Compute ratio (intercept - 1) / (mean chi^2 -1 ).''' + if mean_chisq > 1: + ratio_se = intercept_se / (mean_chisq - 1) + ratio = (intercept - 1) / (mean_chisq - 1) + else: + ratio = 'NA' + ratio_se = 'NA' + + return ratio, ratio_se + + def _overlap_output(self, category_names, overlap_matrix, M_annot, M_tot, print_coefficients): + '''LD Score regression summary for overlapping categories.''' + overlap_matrix_prop = np.zeros([self.n_annot,self.n_annot]) + for i in range(self.n_annot): + overlap_matrix_prop[i, :] = overlap_matrix[i, :] / M_annot + + prop_hsq_overlap = np.dot( + overlap_matrix_prop, self.prop.T).reshape((1, self.n_annot)) + prop_hsq_overlap_var = np.diag( + np.dot(np.dot(overlap_matrix_prop, self.prop_cov), overlap_matrix_prop.T)) + prop_hsq_overlap_se = np.sqrt( + np.maximum(0, prop_hsq_overlap_var)).reshape((1, self.n_annot)) + one_d_convert = lambda x: np.array(x).reshape(np.prod(x.shape)) + prop_M_overlap = M_annot / M_tot + enrichment = prop_hsq_overlap / prop_M_overlap + enrichment_se = prop_hsq_overlap_se / prop_M_overlap + overlap_matrix_diff = np.zeros([self.n_annot,self.n_annot]) + for i in range(self.n_annot): + if not M_tot == M_annot[0,i]: + overlap_matrix_diff[i, :] = overlap_matrix[i,:]/M_annot[0,i] - \ + (M_annot - overlap_matrix[i,:]) / (M_tot-M_annot[0,i]) + + diff_est = np.dot(overlap_matrix_diff,self.coef) + diff_cov = np.dot(np.dot(overlap_matrix_diff,self.coef_cov),overlap_matrix_diff.T) + diff_se = np.sqrt(np.diag(diff_cov)) + diff_p = [np.nan if diff_se[i]==0 else 2*tdist.sf(abs(diff_est[i]/diff_se[i]),self.n_blocks) \ + for i in range(self.n_annot)] + + coef_z = [] + for i in range(self.n_annot): + if one_d_convert(self.coef)[i]==0 and one_d_convert(self.coef_se)[i]==0: + coef_z.append(0) + elif one_d_convert(self.coef_se)[i]==0: + coef_z.append('NA') + else: + coef_z.append(one_d_convert(self.coef)[i] / one_d_convert(self.coef_se)[i]) + + df = pd.DataFrame({ + 'Category': category_names, + 'Prop._SNPs': one_d_convert(prop_M_overlap), + 'Prop._h2': one_d_convert(prop_hsq_overlap), + 'Prop._h2_std_error': one_d_convert(prop_hsq_overlap_se), + 'Enrichment': one_d_convert(enrichment), + 'Enrichment_std_error': one_d_convert(enrichment_se), + 'Enrichment_p':diff_p, + 'Coefficient': one_d_convert(self.coef), + 'Coefficient_std_error': self.coef_se, + 'Coefficient_z-score': coef_z + }) + if print_coefficients: + df = df[['Category', 'Prop._SNPs', 'Prop._h2', 'Prop._h2_std_error', + 'Enrichment','Enrichment_std_error', 'Enrichment_p', + 'Coefficient', 'Coefficient_std_error','Coefficient_z-score']] + else: + df = df[['Category', 'Prop._SNPs', 'Prop._h2', 'Prop._h2_std_error', + 'Enrichment','Enrichment_std_error', 'Enrichment_p']] + return df + + + def summary(self, ref_ld_colnames=None, P=None, K=None, overlap=False): + '''Print summary of the LD Score Regression.''' + if P is not None and K is not None: + T = 'Liability' + c = h2_obs_to_liab(1, P, K) + else: + T = 'Observed' + c = 1 + + out = ['Total ' + T + ' scale h2: ' + + s(c * self.tot) + ' (' + s(c * self.tot_se) + ')'] + if self.n_annot > 1: + if ref_ld_colnames is None: + ref_ld_colnames = ['CAT_' + str(i) + for i in range(self.n_annot)] + + out.append('Categories: ' + ' '.join(ref_ld_colnames)) + + if not overlap: + out.append(T + ' scale h2: ' + s(c * self.cat)) + out.append(T + ' scale h2 SE: ' + s(c * self.cat_se)) + out.append('Proportion of SNPs: ' + s(self.M_prop)) + out.append('Proportion of h2g: ' + s(self.prop)) + out.append('Enrichment: ' + s(self.enrichment)) + out.append('Coefficients: ' + s(self.coef)) + out.append('Coefficient SE: ' + s(self.coef_se)) + + out.append('Lambda GC: ' + s(self.lambda_gc)) + out.append('Mean Chi^2: ' + s(self.mean_chisq)) + if self.constrain_intercept: + out.append( + 'Intercept: constrained to {C}'.format(C=s(self.intercept))) + else: + out.append( + 'Intercept: ' + s(self.intercept) + ' (' + s(self.intercept_se) + ')') + if self.mean_chisq > 1: + if self.ratio < 0: + out.append( + 'Ratio < 0 (usually indicates GC correction).') + else: + out.append( + 'Ratio: ' + s(self.ratio) + ' (' + s(self.ratio_se) + ')') + else: + out.append('Ratio: NA (mean chi^2 < 1)') + + return remove_brackets('\n'.join(out)) + + def _update_weights(self, ld, w_ld, N, M, hsq, intercept, ii=None): + if intercept is None: + intercept = self.__null_intercept__ + + return self.weights(ld, w_ld, N, M, hsq, intercept, ii) + + @classmethod + def weights(cls, ld, w_ld, N, M, hsq, intercept=None, ii=None): + ''' + Regression weights. + + Parameters + ---------- + ld : np.matrix with shape (n_snp, 1) + LD Scores (non-partitioned). + w_ld : np.matrix with shape (n_snp, 1) + LD Scores (non-partitioned) computed with sum r^2 taken over only those SNPs included + in the regression. + N : np.matrix of ints > 0 with shape (n_snp, 1) + Number of individuals sampled for each SNP. + M : float > 0 + Number of SNPs used for estimating LD Score (need not equal number of SNPs included in + the regression). + hsq : float in [0,1] + Heritability estimate. + + Returns + ------- + w : np.matrix with shape (n_snp, 1) + Regression weights. Approx equal to reciprocal of conditional variance function. + + ''' + M = float(M) + if intercept is None: + intercept = 1 + + hsq = max(hsq, 0.0) + hsq = min(hsq, 1.0) + ld = np.fmax(ld, 1.0) + w_ld = np.fmax(w_ld, 1.0) + c = hsq * N / M + het_w = 1.0 / (2 * np.square(intercept + np.multiply(c, ld))) + oc_w = 1.0 / w_ld + w = np.multiply(het_w, oc_w) + return w + + +class Gencov(LD_Score_Regression): + __null_intercept__ = 0 + + def __init__(self, z1, z2, x, w, N1, N2, M, hsq1, hsq2, intercept_hsq1, intercept_hsq2, + n_blocks=200, intercept_gencov=None, slow=False, twostep=None): + self.intercept_hsq1 = intercept_hsq1 + self.intercept_hsq2 = intercept_hsq2 + self.hsq1 = hsq1 + self.hsq2 = hsq2 + self.N1 = N1 + self.N2 = N2 + y = z1 * z2 + step1_ii = None + if twostep is not None: + step1_ii = np.logical_and(z1**2 < twostep, z2**2 < twostep) + + LD_Score_Regression.__init__(self, y, x, w, np.sqrt(N1 * N2), M, n_blocks, + intercept=intercept_gencov, slow=slow, step1_ii=step1_ii) + self.p, self.z = p_z_norm(self.tot, self.tot_se) + self.mean_z1z2 = np.mean(np.multiply(z1, z2)) + + def summary(self, ref_ld_colnames, P=None, K=None): + '''Print summary of the LD Score regression.''' + out = [] + if P is not None and K is not None and\ + all((i is not None for i in P)) and all((i is not None for i in K)): + T = 'Liability' + c = gencov_obs_to_liab(1, P[0], P[1], K[0], K[1]) + else: + T = 'Observed' + c = 1 + + out.append('Total ' + T + ' scale gencov: ' + + s(self.tot) + ' (' + s(self.tot_se) + ')') + if self.n_annot > 1: + out.append('Categories: ' + str(' '.join(ref_ld_colnames))) + out.append(T + ' scale gencov: ' + s(c * self.cat)) + out.append(T + ' scale gencov SE: ' + s(c * self.cat_se)) + out.append('Proportion of SNPs: ' + s(self.M_prop)) + out.append('Proportion of gencov: ' + s(self.prop)) + out.append('Enrichment: ' + s(self.enrichment)) + + out.append('Mean z1*z2: ' + s(self.mean_z1z2)) + if self.constrain_intercept: + out.append( + 'Intercept: constrained to {C}'.format(C=s(self.intercept))) + else: + out.append( + 'Intercept: ' + s(self.intercept) + ' (' + s(self.intercept_se) + ')') + + return remove_brackets('\n'.join(out)) + + def _update_func(self, x, ref_ld_tot, w_ld, N, M, Nbar, intercept=None, ii=None): + ''' + Update function for IRWLS + x is the output of np.linalg.lstsq. + x[0] is the regression coefficients + x[0].shape is (# of dimensions, 1) + the last element of x[0] is the intercept. + + ''' + rho_g = M * x[0][0] / Nbar + if intercept is None: # if the regression includes an intercept + intercept = x[0][1] + + # remove intercept if we have one + ld = ref_ld_tot[:, 0].reshape(w_ld.shape) + if ii is not None: + N1 = self.N1[ii].reshape((w_ld.shape)) + N2 = self.N2[ii].reshape((w_ld.shape)) + else: + N1 = self.N1 + N2 = self.N2 + + return self.weights(ld, w_ld, N1, N2, np.sum(M), self.hsq1, self.hsq2, rho_g, + intercept, self.intercept_hsq1, self.intercept_hsq2, ii) + + def _update_weights(self, ld, w_ld, sqrt_n1n2, M, rho_g, intercept, ii=None): + '''Weight function with the same signature for Hsq and Gencov.''' + w = self.weights(ld, w_ld, self.N1, self.N2, M, self.hsq1, self.hsq2, rho_g, + intercept, self.intercept_hsq1, self.intercept_hsq2) + return w + + @classmethod + def weights(cls, ld, w_ld, N1, N2, M, h1, h2, rho_g, intercept_gencov=None, + intercept_hsq1=None, intercept_hsq2=None, ii=None): + ''' + Regression weights. + + Parameters + ---------- + ld : np.matrix with shape (n_snp, 1) + LD Scores (non-partitioned) + w_ld : np.matrix with shape (n_snp, 1) + LD Scores (non-partitioned) computed with sum r^2 taken over only those SNPs included + in the regression. + M : float > 0 + Number of SNPs used for estimating LD Score (need not equal number of SNPs included in + the regression). + N1, N2 : np.matrix of ints > 0 with shape (n_snp, 1) + Number of individuals sampled for each SNP for each study. + h1, h2 : float in [0,1] + Heritability estimates for each study. + rhog : float in [0,1] + Genetic covariance estimate. + intercept : float + Genetic covariance intercept, on the z1*z2 scale (so should be Ns*rho/sqrt(N1*N2)). + + Returns + ------- + w : np.matrix with shape (n_snp, 1) + Regression weights. Approx equal to reciprocal of conditional variance function. + + ''' + M = float(M) + if intercept_gencov is None: + intercept_gencov = 0 + if intercept_hsq1 is None: + intercept_hsq1 = 1 + if intercept_hsq2 is None: + intercept_hsq2 = 1 + + h1, h2 = max(h1, 0.0), max(h2, 0.0) + h1, h2 = min(h1, 1.0), min(h2, 1.0) + rho_g = min(rho_g, 1.0) + rho_g = max(rho_g, -1.0) + ld = np.fmax(ld, 1.0) + w_ld = np.fmax(w_ld, 1.0) + a = np.multiply(N1, h1 * ld) / M + intercept_hsq1 + b = np.multiply(N2, h2 * ld) / M + intercept_hsq2 + sqrt_n1n2 = np.sqrt(np.multiply(N1, N2)) + c = np.multiply(sqrt_n1n2, rho_g * ld) / M + intercept_gencov + try: + het_w = 1.0 / (np.multiply(a, b) + np.square(c)) + except FloatingPointError: # bizarre error; should never happen + raise FloatingPointError('Why did you set hsq intercept <= 0?') + + oc_w = 1.0 / w_ld + w = np.multiply(het_w, oc_w) + return w + + +class RG(object): + + def __init__(self, z1, z2, x, w, N1, N2, M, intercept_hsq1=None, intercept_hsq2=None, + intercept_gencov=None, n_blocks=200, slow=False, twostep=None): + self.intercept_gencov = intercept_gencov + self._negative_hsq = None + n_snp, n_annot = x.shape + hsq1 = Hsq(np.square(z1), x, w, N1, M, n_blocks=n_blocks, intercept=intercept_hsq1, + slow=slow, twostep=twostep) + hsq2 = Hsq(np.square(z2), x, w, N2, M, n_blocks=n_blocks, intercept=intercept_hsq2, + slow=slow, twostep=twostep) + gencov = Gencov(z1, z2, x, w, N1, N2, M, hsq1.tot, hsq2.tot, hsq1.intercept, + hsq2.intercept, n_blocks, intercept_gencov=intercept_gencov, slow=slow, + twostep=twostep) + gencov.N1 = None # save memory + gencov.N2 = None + self.hsq1, self.hsq2, self.gencov = hsq1, hsq2, gencov + if (hsq1.tot <= 0 or hsq2.tot <= 0): + self._negative_hsq = True + self.rg_ratio = self.rg = self.rg_se = 'NA' + self.p = self.z = 'NA' + else: + rg_ratio = np.array( + gencov.tot / np.sqrt(hsq1.tot * hsq2.tot)).reshape((1, 1)) + denom_delete_values = np.sqrt( + np.multiply(hsq1.tot_delete_values, hsq2.tot_delete_values)) + rg = jk.RatioJackknife( + rg_ratio, gencov.tot_delete_values, denom_delete_values) + self.rg_jknife = float(rg.jknife_est) + self.rg_se = float(rg.jknife_se) + self.rg_ratio = float(rg_ratio) + self.p, self.z = p_z_norm(self.rg_ratio, self.rg_se) + + def summary(self, silly=False): + '''Print output of Gencor object.''' + out = [] + if self._negative_hsq: + out.append('Genetic Correlation: nan (nan) (h2 out of bounds) ') + out.append('Z-score: nan (nan) (h2 out of bounds)') + out.append('P: nan (nan) (h2 out of bounds)') + out.append('WARNING: One of the h2\'s was out of bounds.') + out.append( + 'This usually indicates a data-munging error ' + + 'or that h2 or N is low.') + elif (self.rg_ratio > 1.2 or self.rg_ratio < -1.2) and not silly: + out.append('Genetic Correlation: nan (nan) (rg out of bounds) ') + out.append('Z-score: nan (nan) (rg out of bounds)') + out.append('P: nan (nan) (rg out of bounds)') + out.append('WARNING: rg was out of bounds.') + if self.intercept_gencov is None: + out.append( + 'This often means that h2 is not significantly ' + + 'different from zero.') + else: + out.append( + 'This often means that you have constrained' + + ' the intercepts to the wrong values.') + else: + out.append( + 'Genetic Correlation: ' + s(self.rg_ratio) + + ' (' + s(self.rg_se) + ')') + out.append('Z-score: ' + s(self.z)) + out.append('P: ' + s(self.p)) + return remove_brackets('\n'.join(out)) diff --git a/SCRIPTS/polyfun/ldsc_polyfun/sumstats.py b/SCRIPTS/polyfun/ldsc_polyfun/sumstats.py new file mode 100644 index 0000000..49ec4a3 --- /dev/null +++ b/SCRIPTS/polyfun/ldsc_polyfun/sumstats.py @@ -0,0 +1,567 @@ +''' +(c) 2014 Brendan Bulik-Sullivan and Hilary Finucane + +This module deals with getting all the data needed for LD Score regression from files +into memory and checking that the input makes sense. There is no math here. LD Score +regression is implemented in the regressions module. + +''' + +import numpy as np +import pandas as pd +from scipy import stats +import itertools as it +from . import parse as ps +from . import regressions as reg +import sys +import traceback +import copy +import os + + +_N_CHR = 22 +# complementary bases +COMPLEMENT = {'A': 'T', 'T': 'A', 'C': 'G', 'G': 'C'} +# bases +BASES = list(COMPLEMENT.keys()) +# true iff strand ambiguous +STRAND_AMBIGUOUS = {''.join(x): x[0] == COMPLEMENT[x[1]] + for x in it.product(BASES, BASES) + if x[0] != x[1]} +# SNPS we want to keep (pairs of alleles) +VALID_SNPS = {x for x in [''.join(y) for y in it.product(BASES, BASES)] + if x[0] != x[1] and not STRAND_AMBIGUOUS[x]} +# T iff SNP 1 has the same alleles as SNP 2 (allowing for strand or ref allele flip). +MATCH_ALLELES = {x for x in [''.join(y) for y in it.product(VALID_SNPS, VALID_SNPS)] + # strand and ref match + if ((x[0] == x[2]) and (x[1] == x[3])) or + # ref match, strand flip + ((x[0] == COMPLEMENT[x[2]]) and (x[1] == COMPLEMENT[x[3]])) or + # ref flip, strand match + ((x[0] == x[3]) and (x[1] == x[2])) or + ((x[0] == COMPLEMENT[x[3]]) and (x[1] == COMPLEMENT[x[2]]))} # strand and ref flip +# T iff SNP 1 has the same alleles as SNP 2 w/ ref allele flip. +FLIP_ALLELES = {''.join(x): + ((x[0] == x[3]) and (x[1] == x[2])) or # strand match + # strand flip + ((x[0] == COMPLEMENT[x[3]]) and (x[1] == COMPLEMENT[x[2]])) + for x in MATCH_ALLELES} + + +def _splitp(fstr): + flist = fstr.split(',') + flist = [os.path.expanduser(os.path.expandvars(x)) for x in flist] + return flist + + +def _select_and_log(x, ii, log, msg): + '''Fiter down to rows that are True in ii. Log # of SNPs removed.''' + new_len = ii.sum() + if new_len == 0: + raise ValueError(msg.format(N=0)) + else: + x = x[ii] + log.log(msg.format(N=new_len)) + return x + + +def smart_merge(x, y): + '''Check if SNP columns are equal. If so, save time by using concat instead of merge.''' + assert len(set(x.columns).intersection(set(y.drop(columns=['SNP']).columns))) == 0 + if len(x) == len(y) and (x.index == y.index).all() and (x.SNP == y.SNP).all(): + #x = x.reset_index(drop=True) + #y = y.reset_index(drop=True).drop(columns=['SNP']) + #out = pd.concat([x, y], axis=1) + out = pd.concat([x, y.drop(columns=['SNP'])], axis=1) + else: + if x.index.name == 'snpid' and y.index.name == 'snpid': + out = pd.merge(x, y.drop(columns=['SNP']), how='inner', left_index=True, right_index=True) + else: + out = pd.merge(x, y, how='inner', on='SNP') + return out + + +def _read_ref_ld(args, log): + '''Read reference LD Scores.''' + ref_ld = _read_chr_split_files(args.ref_ld_chr, args.ref_ld, log, + 'reference panel LD Score', ps.ldscore_fromlist) + log.log( + 'Read reference panel LD Scores for {N} SNPs.'.format(N=len(ref_ld))) + return ref_ld + + +def _read_annot(args, log): + '''Read annot matrix.''' + if (args.anno is not None): annotations = args.anno.split(',') + else: annotations = None + try: + if args.ref_ld is not None: + overlap_matrix, M_tot = _read_chr_split_files(args.ref_ld_chr, args.ref_ld, log, + 'annot matrix', ps.annot, frqfile=args.frqfile, anno=annotations) + elif args.ref_ld_chr is not None: + overlap_matrix, M_tot = _read_chr_split_files(args.ref_ld_chr, args.ref_ld, log, + 'annot matrix', ps.annot, frqfile=args.frqfile_chr, anno=annotations) + except Exception: + log.log('Error parsing .annot file.') + raise + + return overlap_matrix, M_tot + + +def _read_M(args, log, n_annot): + '''Read M (--M, --M-file, etc).''' + if args.M: + try: + M_annot = [float(x) for x in _splitp(args.M)] + except ValueError as e: + raise ValueError('Could not cast --M to float: ' + str(e.args)) + else: + if args.ref_ld: + M_annot = ps.M_fromlist( + _splitp(args.ref_ld), common=(not args.not_M_5_50)) + elif args.ref_ld_chr: + M_annot = ps.M_fromlist( + _splitp(args.ref_ld_chr), _N_CHR, common=(not args.not_M_5_50)) + + try: + M_annot = np.array(M_annot).reshape((1, n_annot)) + except ValueError as e: + raise ValueError( + '# terms in --M must match # of LD Scores in --ref-ld.\n' + str(e.args)) + + return M_annot + + +def _read_w_ld(args, log): + '''Read regression SNP LD.''' + if (args.w_ld and ',' in args.w_ld) or (args.w_ld_chr and ',' in args.w_ld_chr): + raise ValueError( + '--w-ld must point to a single fileset (no commas allowed).') + w_ld = _read_chr_split_files(args.w_ld_chr, args.w_ld, log, + 'regression weight LD Score', ps.ldscore_fromlist) + w_ld.drop(['CHR'], axis=1, inplace=True) + if len(w_ld.columns) != 2: + raise ValueError('--w-ld may only have one LD Score column.') + w_ld.columns = ['SNP', 'LD_weights'] # prevent colname conflicts w/ ref ld + log.log( + 'Read regression weight LD Scores for {N} SNPs.'.format(N=len(w_ld))) + return w_ld + + +def _read_chr_split_files(chr_arg, not_chr_arg, log, noun, parsefunc, **kwargs): + '''Read files split across chromosomes (annot, ref_ld, w_ld).''' + try: + if not_chr_arg: + log.log('Reading {N} from {F} ...'.format(F=not_chr_arg, N=noun)) + out = parsefunc(_splitp(not_chr_arg), **kwargs) + elif chr_arg: + f = ps.sub_chr(chr_arg, '[1-%d]'%(_N_CHR)) + log.log('Reading {N} from {F} ...'.format(F=f, N=noun)) + out = parsefunc(_splitp(chr_arg), _N_CHR, **kwargs) + except ValueError as e: + log.log('Error parsing {N}.'.format(N=noun)) + raise e + + return out + + +def _read_sumstats(args, log, fh, alleles=True, dropna=False): + '''Parse summary statistics.''' + log.log('Reading summary statistics from {S} ...'.format(S=fh)) + sumstats = ps.sumstats(fh, alleles=alleles, dropna=dropna) + log_msg = 'Read summary statistics for {N} SNPs.' + log.log(log_msg.format(N=len(sumstats))) + if np.any(sumstats.index.duplicated()): + m = len(sumstats) + sumstats = sumstats.loc[~sumstats.index.duplicated()] + log.log('Dropped {M} duplicated SNPs.'.format(M=m - len(sumstats))) + + return sumstats + + +def _check_ld_condnum(args, log, ref_ld): + '''Check condition number of LD Score matrix.''' + if len(ref_ld.shape) >= 2: + cond_num = int(np.linalg.cond(ref_ld)) + if cond_num > 100000: + if args.invert_anyway: + warn = "WARNING: LD Score matrix condition number is {C}. " + warn += "Inverting anyway because the --invert-anyway flag is set." + log.log(warn.format(C=cond_num)) + else: + warn = "WARNING: LD Score matrix condition number is {C}. " + warn += "Remove collinear LD Scores. " + raise ValueError(warn.format(C=cond_num)) + + +def _check_variance(log, M_annot, ref_ld): + '''Remove zero-variance LD Scores.''' + ###ii = ref_ld.iloc[:, 2:].var(axis=0) == 0 # NB there is a SNP and CHR column here + ii = np.array([(ref_ld[c].var() == 0) for c in ref_ld.columns[2:]]) #This command uses way way less memory + if ii.all(): + raise ValueError('All LD Scores have zero variance.') + elif ii.any(): + log.log('Removing partitioned LD Scores with zero variance: %s'%(','.join(ref_ld.columns[2:][ii]))) + ii_snp = np.array([True, True] + list(~ii)) + ii_m = np.array(~ii) + ref_ld = ref_ld.loc[:, ii_snp] + M_annot = M_annot[:, ii_m] + + return M_annot, ref_ld, ii + + +def _warn_length(log, sumstats): + if len(sumstats) < 200000: + log.log( + 'WARNING: number of SNPs less than 200k; this is almost always bad.') + + +def _print_cov(ldscore_reg, ofh, log): + '''Prints covariance matrix of slopes.''' + log.log( + 'Printing covariance matrix of the estimates to {F}.'.format(F=ofh)) + np.savetxt(ofh, ldscore_reg.coef_cov) + + +def _print_delete_values(ldscore_reg, ofh, log): + '''Prints block jackknife delete-k values''' + log.log('Printing block jackknife delete values to {F}.'.format(F=ofh)) + np.savetxt(ofh, ldscore_reg.tot_delete_values) + +def _print_part_delete_values(ldscore_reg, ofh, log): + '''Prints partitioned block jackknife delete-k values''' + log.log('Printing partitioned block jackknife delete values to {F}.'.format(F=ofh)) + np.savetxt(ofh, ldscore_reg.part_delete_values) + + +def _merge_and_log(ld, sumstats, noun, log): + '''Wrap smart merge with log messages about # of SNPs.''' + sumstats = smart_merge(ld, sumstats) + msg = 'After merging with {F}, {N} SNPs remain.' + if len(sumstats) == 0: + msg += ' Please make sure that your annotation files include the SNPs in your sumstats files (please see the PolyFun wiki for details on downloading functional annotations)' + raise ValueError(msg.format(N=len(sumstats), F=noun)) + else: + log.log(msg.format(N=len(sumstats), F=noun)) + + return sumstats + + +def _read_ld_sumstats(args, log, fh, alleles=True, dropna=True): + _N_CHR = args.num_chr + sumstats = _read_sumstats(args, log, fh, alleles=alleles, dropna=dropna) + ref_ld = _read_ref_ld(args, log) + n_annot = len(ref_ld.columns) - 2 #Changed to -2 because we also have chromosome column now + M_annot = _read_M(args, log, n_annot) + + #keep only requested annotations if --anno was specified + if args.anno is not None: + cols_to_keep = np.zeros(len(ref_ld.columns), dtype=bool) + annotations = args.anno.split(',') + is_found1 = np.isin(annotations, ref_ld.columns.str[:-2]) + is_found2 = np.isin(annotations, ref_ld.columns.str[:-4]) + is_found = is_found1 | is_found2 + if np.any(~is_found): + raise ValueError('Not all annotations specified with --anno are found in the LD scores file') + cols_to_keep = (ref_ld.columns.str[:-2].isin(annotations)) | (ref_ld.columns.str[:-4].isin(annotations)) | (ref_ld.columns.isin(['CHR', 'SNP'])) + assert np.sum(cols_to_keep) == len(annotations)+2 + cols_nochrsnp = ref_ld.drop(columns=['CHR', 'SNP']).columns + M_cols_to_keep = (cols_nochrsnp.str[:-2].isin(annotations)) | (cols_nochrsnp.str[:-4].isin(annotations)) + assert np.sum(M_cols_to_keep) == len(annotations) + ref_ld = ref_ld.loc[:, cols_to_keep] + M_annot = M_annot[:, M_cols_to_keep] + log.log('Keeping only annotations specified with --anno') + + M_annot, ref_ld, novar_cols = _check_variance(log, M_annot, ref_ld) + w_ld = _read_w_ld(args, log) + sumstats = _merge_and_log(ref_ld, sumstats, 'reference panel LD', log) + sumstats = _merge_and_log(sumstats, w_ld, 'regression SNP LD', log) + w_ld_cname = sumstats.columns[-1] + ref_ld_cnames = ref_ld.drop(columns=['CHR', 'SNP']).columns + return M_annot, w_ld_cname, ref_ld_cnames, sumstats, novar_cols + +def estimate_h2(args, log): + '''Estimate h2 and partitioned h2.''' + args = copy.deepcopy(args) + _N_CHR = args.num_chr + if args.samp_prev is not None and args.pop_prev is not None: + args.samp_prev, args.pop_prev = list(map( + float, [args.samp_prev, args.pop_prev])) + if args.intercept_h2 is not None: + args.intercept_h2 = float(args.intercept_h2) + if args.no_intercept: + args.intercept_h2 = 1 + M_annot, w_ld_cname, ref_ld_cnames, sumstats, novar_cols = _read_ld_sumstats( + args, log, args.h2) + ref_ld = np.array(sumstats[ref_ld_cnames], dtype=np.float32) + if not args.skip_cond_check: + _check_ld_condnum(args, log, ref_ld_cnames) + _warn_length(log, sumstats) + n_snp = len(sumstats) + n_blocks = min(n_snp, args.n_blocks) + n_annot = len(ref_ld_cnames) + chisq_max = args.chisq_max + old_weights = False + if n_annot == 1: + if args.two_step is None and args.intercept_h2 is None: + args.two_step = 30 + else: + old_weights = True + if args.chisq_max is None: + chisq_max = max(0.001*sumstats.N.max(), args.max_chi2) + + s = lambda x: np.array(x).reshape((n_snp, 1)) + chisq = s(sumstats.Z**2).astype(np.float32) + if chisq_max is not None and not args.keep_large: + ii = np.ravel(chisq < chisq_max) + sumstats = sumstats.loc[ii, :] + log.log('Removed {M} SNPs with chi^2 > {C} ({N} SNPs remain)'.format( + C=chisq_max, N=np.sum(ii), M=n_snp-np.sum(ii))) + n_snp = np.sum(ii) # lambdas are late-binding, so this works + ref_ld = np.array(sumstats[ref_ld_cnames], dtype=np.float32) + chisq = chisq[ii].reshape((n_snp, 1)) + + if args.two_step is not None: + log.log('Using two-step estimator with cutoff at {M}.'.format(M=args.two_step)) + + hsqhat = reg.Hsq(chisq, ref_ld, s(sumstats[w_ld_cname]), s(sumstats.N), + M_annot, n_blocks=n_blocks, intercept=args.intercept_h2, + twostep=args.two_step, old_weights=old_weights, + chr_num=sumstats['CHR'], + loco=args.loco, ridge_lambda=args.ridge_lambda, + standardize_ridge=not args.no_standardize_ridge, + approx_ridge=not args.reestimate_lambdas, + skip_ridge_jackknife=not args.ridge_jackknife, + num_chr_sets = args.num_chr_sets, + evenodd_split=args.evenodd_split, + nn=args.nn, + keep_large=args.keep_large, + nnls_exact=args.nnls_exact, + num_chr=args.num_chr + ) + + if args.print_cov: + _print_cov(hsqhat, args.out + '.cov', log) + if args.print_delete_vals: + _print_delete_values(hsqhat, args.out + '.delete', log) + _print_part_delete_values(hsqhat, args.out + '.part_delete', log) + + + #save ridge-regression lambdas if possible + if args.loco and args.ridge_jackknife and args.reestimate_lambdas: + np.savetxt(args.out+'.out_of_chrom_r2.txt', [hsqhat.jknife_ridge.r2_best_lambda]) + df = pd.Series(hsqhat.jknife_ridge.best_r2_jk_noblock) + df.to_csv(args.out+'.out_of_chrom_r2_jk.txt', index=False, header=False) + + log.log(hsqhat.summary(ref_ld_cnames, P=args.samp_prev, K=args.pop_prev, overlap = args.overlap_annot)) + if args.overlap_annot: + overlap_matrix, M_tot = _read_annot(args, log) + + # overlap_matrix = overlap_matrix[np.array(~novar_cols), np.array(~novar_cols)]#np.logical_not + df_results = hsqhat._overlap_output(ref_ld_cnames, overlap_matrix, M_annot, M_tot, args.print_coefficients) + df_results.to_csv(args.out+'.results', sep="\t", index=False, na_rep='NA', float_format='%0.4e') + log.log('Results printed to '+args.out+'.results') + + return hsqhat + + +def estimate_rg(args, log): + '''Estimate rg between trait 1 and a list of other traits.''' + args = copy.deepcopy(args) + rg_paths, rg_files = _parse_rg(args.rg) + n_pheno = len(rg_paths) + f = lambda x: _split_or_none(x, n_pheno) + args.intercept_h2, args.intercept_gencov, args.samp_prev, args.pop_prev = list(map(f, + (args.intercept_h2, args.intercept_gencov, args.samp_prev, args.pop_prev))) + list(map(lambda x: _check_arg_len(x, n_pheno), ((args.intercept_h2, '--intercept-h2'), + (args.intercept_gencov, '--intercept-gencov'), + (args.samp_prev, '--samp-prev'), + (args.pop_prev, '--pop-prev')))) + if args.no_intercept: + args.intercept_h2 = [1 for _ in range(n_pheno)] + args.intercept_gencov = [0 for _ in range(n_pheno)] + p1 = rg_paths[0] + out_prefix = args.out + rg_files[0] + M_annot, w_ld_cname, ref_ld_cnames, sumstats, _ = _read_ld_sumstats(args, log, p1, + alleles=True, dropna=True) + RG = [] + n_annot = M_annot.shape[1] + if n_annot == 1 and args.two_step is None and args.intercept_h2 is None: + args.two_step = 30 + if args.two_step is not None: + log.log('Using two-step estimator with cutoff at {M}.'.format(M=args.two_step)) + + for i, p2 in enumerate(rg_paths[1:n_pheno]): + log.log( + 'Computing rg for phenotype {I}/{N}'.format(I=i + 2, N=len(rg_paths))) + try: + loop = _read_other_sumstats(args, log, p2, sumstats, ref_ld_cnames) + rghat = _rg(loop, args, log, M_annot, ref_ld_cnames, w_ld_cname, i) + RG.append(rghat) + _print_gencor(args, log, rghat, ref_ld_cnames, i, rg_paths, i == 0) + out_prefix_loop = out_prefix + '_' + rg_files[i + 1] + if args.print_cov: + _print_rg_cov(rghat, out_prefix_loop, log) + if args.print_delete_vals: + _print_rg_delete_values(rghat, out_prefix_loop, log) + + except Exception: # keep going if phenotype 50/100 causes an error + msg = 'ERROR computing rg for phenotype {I}/{N}, from file {F}.' + log.log(msg.format(I=i + 2, N=len(rg_paths), F=rg_paths[i + 1])) + ex_type, ex, tb = sys.exc_info() + log.log(traceback.format_exc(ex) + '\n') + if len(RG) <= i: # if exception raised before appending to RG + RG.append(None) + + log.log('\nSummary of Genetic Correlation Results\n' + + _get_rg_table(rg_paths, RG, args)) + return RG + + +def _read_other_sumstats(args, log, p2, sumstats, ref_ld_cnames): + loop = _read_sumstats(args, log, p2, alleles=True, dropna=False) + loop = _merge_sumstats_sumstats(args, sumstats, loop, log) + loop = loop.dropna(how='any') + alleles = loop.A1 + loop.A2 + loop.A1x + loop.A2x + if not args.no_check_alleles: + loop = _select_and_log(loop, _filter_alleles(alleles), log, + '{N} SNPs with valid alleles.') + + loop['Z2'] = _align_alleles(loop.Z2, alleles) + loop = loop.drop(['A1', 'A1x', 'A2', 'A2x'], axis=1) + _check_ld_condnum(args, log, loop[ref_ld_cnames]) + _warn_length(log, loop) + return loop + + +def _get_rg_table(rg_paths, RG, args): + '''Print a table of genetic correlations.''' + t = lambda attr: lambda obj: getattr(obj, attr, 'NA') + x = pd.DataFrame() + x['p1'] = [rg_paths[0] for i in range(1, len(rg_paths))] + x['p2'] = rg_paths[1:len(rg_paths)] + x['rg'] = list(map(t('rg_ratio'), RG)) + x['se'] = list(map(t('rg_se'), RG)) + x['z'] = list(map(t('z'), RG)) + x['p'] = list(map(t('p'), RG)) + if args.samp_prev is not None and args.pop_prev is not None and\ + all((i is not None for i in args.samp_prev)) and all((i is not None for it in args.pop_prev)): + c = reg.h2_obs_to_liab(1, args.samp_prev[1], args.pop_prev[1]) + x['h2_liab'] = [c * x for x in list(map(t('tot'), list(map(t('hsq2'), RG))))] + x['h2_liab_se'] = [c * x for x in list(map(t('tot_se'), list(map(t('hsq2'), RG))))] + else: + x['h2_obs'] = list(map(t('tot'), list(map(t('hsq2'), RG)))) + x['h2_obs_se'] = list(map(t('tot_se'), list(map(t('hsq2'), RG)))) + + x['h2_int'] = list(map(t('intercept'), list(map(t('hsq2'), RG)))) + x['h2_int_se'] = list(map(t('intercept_se'), list(map(t('hsq2'), RG)))) + x['gcov_int'] = list(map(t('intercept'), list(map(t('gencov'), RG)))) + x['gcov_int_se'] = list(map(t('intercept_se'), list(map(t('gencov'), RG)))) + return x.to_string(header=True, index=False) + '\n' + + +def _print_gencor(args, log, rghat, ref_ld_cnames, i, rg_paths, print_hsq1): + l = lambda x: x + ''.join(['-' for i in range(len(x.replace('\n', '')))]) + P = [args.samp_prev[0], args.samp_prev[i + 1]] + K = [args.pop_prev[0], args.pop_prev[i + 1]] + if args.samp_prev is None and args.pop_prev is None: + args.samp_prev = [None, None] + args.pop_prev = [None, None] + if print_hsq1: + log.log(l('\nHeritability of phenotype 1\n')) + log.log(rghat.hsq1.summary(ref_ld_cnames, P=P[0], K=K[0])) + + log.log( + l('\nHeritability of phenotype {I}/{N}\n'.format(I=i + 2, N=len(rg_paths)))) + log.log(rghat.hsq2.summary(ref_ld_cnames, P=P[1], K=K[1])) + log.log(l('\nGenetic Covariance\n')) + log.log(rghat.gencov.summary(ref_ld_cnames, P=P, K=K)) + log.log(l('\nGenetic Correlation\n')) + log.log(rghat.summary() + '\n') + + +def _merge_sumstats_sumstats(args, sumstats1, sumstats2, log): + '''Merge two sets of summary statistics.''' + sumstats1.rename(columns={'N': 'N1', 'Z': 'Z1'}, inplace=True) + sumstats2.rename( + columns={'A1': 'A1x', 'A2': 'A2x', 'N': 'N2', 'Z': 'Z2'}, inplace=True) + x = _merge_and_log(sumstats1, sumstats2, 'summary statistics', log) + return x + + +def _filter_alleles(alleles): + '''Remove bad variants (mismatched alleles, non-SNPs, strand ambiguous).''' + ii = alleles.apply(lambda y: y in MATCH_ALLELES) + return ii + + +def _align_alleles(z, alleles): + '''Align Z1 and Z2 to same choice of ref allele (allowing for strand flip).''' + try: + z *= (-1) ** alleles.apply(lambda y: FLIP_ALLELES[y]) + except KeyError as e: + msg = 'Incompatible alleles in .sumstats files: %s. ' % e.args + msg += 'Did you forget to use --merge-alleles with munge_sumstats.py?' + raise KeyError(msg) + return z + + +def _rg(sumstats, args, log, M_annot, ref_ld_cnames, w_ld_cname, i): + '''Run the regressions.''' + n_snp = len(sumstats) + s = lambda x: np.array(x).reshape((n_snp, 1)) + if args.chisq_max is not None: + ii = sumstats.Z1**2*sumstats.Z2**2 < args.chisq_max**2 + n_snp = np.sum(ii) # lambdas are late binding, so this works + sumstats = sumstats[ii] + n_blocks = min(args.n_blocks, n_snp) + ref_ld = sumstats.as_matrix(columns=ref_ld_cnames) + intercepts = [args.intercept_h2[0], args.intercept_h2[ + i + 1], args.intercept_gencov[i + 1]] + rghat = reg.RG(s(sumstats.Z1), s(sumstats.Z2), + ref_ld, s(sumstats[w_ld_cname]), s( + sumstats.N1), s(sumstats.N2), M_annot, + intercept_hsq1=intercepts[0], intercept_hsq2=intercepts[1], + intercept_gencov=intercepts[2], n_blocks=n_blocks, twostep=args.two_step) + + return rghat + + +def _parse_rg(rg): + '''Parse args.rg.''' + rg_paths = _splitp(rg) + rg_files = [x.split('/')[-1] for x in rg_paths] + if len(rg_paths) < 2: + raise ValueError( + 'Must specify at least two phenotypes for rg estimation.') + + return rg_paths, rg_files + + +def _print_rg_delete_values(rg, fh, log): + '''Print block jackknife delete values.''' + _print_delete_values(rg.hsq1, fh + '.hsq1.delete', log) + _print_delete_values(rg.hsq2, fh + '.hsq2.delete', log) + _print_delete_values(rg.gencov, fh + '.gencov.delete', log) + + +def _print_rg_cov(rghat, fh, log): + '''Print covariance matrix of estimates.''' + _print_cov(rghat.hsq1, fh + '.hsq1.cov', log) + _print_cov(rghat.hsq2, fh + '.hsq2.cov', log) + _print_cov(rghat.gencov, fh + '.gencov.cov', log) + + +def _split_or_none(x, n): + if x is not None: + y = list(map(float, x.replace('N', '-').split(','))) + else: + y = [None for _ in range(n)] + return y + + +def _check_arg_len(x, n): + x, m = x + if len(x) != n: + raise ValueError( + '{M} must have the same number of arguments as --rg/--h2.'.format(M=m)) diff --git a/SCRIPTS/polyfun/ldstore/__init__.py b/SCRIPTS/polyfun/ldstore/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/SCRIPTS/polyfun/ldstore/bcor.py b/SCRIPTS/polyfun/ldstore/bcor.py new file mode 100644 index 0000000..ea4978e --- /dev/null +++ b/SCRIPTS/polyfun/ldstore/bcor.py @@ -0,0 +1,136 @@ +#(C) Christian Benner 2020. + + +from .parse import * + +import numpy as np +import pandas as pd +import math +import sys + +class bcor: + + ##-----------------------------------## + ## Initialize BCOR instance ## + ##-----------------------------------## + def __init__( self, fname ): + self.__fname = fname + try: + self.__fh = open( self.__fname, 'rb' ) + self.__readHeader() + self.__readMeta () + except IOError: + raise + + ##-----------------------------------## + ## Getters ## + ##-----------------------------------## + def getFname ( self ): return self.__fname + def getFsize ( self ): return self.__fsize + def getMeta ( self ): return self.__meta + def getNumOfSNPs ( self ): return self.__nSNPs + def getNumOfSamples( self ): return self.__nSamples + + ##-----------------------------------## + ## Get linear index for strictly ## + ## lower triangular matrix ## + ##-----------------------------------## + def __getIndex( self, snp_x, snp_y ): + if snp_x > snp_y: + return 0.5 * self.__nSNPs * ( self.__nSNPs - 1.0 ) - 0.5 * ( self.__nSNPs - snp_y ) * ( ( self.__nSNPs - snp_y ) - 1.0 ) + snp_x - snp_y - 1.0 + else: + return 0.5 * self.__nSNPs * ( self.__nSNPs - 1.0 ) - 0.5 * ( self.__nSNPs - snp_x ) * ( ( self.__nSNPs - snp_x ) - 1.0 ) + snp_y - snp_x - 1.0 + + ##-----------------------------------## + ## Read correlations for a pair ## + ## of SNPs ## + ##-----------------------------------## + def __readCorrPair( self, snp_x, snp_y, seek ): + if self.__compression == 0: + if seek: + self.__fh.seek( self.__corr_block_offset + self.__getIndex( snp_x, snp_y ) * 2 ) + return convertIntToFloat( np.fromfile( self.__fh, np.uint16, 1 )[ 0 ], 2 ) - 1.0 + elif self.__compression == 1: + if seek: + self.__fh.seek( self.__corr_block_offset + self.__getIndex( snp_x, snp_y ) * 4 ) + return convertIntToFloat( np.fromfile( self.__fh, np.uint32, 1 )[ 0 ], 4 ) - 1.0 + elif self.__compression == 2: + if seek: + self.__fh.seek( self.__corr_block_offset + self.__getIndex( snp_x, snp_y ) * 8 ) + return convertIntToFloat( np.fromfile( self.__fh, np.uint64, 1 )[ 0 ], 8 ) - 1.0 + elif self.__compression == 3: + if seek: + self.__fh.seek( self.__corr_block_offset + self.__getIndex( snp_x, snp_y ) * 1 ) + return convertIntToFloat( np.fromfile( self.__fh, np.uint8, 1 )[ 0 ], 1 ) - 1.0 + + ##-----------------------------------## + ## Read correlations in a BCOR file ## + ##-----------------------------------## + def readCorr( self, snps ): + if type( snps ) is not list: + raise ValueError('Need to input "snps" as a list!') + + if len( snps ) == 0: + self.__fh.seek( self.__corr_block_offset ) + corr = np.identity( self.__nSNPs, dtype=np.float32) + for snp_x in range( self.__nSNPs - 1 ): + for snp_y in range( snp_x + 1, self.__nSNPs ): + corr[ snp_y, snp_x ] = self.__readCorrPair( snp_x, snp_y, False ) + return np.tril( corr ) + np.triu( corr.T, 1 ) + else: + areSNPsIncluded( snps, self.__nSNPs, self.__fname ) + + corr = np.zeros( [ self.__nSNPs, len( snps ) ], dtype=np.float32) + for snp_x, index in zip( snps, list(range( len( snps ))) ): + for snp_y in range( self.__nSNPs ): + if snp_x == snp_y: + corr[ snp_y, index ] = 1.0 + else: + corr[ snp_y, index ] = self.__readCorrPair( snp_x, snp_y, True ) + return corr + + ##-----------------------------------## + ## Read header block ## + ##-----------------------------------## + def __readHeader( self ): + magic = self.__fh.read( 7 ) + if magic.decode('utf-8') != 'bcor1.1': + raise IOError('File "' + self.__fname + '" is not from LDStore!') + + self.__fsize = np.fromfile ( self.__fh, np.uint64, 1 )[ 0 ] + self.__nSamples = np.fromfile ( self.__fh, np.uint32, 1 )[ 0 ] + self.__nSNPs = np.fromfile ( self.__fh, np.uint32, 1 )[ 0 ] + self.__compression = np.fromfile ( self.__fh, np.uint8, 1 )[ 0 ] + self.__corr_block_offset = np.fromfile ( self.__fh, np.uint64, 1 )[ 0 ] + + ##-----------------------------------## + ## Read meta information block ## + ##-----------------------------------## + def __readMeta( self ): + rsid = [] + position = np.zeros( self.__nSNPs ) + chromosome = [] + allele1 = [] + allele2 = [] + + for snp in range( self.__nSNPs ): + L_buffer = np.fromfile( self.__fh, np.uint32, 1 )[ 0 ] + index = np.fromfile( self.__fh, np.uint32, 1 )[ 0 ] + L_rsid = np.fromfile( self.__fh, np.uint16, 1 )[ 0 ] + rsid.append( self.__fh.read( L_rsid ).decode('utf-8') ) + position[ snp ] = np.fromfile( self.__fh, np.uint32, 1 )[ 0 ] + L_chromosome = np.fromfile( self.__fh, np.uint16, 1 )[ 0 ] + chromosome.append( self.__fh.read( L_chromosome ).decode('utf-8') ) + L_allele1 = np.fromfile( self.__fh, np.uint32, 1 )[ 0 ] + allele1.append( self.__fh.read( L_allele1 ).decode('utf-8') ) + L_allele2 = np.fromfile( self.__fh, np.uint32, 1 )[ 0 ] + allele2.append( self.__fh.read( L_allele2 ).decode('utf-8') ) + assert( L_buffer == ( 20 + L_rsid + L_chromosome + L_allele1 + L_allele2 ) ) + + self.__meta = pd.concat( [ + pd.DataFrame( rsid, columns = [ 'rsid' ] ), + pd.DataFrame( position, columns = [ 'position' ] ), + pd.DataFrame( chromosome, columns = [ 'chromosome' ] ), + pd.DataFrame( allele1, columns = [ 'allele1' ] ), + pd.DataFrame( allele2, columns = [ 'allele2' ] ) + ], axis = 1 ) diff --git a/SCRIPTS/polyfun/ldstore/parse.py b/SCRIPTS/polyfun/ldstore/parse.py new file mode 100644 index 0000000..e123090 --- /dev/null +++ b/SCRIPTS/polyfun/ldstore/parse.py @@ -0,0 +1,42 @@ +#(C) Christian Benner 2020. + +import numpy as np + +def areSNPsIncluded( snps, n_snps, fname ): + for snp in snps: + if snp >= n_snps: + print('Cannot read dosages for ' + str( snp ) + 'th SNP. File "' + fname + '" contains only ' + str( n_snps ) + ' SNPs!') + return + +def convertIntToFloat( x, n_bytes ): + if type( x ) is np.ndarray: + return convertIntToFloat_array( x, n_bytes ) + else: + return convertIntToFloat_scalar( x, n_bytes ) + +def convertIntToFloat_array( x, n_bytes ): + int_na = getIntNA( n_bytes ) + y = np.zeros( len( x ) ) + y[ x == int_na ] = np.nan + y[ x != int_na ] = np.ldexp( x[ x != int_na ], -1 * ( 8 * n_bytes - 2 ) ) + + return y + +def convertIntToFloat_scalar( x, n_bytes ): + if x == getIntNA( n_bytes ): + return np.nan + else: + return np.ldexp( x, -1 * ( 8 * n_bytes - 2 ) ) + +def getIntNA( n_bytes ): + if n_bytes == 2: + return 53248 + elif n_bytes == 4: + return 3489660928 + elif n_bytes == 8: + return 14987979559889010688 + elif n_bytes == 1: + return 208 + else: + print('Only 1, 2, 4 and 8 bytes are supported!') + return diff --git a/SCRIPTS/polyfun/munge_polyfun_sumstats.py b/SCRIPTS/polyfun/munge_polyfun_sumstats.py new file mode 100644 index 0000000..f779912 --- /dev/null +++ b/SCRIPTS/polyfun/munge_polyfun_sumstats.py @@ -0,0 +1,310 @@ +import pandas as pd +import numpy as np +import os +import time +import scipy.stats as stats +import logging +from pandas.api.types import is_integer_dtype +from polyfun_utils import check_package_versions, configure_logger + + +def compute_Neff(df_sumstats, n, chi2_cutoff=30): + df_sumstats_chi2_large = df_sumstats.query('CHISQ_BOLT_LMM > %s'%(chi2_cutoff)) + if df_sumstats_chi2_large.shape[0]==0: + return n + Neff = int(np.median(df_sumstats_chi2_large['CHISQ_BOLT_LMM'] / df_sumstats_chi2_large['CHISQ_LINREG']) * n) + return Neff + + +def find_df_column(df, strings_to_find, allow_missing=False): + + if isinstance(strings_to_find, str): + strings_to_find = [strings_to_find] + + is_relevant_col = np.zeros(df.shape[1], dtype=bool) + for str_to_find in strings_to_find: + is_relevant_col = is_relevant_col | (df.columns.str.upper() == str_to_find.upper()) + if np.sum(is_relevant_col)==0: + if allow_missing: + return '' + else: + raise ValueError('No matching column found among: %s'%str(strings_to_find)) + elif np.sum(is_relevant_col)>1: + raise ValueError('Too many matching columns found among: %s'%str(strings_to_find)) + else: + return df.columns[is_relevant_col][0] + + +def rename_df_columns(df_sumstats): + chr_column = find_df_column(df_sumstats, ['CHR', 'CHROMOSOME', 'CHROM']) + bp_column = find_df_column(df_sumstats, ['BP', 'POS', 'POSITION', 'COORDINATE', 'BASEPAIR']) + snp_column = find_df_column(df_sumstats, ['SNP', 'RSID', 'RS', 'NAME', 'MarkerName']) + a1freq_col = find_df_column(df_sumstats, ['A1FREQ', 'freq', 'MAF', 'FRQ'], allow_missing=True) + info_col = find_df_column(df_sumstats, 'INFO', allow_missing=True) + beta_col = find_df_column(df_sumstats, ['BETA', 'EFF', 'EFFECT', 'EFFECT_SIZE', 'OR'], allow_missing=True) + se_col = find_df_column(df_sumstats, ['SE'], allow_missing=True) + pvalue_col = find_df_column(df_sumstats, ['P_BOLT_LMM', 'P', 'PVALUE', 'P-VALUE', 'P_value', 'PVAL'], allow_missing=True) + z_col = find_df_column(df_sumstats, ['Z', 'ZSCORE', 'Z_SCORE'], allow_missing=True) + n_col = find_df_column(df_sumstats, ['N', 'sample_size'], allow_missing=True) + ncase_col = find_df_column(df_sumstats, ['N_cases', 'Ncase', 'Nca','Total_NCase'], allow_missing=True) + ncontrol_col = find_df_column(df_sumstats, ['N_controls', 'Ncontrol','Nco','Total_NControl'], allow_missing=True) + try: + allele1_col = find_df_column(df_sumstats, ['ALLELE1', 'A1', 'a1', 'a_1']) + allele0_col = find_df_column(df_sumstats, ['ALLELE0', 'A0', 'a0', 'a_0']) + except ValueError: + allele1_col = find_df_column(df_sumstats, ['ALLELE1', 'A1', 'a1', 'a_1']) + allele0_col = find_df_column(df_sumstats, ['ALLELE2', 'A2', 'a2', 'a_2']) + + # make alleles uppercase + df_sumstats[allele1_col] = df_sumstats[allele1_col].str.upper() + df_sumstats[allele0_col] = df_sumstats[allele0_col].str.upper() + + # rename columns + return df_sumstats.rename(columns={snp_column:'SNP', allele1_col:'A1', + allele0_col:'A2', a1freq_col:'MAF', bp_column:'BP', + chr_column:'CHR', info_col:'INFO', beta_col:'BETA', + se_col:'SE', pvalue_col:'P', z_col:'Z', n_col:'N', + ncase_col:'N_CASES', ncontrol_col:'N_CONTROLS'}, errors='ignore') + + + + +def compute_z(df_sumstats): + + #make sure that we have required fields + if 'BETA' not in df_sumstats.columns: + raise ValueError('Beta column not found in sumstats file (required to compute Z-scores)') + if 'P' not in df_sumstats.columns: + raise ValueError('P-value column not found in sumstats file (required to compute Z-scores)') + + #compute z-scores + df_sumstats['Z'] = stats.norm(0,1).isf(df_sumstats['P'] / 2.0) * np.sign(df_sumstats['BETA']) + + #Use LDpred-funct trick to estimate Z for SNPs with P=0 + is_zero_pval = np.isinf(df_sumstats['Z']) + if np.any(is_zero_pval): + + #make sure that we have required fields + if 'MAF' not in df_sumstats.columns: + raise ValueError('MAF column not found in sumstats file (required to compute Z-scores for SNPs with P=0)') + + #estimate sigma2pheno + df_sumstats_nonzero = df_sumstats.loc[~is_zero_pval] + df_snp_var_nonzero = 2 * df_sumstats_nonzero['MAF'] * (1-df_sumstats_nonzero['MAF']) + z_prop = df_sumstats_nonzero['BETA'] * np.sqrt(df_snp_var_nonzero) + assert np.corrcoef(z_prop.values, df_sumstats_nonzero['Z'].values)[0,1] > 0.6 + sqrt_sigma2pheno = np.median(df_sumstats_nonzero['Z'].values / z_prop) + assert not np.isnan(sqrt_sigma2pheno) + + #compute Z for SNPs with P=0 + df_sumstats_iszero = df_sumstats.loc[is_zero_pval] + df_snp_var_zero = 2 * df_sumstats_iszero['MAF'] * (1-df_sumstats_iszero['MAF']) + df_sumstats.loc[is_zero_pval, 'Z'] = df_sumstats_iszero['BETA'] * np.sqrt(df_snp_var_zero) * sqrt_sigma2pheno + assert df_sumstats.loc[is_zero_pval, 'Z'].notnull().all() + + return df_sumstats + + + + +def filter_sumstats(df_sumstats, min_info_score=None, min_maf=None, remove_strand_ambig=False, keep_hla=False): + + logging.info('%d SNPs are in the sumstats file'%(df_sumstats.shape[0])) + is_good_snp = np.ones(df_sumstats.shape[0], dtype=bool) + + #remove 'bad' BOLT-LMM SNPs + if 'CHISQ_BOLT_LMM' in df_sumstats.columns: + is_good_chi2_snp = df_sumstats['CHISQ_BOLT_LMM']>0 + is_good_snp = is_good_snp & is_good_chi2_snp + if np.any(~is_good_chi2_snp): + logging.info('Removing %d SNPs with BOLT CHI2=0'%(np.sum(~is_good_chi2_snp))) + + #Filter SNPs based on INFO score + if min_info_score is not None and min_info_score>0: + if 'INFO' not in df_sumstats.columns: + logging.warning('Could not find INFO column. Please set --min-info 0 to omit this warning.') + else: + is_good_info_snp = (df_sumstats['INFO'] >= min_info_score) + is_good_snp = is_good_snp & is_good_info_snp + if np.any(~is_good_info_snp): + logging.info('Removing %d SNPs with INFO<%0.2f'%(np.sum(~is_good_info_snp), min_info_score)) + + #filter SNPs based on MAF + if min_maf is not None and min_maf>0: + if 'MAF' not in df_sumstats.columns: + logging.warning('Could not find MAF column. Please set --min-maf 0 to omit this warning.') + else: + is_good_maf_snp = (df_sumstats['MAF'].between(min_maf, 1-min_maf)) + is_good_snp = is_good_snp & is_good_maf_snp + if np.any(~is_good_maf_snp): + logging.info('Removing %d SNPs with MAF<%s'%(np.sum(~is_good_maf_snp), min_maf)) + + + #find strand ambiguous summary statistics + if remove_strand_ambig: + is_strand_ambig = np.zeros(df_sumstats.shape[0], dtype=bool) + for ambig_pairs in [('A', 'T'), ('T', 'A'), ('C', 'G'), ('G', 'C')]: + is_strand_ambig = is_strand_ambig | ((df_sumstats['A2']==ambig_pairs[0]) & (df_sumstats['A1']==ambig_pairs[1])) + is_good_snp = is_good_snp & (~is_strand_ambig) + if np.any(is_strand_ambig): + logging.info('Removing %d SNPs with strand ambiguity'%(is_strand_ambig.sum())) + + + #remove HLA SNPs + if not keep_hla: + is_hla = (df_sumstats['CHR']==6) & (df_sumstats['BP'].between(28000000, 34000000)) + is_good_snp = is_good_snp & (~is_hla) + if np.any(is_hla): + logging.info('Removing %d HLA SNPs'%(is_hla.sum())) + + + #finally do the actual filtering + if np.any(~is_good_snp): + if not np.any(is_good_snp): + raise ValueError('No SNPs remained after all filtering stages') + df_sumstats = df_sumstats.loc[is_good_snp] + logging.info('%d SNPs with sumstats remained after all filtering stages'%(df_sumstats.shape[0])) + + return df_sumstats + + + +def compute_casecontrol_neff(df_sumstats): + logging.info('Computing the effective sample size for case-control data...') + Neff = (4.0 / (1.0/df_sumstats['N_CASES'] + 1.0/df_sumstats['N_CONTROLS'])).astype(np.int64) + return Neff + + +def sanity_checks(df_sumstats): + if np.any(df_sumstats['CHR'].isnull()): + raise ValueError('Some SNPs have a null chromosome value') + if np.any(df_sumstats['BP'].isnull()): + raise ValueError('Some SNPs have a null base-pair value') + if not is_integer_dtype(df_sumstats['CHR']): + raise ValueError('Some chromosome values are not integers. Please double-check your input') + if not is_integer_dtype(df_sumstats['BP']): + raise ValueError('Some base-pair values are not integers. Please double-check your input') + + #check for duplicates + df_snp = df_sumstats['CHR'].astype('str') + '.' + \ + df_sumstats['BP'].astype('str') + '.' + \ + df_sumstats['A1'].astype('str') + '.' + \ + df_sumstats['A2'].astype('str') + if np.any(df_snp.duplicated()): + raise ValueError('The input file includes duplicate SNPs') + + #compute Z + + +def convert_odds_ratio_to_log(df_sumstats): + if 'OR' not in df_sumstats.columns: + return df_sumstats + + # If there are negative values, assume it already contains log-odds + if np.any(df_sumstats['OR']<0): + return df_sumstats + + # If they are all greater than zero, log transform + if np.all(df_sumstats['OR']>0): + df_sumstats['OR'] = np.log(df_sumstats['OR']) + logging.info('Converting OR column to log-odds') + return df_sumstats + + # Edge case: No negative values, but contains zero(s) + if np.any(df_sumstats['OR']==0): + raise ValueError('The input file includes SNPs with an odds ratio (OR) of 0. Please remove these variant(s).') + + + + +if __name__ == '__main__': + + import argparse + parser = argparse.ArgumentParser() + parser.add_argument('--sumstats', required=True, help='Input summary statistics file') + parser.add_argument('--out', required=True, help='Name of output file') + parser.add_argument('--n', type=int, default=None, help='Sample size. If not specified, will try to infer this from the input file') + parser.add_argument('--min-info', type=float, default=0.6, help='Minimum INFO score (set to zero to avoid INFO-based filtering)') + parser.add_argument('--min-maf', type=float, default=0.001, help='Minimum MAF (set to zero to avoid MAF-based filtering)') + parser.add_argument('--remove-strand-ambig', default=False, action='store_true', help='If specified, strand-ambigous SNPs will be removed') + parser.add_argument('--chi2-cutoff', type=float, default=30, help ='Chi2 cutoff for effective sample size computations') + parser.add_argument('--keep-hla', default=False, action='store_true', help='If specified, Keep SNPs in the HLA region') + parser.add_argument('--no-neff', default=False, action='store_true', help='If specified, use the true rather than the effective sample size in BOLT-LMM runs') + args = parser.parse_args() + + #check package versions + check_package_versions() + + #configure the logger + configure_logger(args.out) + + #read sumstats file + logging.info('Reading sumstats file...') + t0 = time.time() + df_sumstats = pd.read_table(args.sumstats, sep='\s+') + logging.info('Done in %0.2f seconds'%(time.time()-t0)) + + #convert odds-ratio to log-odds ratio if needed + df_sumstats = convert_odds_ratio_to_log(df_sumstats) + + #rename df_sumstats columns + df_sumstats = rename_df_columns(df_sumstats) + + #filter sumstats + df_sumstats = filter_sumstats(df_sumstats, min_info_score=args.min_info, min_maf=args.min_maf, remove_strand_ambig=args.remove_strand_ambig, keep_hla=args.keep_hla) + + #do some sanity checks + sanity_checks(df_sumstats) + + #compute Neff + if 'CHISQ_BOLT_LMM' in df_sumstats.columns and not args.no_neff: + if args.n is None: + raise ValueError('--n must be specified with BOLT input files') + Neff = compute_Neff(df_sumstats, args.n, args.chi2_cutoff) + logging.info('Effective sample size is %s'%(Neff)) + df_sumstats['N'] = Neff + elif args.n is not None: + if 'N' in df_sumstats.columns: + raise ValueError('cannot both specify --n and have an N column in the sumstats file') + if 'N_CASES' in df_sumstats.columns or 'N_CONTROLS' in df_sumstats.columns: + raise ValueError('cannot both specify --n and have an N_cases/N_controls column in the sumstats file') + df_sumstats['N'] = args.n + elif 'N' in df_sumstats.columns: + if 'N_CASES' in df_sumstats.columns or 'N_CONTROLS' in df_sumstats.columns: + raise ValueError('cannot both have an N column and N_cases/N_controls columns in the sumstats file') + pass + elif 'N_CASES' in df_sumstats.columns and 'N_CONTROLS' in df_sumstats.columns: + df_sumstats['N'] = compute_casecontrol_neff(df_sumstats) + else: + raise ValueError('must specify sample size, via either (1) --n flag; (2) N column in the sumstats file; or (3) Two columns N_cases, N_controls in the sumstats file') + + # #create SNP string + # df_sumstats['SNP'] = df_sumstats['SNP'].astype('str') + '.' + \ + # df_sumstats['CHR'].astype('str') + '.' + \ + # df_sumstats['BP'].astype('str') + '.' + \ + # df_sumstats['A1'].astype('str') + '.' + \ + # df_sumstats['A2'].astype('str') + + #compute Z + if 'Z' in df_sumstats.columns: + pass + elif 'CHISQ_BOLT_LMM' in df_sumstats.columns: + df_sumstats['Z'] = np.sqrt(df_sumstats['CHISQ_BOLT_LMM']) * np.sign(df_sumstats['BETA']) + elif 'BETA' in df_sumstats.columns and 'SE' in df_sumstats.columns: + if np.any(df_sumstats['SE']==0): + raise ValueError('Found SNPs with BETA stderr==0') + df_sumstats['Z'] = df_sumstats['BETA'] / df_sumstats['SE'] + elif 'P' in df_sumstats.columns: + df_sumstats = compute_z(df_sumstats) + else: + raise ValueError('Sumstats file must include a p-value, Z-score or chi2 column to compute Z-scores') + + #write output + logging.info('Saving munged sumstats of %d SNPs to %s'%(df_sumstats.shape[0], args.out)) + if 'MAF' in df_sumstats.columns: + df_sumstats.loc[df_sumstats['MAF']>0.5, 'MAF'] = 1 - df_sumstats.loc[df_sumstats['MAF']>0.5, 'MAF'] + columns_to_keep = [c for c in df_sumstats.columns if c in ['SNP', 'CHR', 'BP', 'A1', 'A2', 'Z', 'N', 'MAF']] + df_sumstats[columns_to_keep].to_parquet(args.out) + logging.info('Done') + + diff --git a/SCRIPTS/polyfun/polyfun.py b/SCRIPTS/polyfun/polyfun.py new file mode 100644 index 0000000..e17f432 --- /dev/null +++ b/SCRIPTS/polyfun/polyfun.py @@ -0,0 +1,854 @@ +import numpy as np +import pandas as pd +import os +import time +from ldsc_polyfun import jackknife, regressions, sumstats, ldscore, parse +import logging +from copy import deepcopy +from tqdm import tqdm +from polyfun_utils import Logger, check_package_versions, set_snpid_index, configure_logger, get_file_name +from polyfun_utils import SNP_COLUMNS +from pyarrow import ArrowIOError +from pyarrow.lib import ArrowInvalid +from compute_ldscores_from_ld import compute_ldscores_chr +import tempfile + + +MAX_CHI2=80 + + +def __filter__(fname, noun, verb, merge_obj): + merged_list = None + if fname: + f = lambda x,n: x.format(noun=noun, verb=verb, fname=fname, num=n) + x = parse.FilterFile(fname) + c = 'Read list of {num} {noun} to {verb} from {fname}' + logging.info(f(c, len(x.IDList))) + merged_list = merge_obj.loj(x.IDList) + len_merged_list = len(merged_list) + if len_merged_list > 0: + c = 'After merging, {num} {noun} remain' + logging.info(f(c, len_merged_list)) + else: + error_msg = 'No {noun} retained for analysis' + raise ValueError(f(error_msg, 0)) + + return merged_list + + +def splash_screen(): + print('*********************************************************************') + print('* PolyFun (POLYgenic FUNctionally-informed fine-mapping)') + print('* Version 1.0.0') + print('* (C) 2019-2024 Omer Weissbrod') + print('*********************************************************************') + print() + + +def check_args(args): + + #verify that the requested computations are valid + mode_params = np.array([args.compute_h2_L2, args.compute_ldscores, args.compute_h2_bins]) + if np.sum(mode_params)==0: + raise ValueError('must specify at least one of --compute-h2-L2, --compute-ldscores, --compute-h2-bins') + if args.compute_h2_L2 and args.compute_h2_bins and not args.compute_ldscores: + raise ValueError('cannot use both --compute-h2_L2 and --compute_h2_bins without also specifying --compute-ldscores') + if args.chr is not None: + if args.compute_h2_L2 or args.compute_h2_bins: + raise ValueError('--chr can only be specified when using only --compute-ldscores') + if args.bfile_chr is not None: + if not args.compute_ldscores: + raise ValueError('--bfile-chr can only be specified when using --compute-ldscores') + if args.ld_ukb: + if not args.compute_ldscores: + raise ValueError('--ld-ukb can only be specified when using --compute-ldscores') + if args.no_partitions: + if not args.compute_h2_L2: + raise ValueError('cannot specify --no-partitions without specifying --compute-h2-L2') + if args.compute_ldscores: + raise ValueError('cannot specify both --no-partitions and --compute-ldscores') + if args.compute_h2_bins: + raise ValueError('cannot specify both --no-partitions and --compute-h2-bins') + if args.compute_ldscores and args.compute_h2_bins and not args.compute_h2_L2: + raise ValueError('cannot use both --compute-ldscores and --compute_h2_bins without also specifying --compute-h2-L2') + + #verify partitioning parameters + if args.skip_Ckmedian and (args.num_bins is None or args.num_bins<=0): + raise ValueError('You must specify --num-bins when using --skip-Ckmedian') + + #verify LD-score related parameters + if args.ld_dir is not None and not args.ld_ukb: + raise ValueError('You cannot specify --ld-dir without also specifying --ld-ukb') + if args.bfile_chr is not None and args.ld_ukb: + raise ValueError('You can specify only one of --bfile-chr and --ld-ukb') + + if args.compute_ldscores: + if args.bfile_chr is None and not args.ld_ukb: + raise ValueError('You must specify either --bfile-chr or --ld-ukb when you specify --compute-ldscores') + if not args.ld_ukb and (args.ld_wind_cm is None and args.ld_wind_kb is None and args.ld_wind_snps is None): + args.ld_wind_cm = 1.0 + logging.warning('no ld-wind argument specified. PolyFun will use --ld-cm 1.0') + + if not args.compute_ldscores: + if not (args.ld_wind_cm is None and args.ld_wind_kb is None and args.ld_wind_snps is None): + raise ValueError('--ld-wind parameters can only be specified together with --compute-ldscores') + if args.keep is not None: + raise ValueError('--keep can only be specified together with --compute-ldscores') + if args.chr is not None: + raise ValueError('--chr can only be specified together with --compute-ldscores') + + + + if args.compute_h2_L2: + if args.sumstats is None: + raise ValueError('--sumstats must be specified when using --compute-h2-L2') + if args.ref_ld_chr is None: + raise ValueError('--ref-ld-chr must be specified when using --compute-h2-L2') + if args.w_ld_chr is None: + raise ValueError('--w-ld-chr must be specified when using --compute-h2-L2') + + if args.compute_h2_bins: + if args.sumstats is None: + raise ValueError('--sumstats must be specified when using --compute-h2-bins') + if args.w_ld_chr is None: + raise ValueError('--w-ld-chr must be specified when using --compute-h2-bins') + if args.ref_ld_chr is not None and not args.compute_ldscores: + raise ValueError('--ref-ld-chr should not be specified when using --compute-h2-bins, unless you also use --compute-ldscores') + + + return args + +def check_files(args): + + #check that required input files exist + if args.compute_h2_L2: + if not os.path.exists(args.sumstats): + raise IOError('Cannot find sumstats file %s'%(args.sumstats)) + for chr_num in range(1, args.num_chr+1): + get_file_name(args, 'ref-ld', chr_num, verify_exists=True, allow_multiple=True) + get_file_name(args, 'w-ld', chr_num, verify_exists=True) + get_file_name(args, 'annot', chr_num, verify_exists=True, allow_multiple=True) + + if args.compute_ldscores: + if args.chr is None: chr_range = range(1, args.num_chr+1) + else: chr_range = range(args.chr, args.chr+1) + + for chr_num in chr_range: + if args.bfile_chr is not None: + get_file_name(args, 'bim', chr_num, verify_exists=True) + get_file_name(args, 'fam', chr_num, verify_exists=True) + get_file_name(args, 'bed', chr_num, verify_exists=True) + if not args.compute_h2_L2: + get_file_name(args, 'snpvar_ridge', chr_num, verify_exists=True) + get_file_name(args, 'bins', chr_num, verify_exists=True) + + if args.compute_h2_bins and not args.compute_ldscores: + for chr_num in range(1, args.num_chr+1): + get_file_name(args, 'w-ld', chr_num, verify_exists=True) + if not args.compute_h2_L2: + get_file_name(args, 'bins', chr_num, verify_exists=True) + + + + + + + +class PolyFun: + def __init__(self): + pass + + def run_ldsc(self, args, use_ridge, nn, keep_large, evenodd_split, n_blocks=2): + + #prepare LDSC objects + log = Logger() + args.h2 = args.sumstats + args.ref_ld = None + args.w_ld = None + args.n_blocks = n_blocks + args.M = None + args.not_M_5_50 = True + + #if not ridge, the we'll use the LD-scores of our bins + if not use_ridge: + args = deepcopy(args) + args.ref_ld_chr = args.output_prefix+'.' + + #read input data + if use_ridge or not args.compute_ldscores or True: + M_annot, w_ld_cname, ref_ld_cnames, df_sumstats, _ = sumstats._read_ld_sumstats(args, log, args.h2) + else: + #TODO: Don't reload files if we don't have to... + M_annot = self.M + w_ld_cname = 'w_ld' + ref_ld_cnames = self.df_bins.columns + try: + df_sumstats = pd.read_parquet(args.sumstats) + except (ArrowIOError, ArrowInvalid): + df_sumstats = pd.read_table(args.sumstats, sep='\s+') + ###merge everything together... + + #prepare LD-scores for S-LDSC run + ref_ld = np.array(df_sumstats[ref_ld_cnames], dtype=np.float32) + sumstats._check_ld_condnum(args, log, ref_ld_cnames) + if df_sumstats.shape[0] < 200000: + logging.warning('number of SNPs is smaller than 200k; this is almost always bad.') + n_snp = len(df_sumstats) + n_blocks = np.minimum(n_snp, args.n_blocks) + n_annot = len(ref_ld_cnames) + if n_annot<=1: + raise ValueError('Only one annotation found') + chisq_max = max(0.001*df_sumstats['N'].max(), MAX_CHI2) + + #prepare chi2 statistics + s = lambda x: np.array(x).reshape((n_snp, 1)) + chisq = s(df_sumstats.Z**2).astype(np.float32) + ii = np.ravel(chisq < chisq_max) + df_sumstats = df_sumstats.loc[ii, :] + if np.any(~ii): + logging.info('Removed {M} SNPs with chi^2 > {C} ({N} SNPs remain)'.format( + C=chisq_max, N=np.sum(ii), M=n_snp-np.sum(ii))) + n_snp = np.sum(ii) # lambdas are late-binding, so this works + ref_ld = np.array(df_sumstats[ref_ld_cnames], dtype=np.float32) + chisq = chisq[ii].reshape((n_snp, 1)) + + #Run S-LDSC + self.ref_ld_cnames = [c[:c.rfind('_')] for c in ref_ld_cnames if c not in SNP_COLUMNS] + hsqhat = regressions.Hsq(chisq, + ref_ld, + s(df_sumstats[w_ld_cname]), + s(df_sumstats.N), + M_annot, n_blocks=n_blocks, intercept=None, + twostep=None, old_weights=True, + chr_num=df_sumstats['CHR'], + loco=use_ridge, ridge_lambda=None, + standardize_ridge=True, + approx_ridge=True, + num_chr_sets=2, + evenodd_split=evenodd_split, + nn=nn, + keep_large=keep_large, + nnls_exact=args.nnls_exact, + num_chr=args.num_chr + ) + + #save the results object + if use_ridge: + self.hsqhat_ridge = hsqhat + else: + self.hsqhat = hsqhat + + + + def load_annotations_file(self, args, chr_num, use_ridge): + + #load annotations file for this chromosome + if use_ridge: + annot_filenames = get_file_name(args, 'annot', chr_num, allow_multiple=True) + else: + annot_filenames = [get_file_name(args, 'bins', chr_num)] + + #load annotation file(s) + df_annot_chr_list = [] + for annot_filename in annot_filenames: + try: + df_annot_chr = pd.read_parquet(annot_filename) + except (ArrowIOError, ArrowInvalid): + df_annot_chr = pd.read_table(annot_filename) + df_annot_chr_list.append(df_annot_chr) + if len(df_annot_chr_list)==1: + df_annot_chr = df_annot_chr_list[0] + else: + for df in df_annot_chr_list[1:]: + for snp_col in SNP_COLUMNS: + if (df.shape[0] != df_annot_chr_list[0].shape[0]) or (np.any(df[snp_col] != df_annot_chr_list[0][snp_col])): + raise ValueError('Different annotation files of chromosome %d must be perfectly aligned'%(chr_num)) + df.drop(columns=['CM'], inplace=True, errors='ignore') + df.drop(columns=SNP_COLUMNS, inplace=True, errors='raise') + df_annot_chr = pd.concat(df_annot_chr_list, axis=1) + + #make sure all required columns were found + df_annot_chr.drop(columns=['CM'], inplace=True, errors='ignore') + found_missing_col = False + for colname in SNP_COLUMNS: + if colname not in df_annot_chr.columns: + logging.error('%s has a missing column: %s'%(annot_filename, colname)) + found_missing_col = True + if found_missing_col: + raise ValueError('Missing columns found in %s'%(annot_filename)) + + #subset annotations if requested + if args.anno is not None: + anno_to_use = args.anno.split(',') + assert np.all(np.isin(anno_to_use, df_annot_chr.columns)) + df_annot_chr = df_annot_chr[SNP_COLUMNS + anno_to_use] + + #if we have more annotations that ref-ld, it might mean that some annotations were removed, so remove them from here as well + if not np.all(np.isin(self.ref_ld_cnames, df_annot_chr.columns)): + missing_annot = [c for c in self.ref_ld_cnames if c not in df_annot_chr.columns] + raise ValueError('The following annotations have LD-scores but are not in any of the annotation files: %s'%(missing_annot)) + if len(self.ref_ld_cnames) < len(df_annot_chr.columns) - len(SNP_COLUMNS): + df_annot_chr = df_annot_chr[SNP_COLUMNS + self.ref_ld_cnames] + + #make sure that we get the same columns as the ones in the LD-score files + if not np.all([c for c in df_annot_chr.columns if c not in SNP_COLUMNS ]== self.ref_ld_cnames): + raise ValueError('Annotation names in annotations file do not match the one in the LD-scores file') + + return df_annot_chr + + + def compute_snpvar_chr(self, args, chr_num, use_ridge): + + #load annotations file from disk + df_annot_chr = self.load_annotations_file(args, chr_num, use_ridge) + + #extract taus from a jknife object + if use_ridge: + hsqhat = self.hsqhat_ridge + jknife = hsqhat.jknife_ridge + + #make sure that the chromosome exists in one set + found_chrom = np.any([chr_num in chr_set for chr_set in jknife.chromosome_sets]) + if not found_chrom: + raise ValueError('not all chromosomes have a taus estimate - please make sure that the intersection of SNPs with sumstats and with annotations data spans all chromosomes') + + #find the relevant set number + set_num=None + for chr_set_i, chr_set in enumerate(jknife.chromosome_sets): + if chr_num not in chr_set: + assert set_num is None + set_num = chr_set_i + if set_num is None: + raise ValueError('Could not find Ridge predictions for chromosome %d'%(chr_num)) + + #compute and return snpvar + taus = jknife.est_loco_ridge[set_num][:hsqhat.n_annot] / hsqhat.Nbar + + else: + hsqhat = self.hsqhat + jknife = hsqhat.jknife + if len(jknife.est_loco) != args.num_chr: + raise ValueError('not all chromosomes have a taus estimate - please make sure that the intersection of SNPs with sumstats and with annotations data spans all chromosomes') + taus = jknife.est_loco[chr_num-1][:hsqhat.n_annot] / hsqhat.Nbar + + #save the taus to disk + taus_output_file = get_file_name(args, ('taus_ridge' if use_ridge else 'taus_nn'), chr_num, verify_exists=False) + df_taus = pd.Series(taus, index=df_annot_chr.drop(columns=SNP_COLUMNS, errors='raise').columns) + df_taus.index.name = 'ANNOTATION' + df_taus.name = 'ANNOTATION_COEFFICIENT' + df_taus.to_csv(taus_output_file, header=True, index=True, sep='\t') + + #compute and return the snp variances + df_snpvar_chr = df_annot_chr.drop(columns=SNP_COLUMNS, errors='raise').dot(taus) + df_snpvar_chr = df_snpvar_chr.to_frame(name='SNPVAR') + df_snpvar_chr = pd.concat((df_annot_chr[SNP_COLUMNS], df_snpvar_chr), axis=1) + return df_snpvar_chr + + + def compute_snpvar(self, args, use_ridge): + logging.info('Computing per-SNP h^2 for each chromosome...') + + #iterate over chromosomes + df_snpvar_chr_list = [] + for chr_num in tqdm(range(1, args.num_chr+1)): + df_snpvar_chr = self.compute_snpvar_chr(args, chr_num, use_ridge=use_ridge) + df_snpvar_chr_list.append(df_snpvar_chr) + df_snpvar = pd.concat(df_snpvar_chr_list, axis=0) + df_snpvar.reset_index(inplace=True, drop=True) + + #save snpvar to a class member + if use_ridge: + self.df_snpvar_ridge = df_snpvar + else: + self.df_snpvar = df_snpvar + + + + def create_df_bins(self, bin_sizes, df_snpvar, df_snpvar_sorted=None, min_bin_size=10): + + #sort df_snpvar if needed + if df_snpvar_sorted is None: + df_snpvar_sorted = df_snpvar['SNPVAR'].sort_values() + assert bin_sizes.sum() == df_snpvar_sorted.shape[0] + + + #rearrange bins to prevent very small bins + bin_i = len(bin_sizes)-1 + while True: + + #if the current bin is large enough, proceed to the previous one + if bin_sizes[bin_i] >= min_bin_size: + bin_i -= 1 + if bin_i==0: break + continue + + #Compare the effects of the weakest bin in the current bin, and the strongest bin in the previous bin + bin_start_ind = bin_sizes[:bin_i].sum() + weakest_bin_snp = df_snpvar_sorted.iloc[::-1].iloc[bin_start_ind] + strongest_lastbin_snp = df_snpvar_sorted.iloc[::-1].iloc[bin_start_ind-1] + + num_snps_to_transfer = np.minimum(min_bin_size-bin_sizes[bin_i], bin_sizes[bin_i-1]) + bin_sizes[bin_i] += num_snps_to_transfer + bin_sizes[bin_i-1] -= num_snps_to_transfer + + #if we emptied the previous bin, delete it + if bin_sizes[bin_i-1]==0: + bin_sizes = np.concatenate((bin_sizes[:bin_i-1], bin_sizes[bin_i:])) + bin_i -= 1 + + #if the current bin is large enough, move to the previous one + if bin_sizes[bin_i] >= min_bin_size: + bin_i -= 1 + + if bin_i==0: break + + + #create df_bins + ind=0 + df_bins = pd.DataFrame(index=df_snpvar_sorted.index) + for bin_i, bin_size in enumerate(bin_sizes): + snpvar_bin = np.zeros(df_bins.shape[0], dtype=bool) + snpvar_bin[ind : ind+bin_size] = True + df_bins['snpvar_bin%d'%(len(bin_sizes) - bin_i)] = snpvar_bin + ind += bin_size + assert np.all(df_bins.sum(axis=0) == bin_sizes) + df_bins = df_bins.iloc[:, ::-1] + assert df_bins.shape[0] == df_snpvar.shape[0] + assert np.all(df_bins.sum(axis=1)==1) + + #reorder df_bins + df_bins = df_bins.loc[df_snpvar.index] + df_bins = pd.concat((df_snpvar[SNP_COLUMNS], df_bins), axis=1) + assert np.all(df_bins.index == df_snpvar.index) + + return df_bins + + + + def partition_snps_Ckmedian(self, args, use_ridge): + logging.info('Clustering SNPs into bins using the R Ckmeans.1d.dp package') + + #try loading the Ckmeans.1d.dp package + try: + import rpy2 + import rpy2.robjects.numpy2ri as numpy2ri + try: + from importlib import reload + reload(rpy2.robjects.numpy2ri) + except: + pass + import rpy2.robjects as ro + ro.conversion.py2ri = numpy2ri + numpy2ri.activate() + from rpy2.robjects.packages import importr + importr('Ckmeans.1d.dp') + median_seg_func = ro.r('Ckmedian.1d.dp') + mean_seg_func = ro.r('Ckmeans.1d.dp') + except: + logging.error('Could not load the R package Ckmeans.1d.dp. Either install it or rerun PolyFun with --skip-Ckmedian') + logging.error('') + raise + + #access the right class member + if use_ridge: + df_snpvar = self.df_snpvar_ridge + else: + df_snpvar = self.df_snpvar + + #sort df_snpvar + df_snpvar_sorted = df_snpvar['SNPVAR'].sort_values() + + #perform the segmentation + if args.num_bins is None or args.num_bins<=0: + logging.info('Determining the optimal number of bins (if this is slow, consider using --num-bins 20 (or some other number))') + seg_obj = median_seg_func(df_snpvar_sorted.values, k=np.array([5,30])) + else: + seg_obj = median_seg_func(df_snpvar_sorted.values, k=args.num_bins) + bin_sizes = np.array(seg_obj.rx2('size')).astype(np.int64) + num_bins = len(bin_sizes) + logging.info('Ckmedian.1d.dp partitioned SNPs into %d bins'%(num_bins)) + + #define df_bins + df_bins = self.create_df_bins(bin_sizes, df_snpvar, df_snpvar_sorted=df_snpvar_sorted) + return df_bins + + + + + def partition_snps_Kmeans(self, args, use_ridge): + logging.info('Clustering SNPs into bins using K-means clustering with %d bins'%(args.num_bins)) + + #make sure that we can run K-means clustering + assert args.num_bins is not None and args.num_bins>0 + try: + from sklearn.cluster import KMeans + except ImportError: + raise ImportError('sklearn not properly installed. Please reinstall it') + + #access the right class member + if use_ridge: df_snpvar = self.df_snpvar_ridge + else: df_snpvar = self.df_snpvar + + + #perform K-means clustering + kmeans_obj = KMeans(n_clusters=args.num_bins) + kmeans_obj.fit(df_snpvar[['SNPVAR']]) + assert kmeans_obj.cluster_centers_.shape[0] == args.num_bins + + #Make sure that clusters are contiguous + bins_order = np.argsort(kmeans_obj.cluster_centers_[:,0]) + for bin_i, cluster_label in enumerate(bins_order[:-1]): + next_cluster_label = bins_order[bin_i+1] + assert df_snpvar.loc[kmeans_obj.labels_==cluster_label, 'SNPVAR'].max() <= df_snpvar.loc[kmeans_obj.labels_==next_cluster_label, 'SNPVAR'].min() + + #define bin_sizes + bin_sizes = np.bincount(kmeans_obj.labels_)[bins_order] + + #define df_bins + df_bins = self.create_df_bins(bin_sizes, df_snpvar, df_snpvar_sorted=None) + return df_bins + + + def partition_snps_to_bins(self, args, use_ridge): + + #if skip_ckmedian was specified, run regular K-means + if args.skip_Ckmedian: + self.df_bins = self.partition_snps_Kmeans(args, use_ridge=use_ridge) + else: + self.df_bins = self.partition_snps_Ckmedian(args, use_ridge=use_ridge) + + + def save_bins_to_disk(self, args): + logging.info('Saving SNP-bins to disk') + for chr_num in tqdm(range(1, args.num_chr+1)): + + #save bins file to disk + df_bins_chr = self.df_bins.query('CHR==%d'%(chr_num)) + bins_chr_file = get_file_name(args, 'bins', chr_num, verify_exists=False) + df_bins_chr.to_parquet(bins_chr_file, index=False) + + #save M files to disk + M_chr_file = get_file_name(args, 'M', chr_num, verify_exists=False) + M_chr = df_bins_chr.drop(columns=SNP_COLUMNS).sum(axis=0).values + np.savetxt(M_chr_file, M_chr.reshape((1, M_chr.shape[0])), fmt='%i') + + + + + def save_snpvar_to_disk(self, args, use_ridge, constrain_range): + if constrain_range: + logging.info('Saving constrained SNP variances to disk') + else: + logging.info('Saving SNP variances to disk') + + #determine which df_snpvar to use + if use_ridge: df_snpvar = self.df_snpvar_ridge + else: df_snpvar = self.df_snpvar + + #constrain the ratio between the largest and smallest snp-var + if constrain_range: + df_snpvar = df_snpvar.copy() + h2_total = df_snpvar['SNPVAR'].sum() + min_snpvar = df_snpvar['SNPVAR'].max() / args.q + df_snpvar.loc[df_snpvar['SNPVAR'] < min_snpvar, 'SNPVAR'] = min_snpvar + df_snpvar['SNPVAR'] *= h2_total / df_snpvar['SNPVAR'].sum() + assert np.isclose(df_snpvar['SNPVAR'].sum(), h2_total) + + #merge snpvar with sumstats + try: + df_sumstats = pd.read_parquet(args.sumstats) + except (ArrowIOError, ArrowInvalid): + df_sumstats = pd.read_table(args.sumstats, sep='\s+') + df_sumstats.drop(columns=['SNP'], errors='ignore', inplace=True) + for col in ['CHR', 'BP', 'A1', 'A2']: + if col not in df_sumstats.columns: + raise ValueError('sumstats file has a missing column: %s'%(col)) + df_snpvar = set_snpid_index(df_snpvar, copy=True) + df_sumstats = set_snpid_index(df_sumstats) + svpvar_cols = df_snpvar.columns.copy() + df_snpvar.drop(columns=['CHR', 'BP', 'A1', 'A2'], inplace=True) + df_snpvar = df_snpvar.merge(df_sumstats, left_index=True, right_index=True) + df_snpvar = df_snpvar[list(svpvar_cols) + [c for c in df_sumstats.columns if c not in list(svpvar_cols)]] + if df_snpvar.shape[0] < df_sumstats.shape[0]: + error_message = 'not all SNPs in the sumstats file and/or in the LD reference files are also in the annotations file' + if args.allow_missing: + logging.warning(error_message + '. Keeping %d/%d SNPs'%(df_snpvar.shape[0], df_sumstats.shape[0])) + else: + raise ValueError(error_message + '. If you wish to omit the missing SNPs, please use the flag --allow-missing') + + #iterate over chromosomes + for chr_num in tqdm(range(1, args.num_chr+1)): + + #define output file name + output_fname = 'snpvar' + if use_ridge: output_fname += '_ridge' + if constrain_range: output_fname += '_constrained' + snpvar_chr_file = get_file_name(args, output_fname, chr_num, verify_exists=False) + + #save snpvar to file + df_snpvar_chr = df_snpvar.query('CHR==%d'%(chr_num)) + df_snpvar_chr.to_csv(snpvar_chr_file, index=False, sep='\t', compression='gzip', float_format='%0.4e') + + + + def polyfun_h2_L2(self, args): + #run Ridge regression + self.run_ldsc(args, use_ridge=True, nn=False, evenodd_split=False, keep_large=False) + + #compute per-SNP h^2 based on L2-regularized S-LDSC coefficients + self.compute_snpvar(args, use_ridge=True) + + #save L2-regularized S-LDSC per-SNP h^2 to disk + self.save_snpvar_to_disk(args, use_ridge=True, constrain_range=True) + self.save_snpvar_to_disk(args, use_ridge=True, constrain_range=False) + + #partitions SNPs into bins and save to disk, unless explicitly requested not to + if not args.no_partitions: + self.partition_snps_to_bins(args, use_ridge=True) + self.save_bins_to_disk(args) + + + def load_bins_chr(self, args, chr_num): + bins_file = get_file_name(args, 'bins', chr_num) + df_bins_chr = pd.read_parquet(bins_file) + return df_bins_chr + + + def compute_ld_scores(self, args): + #define the range of chromosomes to iterate over + if args.chr is None: + chr_range = range(1, args.num_chr+1) + else: + chr_range = range(args.chr, args.chr+1) + + #iterate over chromosomes and compute LD-scores + ###df_ldscores_chr_list = [] + for chr_num in tqdm(chr_range, disable=len(chr_range)==1): + + #load or extract the bins for the current chromosome + try: + df_bins_chr = self.df_bins.query('CHR==%d'%(chr_num)) + except AttributeError: + df_bins_chr = self.load_bins_chr(args, chr_num) + + #compute LD-scores for this chromosome + if args.ld_ukb: + if args.ld_dir is None: ld_dir = tempfile.mkdtemp() + else: ld_dir = args.ld_dir + df_bins_chr = set_snpid_index(df_bins_chr) + df_ldscores_chr = compute_ldscores_chr(df_bins_chr, ld_dir=ld_dir, use_ukb=True) + elif args.bfile_chr is not None: + df_ldscores_chr = self.compute_ldscores_plink_chr(args, chr_num, df_bins_chr) + else: + raise ValueError('no LDscore computation method specified') + + #save the LD-scores to disk + ldscores_output_file = get_file_name(args, 'ldscores', chr_num, verify_exists=False) + df_ldscores_chr.to_parquet(ldscores_output_file, index=False) + + # #add the ldscores to the LDscores list + # df_ldscores_chr_list.append(df_ldscores_chr) + + # #concatenate all the LD-score dfs + # if len(df_ldscores_chr_list)==1: + # self.df_bin_ldscores = df_ldscores_chr_list[0] + # else: + # self.df_bin_ldscores = pd.concat(df_ldscores_chr_list, axis=0) + # self.df_bin_ldscores.reset_index(inplace=True, drop=True) + + + + + + + def compute_ldscores_plink_chr(self, args, chr_num, df_bins_chr): + + # read bim/snp + bim_file = get_file_name(args, 'bim', chr_num) + array_snps = parse.PlinkBIMFile(bim_file) + df_bim = array_snps.df + df_bim = set_snpid_index(df_bim) + + #Remove annotations of SNPs that are not in the .bim file + df_bins_chr = set_snpid_index(df_bins_chr) + df_bins_chr = df_bins_chr.loc[df_bins_chr.index.isin(df_bim.index)] + + #make sure that all SNPs have a bin + keep_snps = None + if np.any(~df_bim.index.isin(df_bins_chr.index)): + error_msg = 'Not all SNPs were assigned a bin (meaning some SNPS in the summary statistics and/or in the LD reference files are not in the annotation files)' + if args.allow_missing: + is_good_snp = df_bim.index.isin(df_bins_chr.index) + if not np.any(is_good_snp): + raise ValueError('No SNPs in chromosome %d have annotations'%(chr_num)) + keep_snps = np.where(is_good_snp)[0] + logging.warning(error_msg) + logging.warning('Keeping only %d/%d SNPs in chromosome %d that have annotations'%(df_bim.shape[0], len(is_good_snp), chr_num)) + else: + raise ValueError(error_msg + '. If you wish to omit the missing SNPs, please use the flag --allow-missing') + + #find #individuals in bfile + fam_file = get_file_name(args, 'fam', chr_num) + df_fam = pd.read_table(fam_file, header=None, usecols=[5], sep='\s+') + n = df_fam.shape[0] + + #find keep_indivs + if args.keep is None: + keep_indivs= None + else: + array_indivs = parse.PlinkFAMFile(args.bfile+'.fam') + keep_indivs = __filter__(args.keep, 'individuals', 'include', array_indivs) + logging.info('after applying --keep, %d individuals remain'%(len(keep_indivs))) + + #read plink file + logging.info('Loading SNP file...') + bed_file = get_file_name(args, 'bed', chr_num) + geno_array = ldscore.PlinkBEDFile(bed_file, n, array_snps, keep_snps=keep_snps, + keep_indivs=keep_indivs, mafMin=None) + + #remove omitted SNPs from df_bim + if len(geno_array.kept_snps) != df_bim.shape[0]: + assert np.all(np.array(geno_array.kept_snps) == np.sort(np.array(geno_array.kept_snps))) + assert geno_array.kept_snps[-1] < df_bim.shape[0] + df_bim = df_bim.iloc[geno_array.kept_snps] + + #rearrange annotations to match the order of SNPs in the plink file + assert df_bins_chr.shape[0] >= df_bim.shape[0] + if (df_bins_chr.shape[0] > df_bim.shape[0]) or np.any(df_bins_chr.index != df_bim.index): + assert np.all(df_bim.index.isin(df_bins_chr.index)) + df_bins_chr = df_bins_chr.loc[df_bim.index] + + # determine block widths + num_wind_args = np.array((args.ld_wind_snps, args.ld_wind_kb, args.ld_wind_cm), dtype=bool) + if np.sum(num_wind_args) != 1: + raise ValueError('Must specify exactly one --ld-wind option') + if args.ld_wind_snps: + max_dist = args.ld_wind_snps + coords = np.array(list(range(geno_array.m))) + elif args.ld_wind_kb: + max_dist = args.ld_wind_kb*1000 + coords = np.array(df_bim['BP']) + if len(np.unique(coords)) == 1: + raise ValueError('bim file has no basepair data --- please use a different ld-wind option') + elif args.ld_wind_cm: + max_dist = args.ld_wind_cm + coords = np.array(df_bim['CM']) + if len(np.unique(coords)) == 1: + raise ValueError('bim file has no CM data --- please use a different ld-wind option') + + #compute LD-scores + block_left = ldscore.getBlockLefts(coords, max_dist) + if block_left[len(block_left)-1] == 0: + error_msg = 'Only a single block selected - this is probably a mistake' + raise ValueError(error_msg) + t0 = time.time() + geno_array._currentSNP = 0 + logging.info('Computing LD scores for chromosome %d'%(chr_num)) + ldscores = geno_array.ldScoreVarBlocks(block_left, args.chunk_size, annot=df_bins_chr.drop(columns=SNP_COLUMNS).values) + + #create an ldscores df + df_ldscores = pd.DataFrame(ldscores, index=df_bins_chr.index, columns=df_bins_chr.drop(columns=SNP_COLUMNS).columns) + df_ldscores = pd.concat((df_bins_chr[SNP_COLUMNS], df_ldscores), axis=1) + return df_ldscores + + + + def compute_h2_bins(self, args, constrain_range): + #run S-LDSC + self.run_ldsc(args, use_ridge=False, nn=True, evenodd_split=True, keep_large=False) + + #compute per-SNP h^2 + self.compute_snpvar(args, use_ridge=False) + + #save per-SNP h^2 to disk + self.save_snpvar_to_disk(args, use_ridge=False, constrain_range=constrain_range) + + + + + + def polyfun_main(self, args): + + #compute snp variances using L2-regularized S-LDSC with an odd/even chromosome split + if args.compute_h2_L2: + self.polyfun_h2_L2(args) + + #compute LD-scores of SNP partitions + if args.compute_ldscores: + self.compute_ld_scores(args) + + if args.compute_h2_bins: + self.compute_h2_bins(args, constrain_range=True) + + + + +if __name__ == '__main__': + + import argparse + parser = argparse.ArgumentParser() + + #partitioning-related parameters + parser.add_argument('--num-bins', type=int, default=None, help='Number of bins to partition SNPs into. If not specified, PolyFun will automatically select this number based on a BIC criterion') + parser.add_argument('--anno', default=None, help='Comma-delimited list of annotations to use (if not specified, will use all annotations)') + parser.add_argument('--skip-Ckmedian', default=False, action='store_true', help='If specified, use a regular K-means algorithm instead of the R Ckmeans.1d.dp package') + + #mode related parameters + parser.add_argument('--compute-ldscores', default=False, action='store_true', help='If specified, PolyFun will compute LD-scores of SNP bins') + parser.add_argument('--compute-h2-L2', default=False, action='store_true', help='If specified, PolyFun will compute per-SNP h2 using L2-regularized S-LDSC') + parser.add_argument('--compute-h2-bins', default=False, action='store_true', help='If specified, PolyFun will robustly compute per-SNP h2 based on SNP bins') + parser.add_argument('--no-partitions', default=False, action='store_true', help='If specified, PolyFun will not partition SNPs into bins (do this only if you want to use the per-SNP h^2 from L2-regularized S-LDSC as prior causal probabilities, which is typically not recommended)') + + #ld-score related parameters + parser.add_argument('--chr', type=int, default=None, help='Chromosome number (only applicable when only specifying --ldscores). If not set, PolyFun will compute LD-scores for all chromosomes') + parser.add_argument('--ld-wind-cm', type=float, default=None, help='window size to be used for estimating LD-scores in units of centiMorgans (cM).') + parser.add_argument('--ld-wind-kb', type=int, default=None, help='window size to be used for estimating LD-scores in units of Kb.') + parser.add_argument('--ld-wind-snps', type=int, default=None, help='window size to be used for estimating LD-scores in units of SNPs.') + parser.add_argument('--chunk-size', type=int, default=50, help='chunk size for LD-scores calculation') + parser.add_argument('--keep', default=None, help='File with ids of individuals to use when computing LD-scores') + + #per-SNP h2 related parameters + parser.add_argument('--q', type=float, default=100, help='The maximum ratio between the largest and smallest truncated per-SNP heritabilites') + + #data input/output parameters + parser.add_argument('--sumstats', help='Input summary statistics file') + parser.add_argument('--ref-ld-chr', help='Suffix of LD-score files (as in ldsc)') + parser.add_argument('--w-ld-chr', help='Suffix of LD-score weights files (as in ldsc)') + parser.add_argument('--bfile-chr', default=None, help='Prefix of plink files (used to compute LD-scores)') + parser.add_argument('--ld-ukb', default=False, action='store_true', help='If specified, PolyFun will use UKB LD matrices to compute LD-scores') + parser.add_argument('--ld-dir', default=None, help='The path of a directory with UKB LD files (if not specified PolyFun will create a temporary directory)') + parser.add_argument('--output-prefix', required=True, help='Prefix of all PolyFun output file names') + parser.add_argument('--allow-missing', default=False, action='store_true', help='If specified, PolyFun will not terminate if some SNPs with sumstats are not found in the annotations files') + parser.add_argument('--num-chr', type=int, default=22, help='Number of chromosomes for your target organism (default is 22)') + + #LDSC parameters + parser.add_argument('--nnls-exact', default=False, action='store_true', help='If specified, S-LDSC will estimate non-negative taus using an exact instead of an approximate solver (this will be slower but slightly more accurate)') + + + #check package versions + check_package_versions() + + #show splash screen + splash_screen() + + #extract args + args = parser.parse_args() + + #check that the output directory exists + if len(os.path.dirname(args.output_prefix))>0 and not os.path.exists(os.path.dirname(args.output_prefix)): + raise ValueError('output directory %s doesn\'t exist'%(os.path.dirname(args.output_prefix))) + + + #configure logger + configure_logger(args.output_prefix) + + #check and fix args + args = check_args(args) + check_files(args) + + #create and run PolyFun object + polyfun_obj = PolyFun() + polyfun_obj.polyfun_main(args) + + print() + diff --git a/SCRIPTS/polyfun/polyfun.run.sh b/SCRIPTS/polyfun/polyfun.run.sh new file mode 100644 index 0000000..62f110a --- /dev/null +++ b/SCRIPTS/polyfun/polyfun.run.sh @@ -0,0 +1,214 @@ +#!/bin/bash + +# SLURM settings +##SBATCH --array=1-49 # create 23 jobs from this +#SBATCH --job-name=polyfun # Job name +#SBATCH --mail-type=FAIL # Mail events (NONE, BEGIN, END, FAIL, ALL) +#SBATCH --mail-user=e.j.a.smulders-2@umcutrecht.nl # Where to send mail +#SBATCH --nodes=1 # Run on one node +#SBATCH --ntasks=1 # Run on a single CPU +#SBATCH --mem=128G # Job memory request +#SBATCH --time=24:00:00 # Time limit hrs:min:sec +#SBATCH --output=polyfun.out # Standard output and error log + + +# SuSIE +# https://github.com/omerwe/polyfun +# make conda env +# CHR - chromosome +# BP - base pair position (in hg19 coordinates) +# A1 - The effect allele (i.e., the sign of the effect size is with respect to A1) +# A2 - the second allele + +# A PolyFun workflow + +# change env location accordingly +source /hpc/local/Rocky8/dhl_ec/software/mambaforge3/bin/activate polyfun + +#ROOT="/Users/esmulde2" + + +python3_exe="/hpc/local/Rocky8/dhl_ec/software/mambaforge3/envs/polyfun/bin/python3" + +DATA="" +REF="" +#DATA="/Volumes/MacExtern/UMC/Werk/GENIUS-CHD" +# DATA="/Volumes/MacExtern/UMC/Werk/cIMT/polyfun" +#REF="/Volumes/MacExtern/UMC/Werk/references" +# Define PHENOTYPES and their respective max sample sizes +PHENOTYPE="" +SAMPLESIZE= +# SAMPLESIZE=(46018 45589) # Corresponding sample sizes + +# Default values for parameters +default_min_maf=0.005 +default_max_num_causal=1 +default_pip_cutoff=0.50 +default_phenotype_file="" + +# Function to display usage information +usage() { + echo "Usage: $0 [Options]" 1>&2 + echo "Options:" 1>&2 + echo " -p : Specify the PHENOTYPE file (default: $default_phenotype_file)." 1>&2 + echo " -d : Specify the map for data (default: $DATA)." 1>&2 + echo " -m : Specify the minimum MAF (default: $default_min_maf)." 1>&2 + echo " -x : Specify the maximum number of causal SNPs (default: $default_max_num_causal)." 1>&2 + echo " -c : Specify the PIP cutoff (default: $default_pip_cutoff)." 1>&2 + exit 1 +} + +# Parsing command-line options +while getopts ":p:s:d:j:l:m:x:c:" opt; do + case ${opt} in + p) PHENOTYPE=$OPTARG ;; + s) SAMPLESIZE=$OPTARG ;; + d) DATA=$OPTARG ;; + j) FILE=$OPTARG ;; + l) REF=$OPTARG ;; + m) min_maf=$OPTARG ;; + x) max_num_causal=$OPTARG ;; + c) pip_cutoff=$OPTARG ;; + :) echo "Option -$OPTARG requires an argument." >&2; exit 1 ;; + \?) echo "Invalid option: -$OPTARG" >&2; exit 1 ;; + esac +done +#cd /hpc/dhl_ec/esmulders/polyfun + +POLYFUN="/hpc/dhl_ec/esmulders/MetaGWASToolKit/SCRIPTS/polyfun" +cd ${POLYFUN} + + +# Use default values if not set +min_maf=${min_maf:-$default_min_maf} +max_num_causal=${max_num_causal:-$default_max_num_causal} +pip_cutoff=${pip_cutoff:-$default_pip_cutoff} +PHENOTYPE_FILE=${PHENOTYPE_FILE:-$default_phenotype_file} + +mkdir -p ${DATA}/polyfun +mkdir -p ${DATA}/polyfun/data +mkdir -p ${DATA}/polyfun/finemapping +mkdir -p ${DATA}/polyfun/annotations + + +# Step 1: Create header +echo "CHR BP A1 A2 BETA SE MAF P RSID N" > ${DATA}/input/${PHENOTYPE}.qc.polyfun.txt + +# # Step 2: Process data +# gzip -dc ${FILE} | awk ' +# NR == 1 {next} +# { +# if ($1 == "X") +# $1 = 23 +# else if ($1 == "Y") +# $1 = 24 +# else if ($1 == "MT") +# $1 = 25 +# else if ($1 ~ /^[0-9]+$/) +# $1 = $1 +# else +# next +# if ($1 ~ /^[0-9]+$/) +# print $1, $2, $3, $4, $5, $6, $7, $8, $9, $11 +# }' >> ${DATA}/${PROJECTNAME}/${PHENOTYPE}/input/${PHENOTYPE}.qc.polyfun.txt +# +# # Step 3: Append remaining data +# gzip -dc ${FILE} | awk 'NR > 1 { +# if ($1 == "X") +# $1 = 23 +# else if ($1 == "Y") +# $1 = 24 +# else if ($1 == "MT") +# $1 = 25 +# else if ($1 ~ /^[0-9]+$/) +# $1 = $1 +# else +# next +# if ($1 ~ /^[0-9]+$/) +# print $1, $2, $3, $4, $5, $6, $7, $8, $9, $11 +# }' >> ${DATA}/${PROJECTNAME}/${PHENOTYPE}/input/${PHENOTYPE}.qc.polyfun.txt + +# Step 2: Process data, handle chromosome integers, and remove duplicates +gzip -dc ${FILE} | awk ' +NR == 1 {next} +{ + if ($1 == "X") + $1 = 23 + else if ($1 == "Y") + $1 = 24 + else if ($1 == "MT") + $1 = 25 + else if ($1 ~ /^[0-9]+$/) + $1 = $1 + else + next + key = $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9 "\t" $11 + if (!seen[key]++) { + print $1, $2, $3, $4, $5, $6, $7, $8, $9, $11 + } +}' >> ${DATA}/input/${PHENOTYPE}.qc.polyfun.txt +# Step 5: Compress the final file +gzip -vf ${DATA}/input/${PHENOTYPE}.qc.polyfun.txt +# +# # 1. lead snps make parquet +$python3_exe ${POLYFUN}/munge_polyfun_sumstats.py --sumstats ${DATA}/input/${PHENOTYPE}.qc.polyfun.txt.gz --out ${DATA}/polyfun/data/${PHENOTYPE}.lead_snps.parquet --min-info 0 --min-maf ${min_maf} +# # +# # # 2. extract snpvar + $python3_exe ${POLYFUN}/extract_snpvar.py --sumstats ${DATA}/polyfun/data/${PHENOTYPE}.lead_snps.parquet --out ${DATA}/polyfun/data/${PHENOTYPE}.snps_with_var.gz --allow-missing +# # # # +# # # # 3. finemapper jobs +$python3_exe ${POLYFUN}/create_finemapper_jobs.py \ + --sumstats ${DATA}/polyfun/data/${PHENOTYPE}.snps_with_var.gz \ + --n ${SAMPLESIZE} \ + --method susie \ + --pvalue-cutoff 5e-8 \ + --max-num-causal ${max_num_causal} \ + --memory 128 \ + --verbose \ + --out-prefix ${DATA}/polyfun/finemapping/${PHENOTYPE} \ + --jobs-file ${DATA}/polyfun/finemapping/${PHENOTYPE}.polyfun_all_jobs.sh \ + --allow-missing +# # # # +# # # 4. Run finemapping if jobs file is not empty +if [ -s ${DATA}/polyfun/finemapping/${PHENOTYPE}.polyfun_all_jobs.sh ]; then + bash ${DATA}/polyfun/finemapping/${PHENOTYPE}.polyfun_all_jobs.sh +else + echo "No finemapping jobs to run. Skipping finemapping step." +fi +# # # + +# # # 5. Aggregate results if there are files in the finemap folder +finemap_folder=${DATA}/polyfun/finemapping +if [ -d "$finemap_folder" ] && [ "$(ls -A $finemap_folder)" ]; then + $python3_exe ${POLYFUN}/aggregate_finemapper_results.py \ + --out-prefix ${DATA}/polyfun/finemapping/${PHENOTYPE} \ + --sumstats ${DATA}/polyfun/data/${PHENOTYPE}.snps_with_var.gz \ + --out ${DATA}/polyfun/data/${PHENOTYPE}.polyfun_agg.txt.gz \ + --allow-missing-jobs +else + echo "No files found in the finemap folder. Skipping aggregation." +fi + +# # # # 6. Extract annotations for each significant locus +finemap_folder=${DATA}/polyfun/finemapping +if [ -d "$finemap_folder" ] && [ "$(ls -A $finemap_folder)" ]; then + for file in ${DATA}/polyfun/finemapping/*.gz; do + if [[ $file =~ ${PHENOTYPE}.chr([0-9]+).([0-9]+)_([0-9]+).gz ]]; then + chr=${BASH_REMATCH[1]} + start=${BASH_REMATCH[2]} + end=${BASH_REMATCH[3]} + annot_file=${REF}/baselineLF2.2.UKB/baselineLF2.2.UKB.${chr}.annot.parquet + output_file=${DATA}/polyfun/annotations/top_annot.${chr}.${start}_${end}.txt.gz + $python3_exe ${POLYFUN}/extract_annotations.py \ + --pips $file \ + --annot $annot_file \ + --pip-cutoff ${pip_cutoff} \ + --out $output_file + else + echo "Skipping file: $file (does not match pattern or no PIP > 0.5)." + fi + done +else + echo "No files found in the finemap folder. Skipping extracting annotations." +fi + diff --git a/SCRIPTS/polyfun/polyfun.yml b/SCRIPTS/polyfun/polyfun.yml new file mode 100644 index 0000000..ddb7e2b --- /dev/null +++ b/SCRIPTS/polyfun/polyfun.yml @@ -0,0 +1,30 @@ +name: polyfun +channels: + - conda-forge + - nodefaults +dependencies: + - tqdm + - r-wavethresh + - pyarrow>=3.0 + - python=3.8 + - scikit-learn + - r-lattice + - r-ckmeans.1d.dp + - r-stringi + - bitarray + - r-base + - r + - r-matrixstats + - networkx + - rpy2<3.5.7|>=3.5.9 + - scipy + - r-devtools + - numpy>=1.20 + - pandas + - r-expm + - pandas-plink + - pip + - r-susier==0.11.92 + - packaging + - pip: + - bgen==1.2.10 diff --git a/SCRIPTS/polyfun/polyfun.yml.lock b/SCRIPTS/polyfun/polyfun.yml.lock new file mode 100644 index 0000000..a9b5b83 --- /dev/null +++ b/SCRIPTS/polyfun/polyfun.yml.lock @@ -0,0 +1,353 @@ +# This file may be used to create an environment using: +# $ conda create --name --file +# platform: linux-64 +@EXPLICIT 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+import os +import sys +import logging +from tqdm import tqdm + + +SNP_COLUMNS = ['CHR', 'SNP', 'BP', 'A1', 'A2'] +LONG_RANGE_LD_REGIONS = [] +LONG_RANGE_LD_REGIONS.append({'chr':6, 'start':25500000, 'end':33500000}) +LONG_RANGE_LD_REGIONS.append({'chr':8, 'start':8000000, 'end':12000000}) +LONG_RANGE_LD_REGIONS.append({'chr':11, 'start':46000000, 'end':57000000}) +DEFAULT_REGIONS_FILE = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'ukb_regions.tsv.gz') + + + +class TqdmUpTo(tqdm): + """ + taken from: https://github.com/tqdm/tqdm/blob/master/examples/tqdm_wget.py + """ + + def update_to(self, b=1, bsize=1, tsize=None): + if tsize is not None: self.total = tsize + self.update(b * bsize - self.n) + + + +''' Logger class (for compatability with LDSC code)''' +class Logger(object): + def __init__(self): + pass + def log(self, msg): + logging.info(msg) + + +class TqdmHandler(logging.StreamHandler): + def __init__(self): + logging.StreamHandler.__init__(self) + + def emit(self, record): + msg = self.format(record) + tqdm.write(msg) + + +def check_package_versions(): + from pkg_resources import parse_version + if parse_version(pd.__version__) < parse_version('0.25.0'): + raise ValueError('your pandas version is too old --- please update pandas') + + try: + import pandas_plink + except (ImportError, ModuleNotFoundError): + raise ValueError('\n\nPlease install the python package pandas_plink (using either "pip install pandas-plink" or "conda install -c conda-forge pandas-plink")\n\n') + + +def set_snpid_index(df, copy=False, allow_duplicates=False, allow_swapped_indel_alleles=False): + if copy: + df = df.copy() + is_indel = (df['A1'].str.len()>1) | (df['A2'].str.len()>1) + alleles_are_alphabetical = df['A1'] < df['A2'] + if allow_swapped_indel_alleles: + df['A1_first'] = alleles_are_alphabetical + else: + df['A1_first'] = alleles_are_alphabetical | is_indel + df['A1s'] = df['A2'].copy() + df.loc[df['A1_first'], 'A1s'] = df.loc[df['A1_first'], 'A1'].copy() + df['A2s'] = df['A1'].copy() + df.loc[df['A1_first'], 'A2s'] = df.loc[df['A1_first'], 'A2'].copy() + df.index = df['CHR'].astype(int).astype(str) + '.' + df['BP'].astype(str) + '.' + df['A1s'] + '.' + df['A2s'] + df.index.name = 'snpid' + df.drop(columns=['A1_first', 'A1s', 'A2s'], inplace=True) + + #check for duplicate SNPs + if not allow_duplicates: + is_duplicate_snp = df.index.duplicated() + if np.any(is_duplicate_snp): + df_dup_snps = df.loc[is_duplicate_snp] + snp_colums = [c for c in ['SNP', 'CHR', 'BP', 'A1', 'A2'] if c in df.columns] + df_dup_snps = df_dup_snps.loc[~df_dup_snps.index.duplicated(), snp_colums] + error_msg = 'Duplicate SNPs were found in the input data:\n%s'%(df_dup_snps) + raise ValueError(error_msg) + return df + + +def configure_logger(out_prefix): + + logFormatter = logging.Formatter("[%(levelname)s] %(message)s") + logger = logging.getLogger() + logger.setLevel(logging.NOTSET) + + consoleHandler = TqdmHandler() + consoleHandler.setFormatter(logFormatter) + logger.addHandler(consoleHandler) + + fileHandler = logging.FileHandler(out_prefix+'.log') + fileHandler.setFormatter(logFormatter) + logger.addHandler(fileHandler) + + + +def get_file_name(args, file_type, chr_num, verify_exists=True, allow_multiple=False): + if file_type == 'ldscores': + file_name = args.output_prefix + '.%d.l2.ldscore.parquet'%(chr_num) + elif file_type == 'snpvar_ridge': + file_name = args.output_prefix + '.%d.snpvar_ridge.gz'%(chr_num) + elif file_type == 'taus_ridge': + file_name = args.output_prefix + '.annot_coeff_ridge.%d.txt'%(chr_num) + elif file_type == 'taus_nn': + file_name = args.output_prefix + '.annot_coeff_nn.%d.txt'%(chr_num) + elif file_type == 'snpvar_ridge_constrained': + file_name = args.output_prefix + '.%d.snpvar_ridge_constrained.gz'%(chr_num) + elif file_type == 'snpvar_constrained': + file_name = args.output_prefix + '.%d.snpvar_constrained.gz'%(chr_num) + elif file_type == 'snpvar': + file_name = args.output_prefix + '.%d.snpvar.gz'%(chr_num) + elif file_type == 'bins': + file_name = args.output_prefix + '.%d.bins.parquet'%(chr_num) + elif file_type == 'M': + file_name = args.output_prefix + '.%d.l2.M'%(chr_num) + + elif file_type == 'annot': + assert verify_exists + assert allow_multiple + file_name = [] + for ref_ld_chr in args.ref_ld_chr.split(','): + file_name_part = ref_ld_chr + '%d.annot.gz'%(chr_num) + if not os.path.exists(file_name_part): + file_name_part = ref_ld_chr + '%d.annot.parquet'%(chr_num) + file_name.append(file_name_part) + + elif file_type == 'ref-ld': + assert verify_exists + assert allow_multiple + file_name = [] + for ref_ld_chr in args.ref_ld_chr.split(','): + file_name_part = ref_ld_chr + '%d.l2.ldscore.gz'%(chr_num) + if not os.path.exists(file_name_part): + file_name_part = ref_ld_chr + '%d.l2.ldscore.parquet'%(chr_num) + file_name.append(file_name_part) + + elif file_type == 'w-ld': + assert verify_exists + file_name = args.w_ld_chr + '%d.l2.ldscore.gz'%(chr_num) + if not os.path.exists(file_name): + file_name = args.w_ld_chr + '%d.l2.ldscore.parquet'%(chr_num) + + + elif file_type == 'bim': + file_name = args.bfile_chr + '%d.bim'%(chr_num) + elif file_type == 'fam': + file_name = args.bfile_chr + '%d.fam'%(chr_num) + elif file_type == 'bed': + file_name = args.bfile_chr + '%d.bed'%(chr_num) + else: + raise ValueError('unknown file type') + + if verify_exists: + if allow_multiple: + for fname in file_name: + if not os.path.exists(fname): + raise IOError('%s file not found: %s'%(file_type, fname)) + else: + if not os.path.exists(file_name): + raise IOError('%s file not found: %s'%(file_type, file_name)) + + return file_name diff --git a/SCRIPTS/polyfun/polyloc.py b/SCRIPTS/polyfun/polyloc.py new file mode 100644 index 0000000..25df667 --- /dev/null +++ b/SCRIPTS/polyfun/polyloc.py @@ -0,0 +1,361 @@ +import numpy as np +import pandas as pd +import os +import sys +import time +import logging +import scipy.stats as stats +from polyfun import PolyFun +from polyfun_utils import configure_logger, set_snpid_index, get_file_name +from polyfun_utils import SNP_COLUMNS +from pyarrow import ArrowIOError +from pyarrow.lib import ArrowInvalid + + +def splash_screen(): + print('*********************************************************************') + print('* PolyLoc (POLYgenic LOCalization of complex trait heritability') + print('* Version 1.0.0') + print('* (C) 2019-2024 Omer Weissbrod') + print('*********************************************************************') + print() + + +def check_args(args): + + #verify that the requested computations are valid + mode_params = np.array([args.compute_partitions, args.compute_ldscores, args.compute_polyloc]) + if np.sum(mode_params)==0: + raise ValueError('must specify at least one of --compute-partitions, --compute-ldscores, --compute-polyloc') + if args.compute_partitions and args.compute_polyloc and not args.compute_ldscores: + raise ValueError('cannot use both --compute-partitions and --compute_polyloc without also specifying --compute-ldscores') + if args.chr is not None: + if args.compute_partitions or args.compute_polyloc: + raise ValueError('--chr can only be specified when using only --compute-ldscores') + if args.bfile_chr is not None: + if not args.compute_ldscores and not args.compute_partitions: + raise ValueError('--bfile-chr can only be specified when using --compute-partitions or --compute-ldscores') + if args.ld_ukb: + if not args.compute_ldscores and not args.compute_partitions: + raise ValueError('--ld-ukb can only be specified when using --compute-partitions or --compute-ldscores') + if args.compute_ldscores and args.compute_polyloc and not args.compute_partitions: + raise ValueError('cannot use both --compute-ldscores and --compute_polyloc without also specifying --compute-partitions') + + if args.posterior is not None and not args.compute_partitions: + raise ValueError('--posterior can only be specified together with --compute-partitions') + if args.sumstats is not None and not args.compute_polyloc: + raise ValueError('--sumstats can only be specified together with --compute-polyloc') + if args.ld_dir is not None and not args.ld_ukb: + raise ValueError('You cannot specify --ld-dir without also specifying --ld-ukb') + + + #verify partitioning parameters + if args.skip_Ckmedian and (args.num_bins is None or args.num_bins<=0): + raise ValueError('You must specify --num-bins when using --skip-Ckmedian') + + #partitionining-related parameters + if args.compute_partitions: + if args.bfile_chr is None: + #raise ValueError('You must specify --bfile-chr when you specify --compute-partitions') + logging.warning('You did not specify --bfile-chr with --compute-partitions. PolyLoc will only use SNPs provided in the posterior files') + if args.posterior is None: + raise ValueError('--posterior must be specified when using --compute-partitions') + + #verify LD-score related parameters + if args.compute_ldscores: + if not args.ld_ukb and args.bfile_chr is None: + raise ValueError('You must specify either --ld-ukb or --bfile-chr when using --compute-ldscores') + if not args.ld_ukb and (args.ld_wind_cm is None and args.ld_wind_kb is None and args.ld_wind_snps is None): + args.ld_wind_cm = 1.0 + logging.warning('no ld-wind argument specified. PolyLoc will use --ld-cm 1.0') + + if not args.compute_ldscores: + if not (args.ld_wind_cm is None and args.ld_wind_kb is None and args.ld_wind_snps is None): + raise ValueError('--ld-wind parameters can only be specified together with --compute-ldscores') + if args.keep is not None: + raise ValueError('--keep can only be specified together with --compute-ldscores') + if args.chr is not None: + raise ValueError('--chr can only be specified together with --compute-ldscores') + + if args.compute_polyloc: + if args.sumstats is None: + raise ValueError('--sumstats must be specified when using --compute-polyloc') + if args.w_ld_chr is None: + raise ValueError('--w-ld-chr must be specified when using --compute-polyloc') + + return args + +def check_files(args): + + if args.compute_partitions: + if not os.path.exists(args.posterior): + raise IOError('%s not found'%(args.posterior)) + + #check that required input files exist + if args.compute_ldscores or args.compute_partitions: + if args.chr is None: chr_range = range(1, args.num_chr+1) + else: chr_range = range(args.chr, args.chr+1) + + for chr_num in chr_range: + if args.bfile_chr is not None: + get_file_name(args, 'bim', chr_num, verify_exists=True) + if args.compute_ldscores and not args.ld_ukb: + if args.bfile_chr is None: + raise ValueError('--bfile-chr not provided') + get_file_name(args, 'fam', chr_num, verify_exists=True) + get_file_name(args, 'bed', chr_num, verify_exists=True) + if not args.compute_partitions: + get_file_name(args, 'bins', chr_num, verify_exists=True) + + if args.compute_polyloc: + for chr_num in range(1, args.num_chr+1): + get_file_name(args, 'w-ld', chr_num, verify_exists=True) + if not args.compute_partitions: + get_file_name(args, 'bins', chr_num, verify_exists=True) + + + + + +class PolyLoc(PolyFun): + def __init__(self): + pass + + def load_posterior_betas(self, args): + try: + df_posterior = pd.read_parquet(args.posterior) + except (ArrowIOError, ArrowInvalid): + df_posterior = pd.read_table(args.posterior, sep='\s+') + + #preprocess columns + df_posterior.columns = df_posterior.columns.str.upper() + + #make sure that all required columns are found + has_missing_col = False + for column in SNP_COLUMNS + ['BETA_MEAN', 'BETA_SD']: + if column not in df_posterior.columns: + logging.error('%s has a missing column: %s'%(args.posterior, column)) + has_missing_col = True + if has_missing_col: + raise ValueError('%s has missing columns'%(args.posterior)) + + df_posterior['SNPVAR'] = df_posterior['BETA_MEAN']**2 + df_posterior['BETA_SD']**2 + self.df_snpvar = df_posterior + + + def polyloc_partitions(self, args): + + self.load_posterior_betas(args) + self.partition_snps_to_bins(args, use_ridge=False) + self.df_bins = set_snpid_index(self.df_bins) + + #add another partition for all SNPs not in the posterior file + if args.bfile_chr is not None: + df_bim_list = [] + for chr_num in range(1, args.num_chr+1): + df_bim_chr = pd.read_table(args.bfile_chr+'%d.bim'%(chr_num), sep='\s+', names=['CHR', 'SNP', 'CM', 'BP', 'A1', 'A2'], header=None) + df_bim_list.append(df_bim_chr) + df_bim = pd.concat(df_bim_list, axis=0) + df_bim = set_snpid_index(df_bim) + + #make sure that all variants in the posterior file are also in the plink files + if np.any(~self.df_bins.index.isin(df_bim.index)): + raise ValueError('Found variants in posterior file that are not found in the plink files') + + #add a new bin for SNPs that are not found in the posterior file (if there are any) + if df_bim.shape[0] > self.df_bins.shape[0]: + new_snps = df_bim.index[~df_bim.index.isin(self.df_bins.index)] + df_bins_new = df_bim.loc[new_snps, SNP_COLUMNS].copy() + for colname in self.df_bins.drop(columns=SNP_COLUMNS).columns: + df_bins_new[colname] = False + new_colname = 'snpvar_bin%d'%(df_bins_new.shape[1] - len(SNP_COLUMNS)+1) + self.df_bins[new_colname] = False + df_bins_new[new_colname] = True + self.df_bins = pd.concat([self.df_bins, df_bins_new], axis=0) + + #save the bins to disk + self.save_bins_to_disk(args) + + #save the bin sizes to disk + df_binsize = pd.DataFrame(index=np.arange(1,self.df_bins.shape[1] - len(SNP_COLUMNS)+1)) + df_binsize.index.name='BIN' + df_binsize['BIN_SIZE'] = [self.df_bins[c].sum() for c in self.df_bins.drop(columns=SNP_COLUMNS).columns] #saves memory + df_binsize.to_csv(args.output_prefix+'.binsize', sep='\t', index=True) + + + def compute_per_bin_h2(self, prop_h2, prop_h2_jk, df_binsize): + + #compute required quantities + sum_prop_h2 = np.cumsum(prop_h2) + prop_h2_stderr = np.std(prop_h2_jk, axis=1, ddof=0) * np.sqrt(prop_h2_jk.shape[1]-1) + sum_prop_h2_jk = np.cumsum(prop_h2_jk, axis=0) + sum_prop_h2_stderr = np.std(sum_prop_h2_jk, axis=1, ddof=0) * np.sqrt(prop_h2_jk.shape[1]-1) + + #create df_bin_h2 + df_bin_h2 = df_binsize.copy() + df_bin_h2['SUM_BINSIZE'] = df_bin_h2['BIN_SIZE'].cumsum() + df_bin_h2['%H2'] = prop_h2 + df_bin_h2['%H2_STDERR'] = prop_h2_stderr + df_bin_h2['SUM_%H2'] = sum_prop_h2 + df_bin_h2['SUM_%H2_STDERR'] = sum_prop_h2_stderr + + return df_bin_h2 + + + def compute_Mp(self, p, cumsum_prop_h2, cumnum_binsize): + + if p==1: p=0.99999999 + assert np.all(np.any(cumsum_prop_h2 >= p, axis=0)) + num_jk = cumsum_prop_h2.shape[1] + + last_bin_index = np.argmax(cumsum_prop_h2 >= p, axis=0) + num_snps_bin1 = np.zeros(num_jk, dtype=np.int64) + h2_bin1 = np.zeros(num_jk) + num_snps_bin1[last_bin_index != 0] = cumnum_binsize[last_bin_index[last_bin_index != 0] - 1] + h2_bin1[last_bin_index != 0] = cumsum_prop_h2[last_bin_index[last_bin_index != 0] - 1, np.arange(num_jk)[last_bin_index != 0]] + + num_snps_bin2 = cumnum_binsize[last_bin_index] + h2_bin2 = cumsum_prop_h2[last_bin_index, np.arange(num_jk)] + slope = (num_snps_bin2-num_snps_bin1).astype(np.float64) / (h2_bin2-h2_bin1) + assert not np.any(np.isnan(slope)) + Mp = np.ceil(num_snps_bin1 + slope * (p - h2_bin1)).astype(np.int64) + + return Mp + + + def compute_Mp_df(self, prop_h2, prop_h2_jk, cumnum_binsize, outlier_coef=25): + + cumsum_prop_h2 = np.cumsum(prop_h2) + cumsum_prop_h2_jk = np.cumsum(prop_h2_jk, axis=0) + dicts_list = [] + for p in np.arange(1,101): + Mp = self.compute_Mp(p/100., np.reshape(cumsum_prop_h2, (cumsum_prop_h2.shape[0], 1)), cumnum_binsize)[0] + Mp_jk = self.compute_Mp(p/100., cumsum_prop_h2_jk, cumnum_binsize) + is_outlier_jk = np.abs(Mp_jk - np.median(Mp_jk)) > stats.iqr(Mp_jk) * outlier_coef + Mp_jk = Mp_jk[~is_outlier_jk] + Mp_stderr = np.std(Mp_jk, ddof=0) * np.sqrt(len(Mp_jk)-1) + dicts_list.append({'p':p, 'Mp':Mp, 'Mp_STDERR':Mp_stderr}) + df_Mp = pd.DataFrame(dicts_list) + return df_Mp + + def compute_polyloc(self, args): + + #run S-LDSC and compute taus + self.run_ldsc(args, use_ridge=False, nn=True, evenodd_split=False, keep_large=True, n_blocks=200) + hsqhat = self.hsqhat + jknife = hsqhat.jknife + taus = jknife.est[0, :hsqhat.n_annot] / hsqhat.Nbar + taus_jk = jknife.delete_values[:, :hsqhat.n_annot] / hsqhat.Nbar + + #load bin sizes + df_binsize = pd.read_table(args.output_prefix+'.binsize', sep='\t') + + #compute prop_h2 for the main analysis and for each jackknife block + prop_h2 = taus * df_binsize['BIN_SIZE'].values + prop_h2 /= prop_h2.sum() + prop_h2_jk = taus_jk.T * df_binsize['BIN_SIZE'].values[:, np.newaxis] + prop_h2_jk = prop_h2_jk / prop_h2_jk.sum(axis=0) + + #compuute per-bin h^2 and stderrs + df_bin_h2 = self.compute_per_bin_h2(prop_h2, prop_h2_jk, df_binsize) + + #compute df_Mp + cumnum_binsize = df_binsize['BIN_SIZE'].cumsum().values + df_Mp = self.compute_Mp_df(prop_h2, prop_h2_jk, cumnum_binsize) + + #write df_bin_h2 to output file + outfile = args.output_prefix+'.bin_h2' + df_bin_h2.to_csv(outfile, sep='\t', float_format='%0.5f', index=False) + logging.info('Wrote per-bin heritability to %s'%(outfile)) + + #write df_Mp to output file + outfile = args.output_prefix+'.Mp' + df_Mp.to_csv(outfile, sep='\t', float_format='%0.5f', index=False) + logging.info('Wrote Mp estimates to %s'%(outfile)) + + + + def polyloc_main(self, args): + + #compute snp variances using L2-regularized S-LDSC with an odd/even chromosome split + if args.compute_partitions: + self.polyloc_partitions(args) + + #compute LD-scores of SNP partitions + if args.compute_ldscores: + self.compute_ld_scores(args) + + #compute polygenic localization + if args.compute_polyloc: + self.compute_polyloc(args) + + + + + + + + + +if __name__ == '__main__': + + import argparse + parser = argparse.ArgumentParser() + + #partitioning-related parameters + parser.add_argument('--num-bins', type=int, default=None, help='Number of bins to partition SNPs into. If not specified, PolyLoc will automatically select this number based on a BIC criterion') + parser.add_argument('--skip-Ckmedian', default=False, action='store_true', help='If specified, use a regular K-means algorithm instead of the R Ckmeans.1d.dp package') + + #mode related parameters + parser.add_argument('--compute-partitions', default=False, action='store_true', help='If specified, PolyLoc will compute per-SNP h2 using L2-regularized S-LDSC') + parser.add_argument('--compute-ldscores', default=False, action='store_true', help='If specified, PolyLoc will compute LD-scores of SNP bins') + parser.add_argument('--compute-polyloc', default=False, action='store_true', help='If specified, PolyLoc will perform polygenic localization of SNP heritability') + + #ld-score related parameters + parser.add_argument('--chr', type=int, default=None, help='Chromosome number (only applicable when only specifying --ldscores). If not set, PolyLoc will compute LD-scores for all chromosomes') + #parser.add_argument('--npz-prefix', default=None, help='Prefix of npz files that encode LD matrices (used to compute LD-scores)') + parser.add_argument('--ld-wind-cm', type=float, default=None, help='window size to be used for estimating LD-scores in units of centiMorgans (cM).') + parser.add_argument('--ld-wind-kb', type=int, default=None, help='window size to be used for estimating LD-scores in units of Kb.') + parser.add_argument('--ld-wind-snps', type=int, default=None, help='window size to be used for estimating LD-scores in units of SNPs.') + parser.add_argument('--chunk-size', type=int, default=50, help='chunk size for LD-scores calculation') + parser.add_argument('--keep', default=None, help='File with ids of individuals to use when computing LD-scores') + + #data input/output parameters + parser.add_argument('--sumstats', help='Input summary statistics file') + parser.add_argument('--posterior', help='Input file with posterior means and variances of causal effect sizes') + parser.add_argument('--w-ld-chr', help='Suffix of LD-score weights files (as in ldsc)') + parser.add_argument('--bfile-chr', default=None, help='Prefix of plink files (used to compute LD-scores)') + parser.add_argument('--output-prefix', required=True, help='Prefix of all PolyLoc out file names') + parser.add_argument('--ld-ukb', default=False, action='store_true', help='If specified, PolyLoc will use UKB LD matrices to compute LD-scores') + parser.add_argument('--ld-dir', default=None, help='The path of a directory with UKB LD files (if not specified PolyLoc will create a temporary directory)') + parser.add_argument('--num-chr', type=int, default=22, help='Number of chromosomes for your target organism (default is 22)') + + + + #LDSC parameters + parser.add_argument('--nnls-exact', default=False, action='store_true', help='If specified, S-LDSC will estimate non-negative taus using an exact instead of an approximate solver (this will be slower but slightly more accurate)') + + #show splash screen + splash_screen() + + #extract args + args = parser.parse_args() + + #check that the output directory exists + if len(os.path.dirname(args.output_prefix))>0 and not os.path.exists(os.path.dirname(args.output_prefix)): + raise ValueError('output directory %s doesn\'t exist'%(os.path.dirname(args.output_prefix))) + + #configure logger + configure_logger(args.output_prefix) + + #check and fix args + args = check_args(args) + check_files(args) + args.anno = None + + #create and run PolyLoc object + polyloc_obj = PolyLoc() + polyloc_obj.polyloc_main(args) + + print() + diff --git a/SCRIPTS/polyfun/polypred.py b/SCRIPTS/polyfun/polypred.py new file mode 100644 index 0000000..f517bac --- /dev/null +++ b/SCRIPTS/polyfun/polypred.py @@ -0,0 +1,439 @@ +import pandas as pd +import numpy as np +import os +from tqdm import tqdm +import tempfile +import logging +from glob import glob +from pyarrow import ArrowIOError +from pyarrow.lib import ArrowInvalid +import time +import random +from pandas.api.types import is_numeric_dtype +from polyfun_utils import Logger, check_package_versions, set_snpid_index, configure_logger +from sklearn.linear_model import LinearRegression + +def splash_screen(): + print('*********************************************************************') + print('* PolyPred (POLYgenic risk PREDiction)') + print('* Version 1.0.0') + print('* (C) 2020-2024 Omer Weissbrod') + print('*********************************************************************') + print() + + + +def create_plink_range_file(df_betas, temp_dir, num_jk=200): + #make sure that the df_betas is ordered + is_chr_ordered = np.all(np.diff(df_betas['CHR']) >= 0) + is_bp_ordered = True + for chr_num, df_chr in df_betas.groupby('CHR'): + is_bp_ordered = is_bp_ordered & (np.all(np.diff(df_chr['BP']) >= 0)) + if not is_bp_ordered: break + if not is_bp_ordered or not is_chr_ordered: + df_betas.sort_values(['CHR', 'BP'], inplace=True) + + #bound num_jk by num_snps if we have very few SNPs + num_jk = np.minimum(num_jk, df_betas.shape[0]) + + #create df_ranges + ranges_file = os.path.join(temp_dir, 'ranges.txt') + ranges_list = [{'block':'block%d'%(block), 'lb':str(block), 'ub':str(block+1)} for block in range(1,num_jk+1)] + df_ranges = pd.DataFrame(ranges_list) + df_ranges = df_ranges[['block', 'lb', 'ub']] + df_ranges.to_csv(ranges_file, sep='\t', header=False, index=False) + + #create df_scores + scores_file = os.path.join(temp_dir, 'snp_scores.txt') + separators = np.floor(np.linspace(0, df_betas.shape[0], num_jk+1)).astype(int) + df_betas['score'] = 0 + is_in_range = np.zeros(df_betas.shape[0], dtype=bool) + for i in range(len(separators)-1): + is_in_range[separators[i] : separators[i+1]] = True + df_betas.loc[is_in_range, 'score'] = i+1.5 + is_in_range[:] = False + assert np.all(df_betas['score'] > 0) + + return ranges_file + + +def compute_prs_for_file(args, + plink_file, + df_betas, + temp_dir, + ranges_file=None, + keep_file=None + ): + + #read the bim file + plink_file_prefix = plink_file[:plink_file.rfind('.')] + df_bim = pd.read_csv(plink_file_prefix+'.bim', + header=None, + names=['CHR', 'SNP', 'CM', 'BP', 'A1', 'A2'], + delim_whitespace=True) + df_bim = set_snpid_index(df_bim) + + #keep only relevant SNPs + df_betas = df_betas.merge(df_bim[['SNP']], + left_index=True, + right_index=True, + suffixes=('_betas', '_bim') + ) + if df_betas.shape[0] == 0: + raise ValueError('No betas found for SNPs in plink file %s'%(plink_file_prefix)) + + #create temp files + betas_file = os.path.join(temp_dir, next(tempfile._get_candidate_names())) + outfile_prs_temp = os.path.join(temp_dir, next(tempfile._get_candidate_names())) + + #save the betas to a file + df_betas[['SNP_bim', 'A1', 'BETA']].to_csv(betas_file, header=False, index=False, sep='\t', float_format='%0.8e') + + #Run plink + plink_exe = args.plink_exe if (args.plink_exe is not None) else args.plink2_exe + plink_cmd = '%s --allow-no-sex --extract %s --out %s --memory %d --threads %d'%( + plink_exe, + betas_file, + outfile_prs_temp, + args.memory*1024, + args.threads + ) + if plink_file.endswith('.pgen'): + plink_cmd += ' --bpfile %s --score %s cols=scoresums'%(plink_file_prefix, betas_file) + elif plink_file.endswith('.bed'): + plink_cmd += ' --bfile %s --score %s sum'%(plink_file_prefix, betas_file) + else: + raise ValueError('neither --bed nor --pgen specified') + if ranges_file is not None: + scores_file = os.path.join(temp_dir, next(tempfile._get_candidate_names())) + df_betas[['SNP_bim', 'score']].drop_duplicates('SNP_bim').to_csv(scores_file, sep='\t', header=False, index=False) + plink_cmd += ' --q-score-range %s %s'%(ranges_file, scores_file) + if keep_file is not None: + plink_cmd += ' --keep %s'%(keep_file) + os.system(plink_cmd) + + #read results + if ranges_file is None: + if plink_file.endswith('.bed'): + df_prs = pd.read_csv(outfile_prs_temp+'.profile', delim_whitespace=True) + elif plink_file.endswith('.pgen'): + df_prs = pd.read_csv(outfile_prs_temp+'.sscore', delim_whitespace=True) + df_prs.rename(columns={'#IID':'IID', 'SCORE1_SUM':'SCORESUM'}, inplace=True) + df_prs['FID'] = df_prs['IID'] + else: + raise ValueError('neither --bed nor --pgen specified') + df_prs.set_index('IID', inplace=True, drop=True) + if np.any(df_prs.index.duplicated()): + raise ValueError('duplicated iids found in %s'%(plink_file_prefix)) + else: + df_prs = None + if plink_file.endswith('.bed'): jk_files = glob(outfile_prs_temp+'.*.profile') + elif plink_file.endswith('.pgen'): jk_files = glob(outfile_prs_temp+'.*.sscore') + else: raise ValueError('neither --bed nor --pgen specified') + for jk_file in jk_files: + df_jk = pd.read_csv(jk_file, delim_whitespace=True) + df_jk.rename(columns={'#IID':'IID', 'SCORE1_SUM':'SCORESUM'}, inplace=True) + df_jk['FID'] = df_jk['IID'] + df_jk.set_index('IID', inplace=True, drop=True) + if np.any(df_jk.index.duplicated()): + raise ValueError('duplicated iids found in %s'%(plink_file_prefix)) + jk_file_basename = os.path.basename(jk_file) + block_name = jk_file_basename.split('.')[-2] + block_num = int(block_name[5:]) + scoresum_colname = 'SCORESUM.jk%d'%(block_num) + df_jk.rename(columns={'SCORESUM':scoresum_colname}, inplace=True) + if df_prs is None: + df_prs = df_jk + df_prs['SCORESUM'] = 0 + else: + assert np.all(df_jk.index == df_prs.index) + df_prs[scoresum_colname] = df_jk[scoresum_colname] + df_prs['SCORESUM'] += df_jk[scoresum_colname] + + + if df_prs is None: + raise ValueError('The following plink command failed:\n%s'%(plink_cmd)) + return df_prs + + +def load_betas_files(betas_file, verbose=True): + + if verbose: + logging.info('Loading betas file %s...'%(betas_file)) + t0 = time.time() + try: + df_betas = pd.read_parquet(betas_file) + if len(df_betas.index.names) > 1: + df_betas.reset_index(inplace=True) + except (ArrowIOError, ArrowInvalid): + if betas_file.endswith('.parquet'): + raise IOError('corrupt parquet file: %s'%(betas_file)) + df_betas = pd.read_csv(betas_file, delim_whitespace=True) + if verbose: + logging.info('done in %0.2f seconds'%(time.time() - t0)) + + #rename columns if needed + df_betas.rename(columns={'sid':'SNP', 'nt1':'A1', 'nt2':'A2', 'BETA_MEAN':'BETA', 'ldpred_inf_beta':'BETA', 'chrom':'CHR', 'Chrom':'CHR', 'pos':'BP'}, inplace=True, errors='ignore') + if not is_numeric_dtype(df_betas['CHR']): + if df_betas['CHR'].str.startswith('chrom_').all(): + df_betas['CHR'] = df_betas['CHR'].str[6:].astype(np.int64) + else: + raise ValueError('unknown CHR format') + df_betas.rename(columns={'BETA_joint':'BETA', 'ALLELE1':'A1', 'ALLELE0':'A2', 'beta_mean':'BETA', 'MAF_BOLT':'A1Frq', 'Name':'SNP', 'A1Effect':'BETA', 'Name':'SNP', 'Chrom':'CHR', 'Position':'BP', 'beta':'BETA'}, inplace=True, errors='ignore') + + #create index + df_betas = set_snpid_index(df_betas) + + #subset SNPs according to extract file + if args.extract is not None: + df_extract = pd.read_csv(args.extract, header=None, squeeze=True) + df_betas = df_betas.loc[df_betas['SNP'].isin(df_extract)] + if df_betas.shape[0]==0: + raise ValueError('No SNPs remained after applying --extract') + if verbose: + logging.info('#SNPs after --extract: %s'%(df_betas.shape[0])) + + return df_betas + +def computs_prs_all_files(args, betas_file, disable_jackknife=False, keep_file=None): + + #Check that all input files are valid + found_pgen = False + found_bed = False + for file in args.files: + if file.endswith('.bed'): + if found_pgen: raise ValueError('Cannot combined .bed and .pgen files') + if args.plink_exe is None: raise ValueError('You must provide --plink-exe if you use .bed files') + found_bed = True + elif file.endswith('.pgen'): + if found_bed: raise ValueError('Cannot combined .bed and .pgen files') + if args.plink2_exe is None: raise ValueError('You must provide --plink2-exe if you use .pgen files') + found_pgen = True + else: + raise ValueError('File suffix must be either .bed or .pgen') + + #create a temp dir + temp_dir = tempfile.mkdtemp() + + #load df_betas + df_betas = load_betas_files(betas_file) + + #create range files for prs + if args.num_jk == 0 or disable_jackknife: + ranges_file = None + else: + ranges_file = create_plink_range_file(df_betas, temp_dir, num_jk=args.num_jk) + + #compute per-chromosome PRS + df_prs_sum = None + tqdm_files = tqdm(args.files) + for plink_file in tqdm_files: + if not os.path.exists(plink_file): + raise IOError('%s doesn\'t exist'%(plink_file)) + tqdm_files.set_description(plink_file) + df_prs_file = compute_prs_for_file(args, + plink_file, + df_betas, + temp_dir, + ranges_file=ranges_file, + keep_file=keep_file + ) + + + #add up the file-specific PRSs + assert df_prs_file.shape[0]>0 + if df_prs_sum is None: + df_prs_sum = df_prs_file + else: + assert np.all(df_prs_sum.index == df_prs_file.index) + for c in df_prs_file.columns: + if not c.startswith('SCORESUM'): continue + if not c in df_prs_sum.columns: df_prs_sum[c] = 0 + df_prs_sum[c] += df_prs_file[c] + + #compute jackknife-block PRS + if args.num_jk > 0 and not disable_jackknife: + jk_columns = df_prs_sum.columns[df_prs_sum.columns.str.startswith('SCORESUM.jk')] + assert np.allclose(df_prs_sum[jk_columns].sum(axis=1), df_prs_sum['SCORESUM'], atol=1e-3) + for c in jk_columns: + df_prs_sum[c] = df_prs_sum['SCORESUM'] - df_prs_sum[c] + + return df_prs_sum + + + +def estimate_mixing_weights(args): + + #read phenotypes + df_pheno = pd.read_csv(args.pheno, names=['FID', 'IID', 'PHENO'], index_col='IID', delim_whitespace=True) + + #make sure that we didn't include a header line + try: + float(df_pheno['PHENO'].iloc[0]) + except: + df_pheno = df_pheno.iloc[1:] + df_pheno['PHENO'] = df_pheno['PHENO'].astype(np.float64) + if np.any(df_pheno.index.duplicated()): + raise ValueError('duplicate ids found in %s'%(args.pheno)) + + #compute a PRS for each beta file + beta_files = args.betas.split(',') + df_prs_sum_list = [] + for betas_file in beta_files: + df_prs_sum = computs_prs_all_files(args, betas_file, disable_jackknife=True, keep_file=args.pheno) + df_prs_sum_list.append(df_prs_sum[['SCORESUM']]) + for df_prs_sum in df_prs_sum_list: + assert np.all(df_prs_sum.index == df_prs_sum_list[0].index) + df_prs_sum_all = pd.concat(df_prs_sum_list, axis=1) + + #sync df_pheno and df_prs_sum_all + df_prs_sum_all.index = df_prs_sum_all.index.astype(str) + df_pheno.index = df_pheno.index.astype(str) + index_shared = df_prs_sum_all.index.intersection(df_pheno.index) + assert len(index_shared)>0 + if len(index_shared) < df_prs_sum_all.shape[0]: + df_prs_sum_all = df_prs_sum_all.loc[index_shared] + if df_pheno.shape[0] != df_prs_sum_all.shape[0] or np.any(df_prs_sum_all.index != df_pheno.index): + df_pheno = df_pheno.loc[df_prs_sum_all.index] + + #flip PRS that are negatively correlated with the phenotype + is_flipped = np.zeros(df_prs_sum_all.shape[1], dtype=bool) + linreg_univariate = LinearRegression() + for c_i in range(df_prs_sum_all.shape[1]): + linreg_univariate.fit(df_prs_sum_all.iloc[:, [c_i]], df_pheno['PHENO']) + is_flipped[c_i] = linreg_univariate.coef_[0] < 0 + df_prs_sum_all.loc[:, is_flipped] *= -1 + + #compute mixing weights + linreg = LinearRegression(positive = not args.allow_neg_mixweights) + linreg.fit(df_prs_sum_all, df_pheno['PHENO']) + mix_weights, intercept = linreg.coef_, linreg.intercept_ + + #create and print df_coef, and save it to disk + df_coef = pd.Series(mix_weights, index=beta_files) + df_coef.loc['intercept'] = intercept + mix_weights_file = args.output_prefix+'.mixweights' + df_coef.to_frame(name='mix_weight').to_csv(mix_weights_file, sep='\t') + logging.info('Writing mixing weights to %s'%(mix_weights_file)) + + #compute weighted betas + df_betas_weighted = None + for is_flipped_beta, betas_file, mix_weight in zip(is_flipped, beta_files, mix_weights): + df_betas = load_betas_files(betas_file) + df_betas = df_betas[['SNP', 'CHR', 'BP', 'A1', 'A2', 'BETA']] + df_betas['BETA'] *= mix_weight + if is_flipped_beta: df_betas['BETA'] = -df_betas['BETA'] + if df_betas_weighted is None: + df_betas_weighted = df_betas + continue + + index_shared = df_betas.index.intersection(df_betas_weighted.index) + df_betas['BETA2'] = df_betas['BETA'] + df_new = df_betas_weighted.loc[index_shared].merge(df_betas.loc[index_shared, ['BETA2']], left_index=True, right_index=True) + df_new['BETA'] += df_new['BETA2'] + del df_new['BETA2'] + del df_betas['BETA2'] + df_list = [df_new, df_betas.loc[~df_betas.index.isin(index_shared)], df_betas_weighted.loc[~df_betas_weighted.index.isin(index_shared)]] + df_betas_weighted = pd.concat(df_list, axis=0) + df_betas_weighted.sort_values(['CHR', 'BP', 'A1'], inplace=True) + + #save output to file + df_betas_weighted[['SNP', 'CHR', 'BP', 'A1', 'A2', 'BETA']].to_csv(args.output_prefix+'.betas', sep='\t', index=False, float_format='%0.6e') + logging.info('Saving weighted betas to %s'%(args.output_prefix+'.betas')) + + +def compute_prs(args): + + if args.betas.count(',') > 0: + raise ValueError('--predict can only be used with a single betas file') + df_prs_sum = computs_prs_all_files(args, args.betas, disable_jackknife=False, keep_file=args.keep) + df_prs_sum.reset_index(inplace=True, drop=False) + df_prs_sum.columns = df_prs_sum.columns.str.replace('SCORESUM', 'PRS') + df_prs_sum_main = df_prs_sum[['FID', 'IID', 'PRS']] + df_prs_sum_jk = df_prs_sum[['FID', 'IID'] + [c for c in df_prs_sum.columns if c.startswith('PRS.')]] + + df_prs_sum_main.to_csv(args.output_prefix+'.prs', sep='\t', index=False, float_format='%0.5f') + if df_prs_sum_jk.shape[1]>1: + df_prs_sum_jk.to_csv(args.output_prefix+'.prs_jk', sep='\t', index=False, float_format='%0.5f') + logging.info('Saving PRS to %s'%(args.output_prefix+'.prs')) + + +def check_args(args): + if int(args.predict) + int(args.combine_betas) != 1: + raise ValueError('you must specify either --predict or --combine-betas (but not both)') + if args.plink_exe is None and args.plink2_exe is None: + raise ValueError('you must specify either --plink-exe or --plink2-exe') + if args.plink_exe is not None and not os.path.exists(args.plink_exe): + raise ValueError('%s not found'%(args.plink_exe)) + if args.plink2_exe is not None and not os.path.exists(args.plink2_exe): + raise ValueError('%s not found'%(args.plink2_exe)) + if args.combine_betas: + if args.keep is not None: + raise ValueError('you cannot provide both --combine-betas and --keep') + if args.pheno is None: + raise ValueError('you must provide --pheno if you specify --combine-betas') + if args.betas.count(',')==0: + raise ValueError('you must provide multiple files in --betas if you specify --combine-betas') + if args.num_jk<0: + raise ValueError('--num-jk must be >=0') + if args.pheno is not None and args.predict: + raise ValueError('--pheno can only be used with --combine-betas') + + if len(list(args.files)) == 0: + raise ValueError('no input files specified') + +if __name__ == '__main__': + + import argparse + parser = argparse.ArgumentParser() + + parser.add_argument('--betas', required=True, help='files with SNP effect sizes (comma separated). A1 is the effect allele.') + parser.add_argument('--output-prefix', required=True, help='Prefix of output file') + + parser.add_argument('--combine-betas', default=False, action='store_true', help='If specified, PolyPred will estimate mixing weights') + parser.add_argument('--allow-neg-mixweights', default=False, action='store_true', help='If specified, PolyPred will not enforce non-negative mixing weights') + parser.add_argument('--predict', default=False, action='store_true', help='If specified, PolyPred will compute PRS') + parser.add_argument('--pheno', default=None, help='Phenotype file (required for estimating mixing weights)') + parser.add_argument('--plink-exe', default=None, help='Plink 1.9 executable (required for .bed files)') + parser.add_argument('--plink2-exe', default=None, help='Plink 2 executable (required for .pgen files)') + + parser.add_argument('--extract', default=None, help='A text file with rsids of SNPs to use (one per line)') + parser.add_argument('--keep', default=None, help='A text file with ids of individuals to use (two columns per line, each containing FID,IID)') + parser.add_argument('--num-jk', type=int, default=200, help='number of genomic jackknife blocks') + + parser.add_argument('--memory', type=int, default=2, help='Maximum memory usage (in GB)') + parser.add_argument('--threads', type=int, default=1, help='Number of CPU threads') + + parser.add_argument('files', nargs='*', help='plink files (either bed files or .pgen files)') + + #check package versions + check_package_versions() + + #show splash screen + splash_screen() + + #extract args + args = parser.parse_args() + + #check that the output directory exists + if len(os.path.dirname(args.output_prefix))>0 and not os.path.exists(os.path.dirname(args.output_prefix)): + raise ValueError('output directory %s doesn\'t exist'%(os.path.dirname(args.output_prefix))) + + #configure logger + configure_logger(args.output_prefix) + + #check arguments + check_args(args) + + #estimate mixing weights if needed + if args.combine_betas: + estimate_mixing_weights(args) + + #compute PRS if needed + if args.predict: + compute_prs(args) + + print() + + + diff --git a/SCRIPTS/polyfun/polypred_example/1000G.pheno.txt b/SCRIPTS/polyfun/polypred_example/1000G.pheno.txt new file mode 100644 index 0000000..b29cd6b --- /dev/null +++ b/SCRIPTS/polyfun/polypred_example/1000G.pheno.txt @@ -0,0 +1,490 @@ +FID IID PHENO +HG00096 HG00096 0.73956 +HG00097 HG00097 -0.63683 +HG00099 HG00099 0.57049 +HG00101 HG00101 -0.01226 +HG00102 HG00102 -0.76630 +HG00103 HG00103 -0.41756 +HG00105 HG00105 -0.73158 +HG00107 HG00107 -1.22242 +HG00108 HG00108 0.48795 +HG00109 HG00109 -0.70116 +HG00110 HG00110 -0.38185 +HG00111 HG00111 -0.63202 +HG00112 HG00112 -0.18958 +HG00113 HG00113 -0.28799 +HG00114 HG00114 -0.27053 +HG00115 HG00115 -0.20632 +HG00118 HG00118 -0.77617 +HG00119 HG00119 -0.55645 +HG00120 HG00120 0.45152 +HG00121 HG00121 0.01265 +HG00122 HG00122 0.55529 +HG00125 HG00125 -0.08535 +HG00126 HG00126 -0.39932 +HG00127 HG00127 -0.46385 +HG00128 HG00128 -0.52281 +HG00129 HG00129 0.60466 +HG00130 HG00130 0.20642 +HG00131 HG00131 0.10421 +HG00132 HG00132 -0.17955 +HG00133 HG00133 -0.78863 +HG00136 HG00136 0.08761 +HG00137 HG00137 0.13778 +HG00138 HG00138 -0.19234 +HG00139 HG00139 0.11423 +HG00140 HG00140 0.44847 +HG00141 HG00141 -0.67293 +HG00142 HG00142 -0.35436 +HG00143 HG00143 0.54845 +HG00145 HG00145 -0.17293 +HG00146 HG00146 -0.43639 +HG00148 HG00148 -0.20047 +HG00149 HG00149 -0.57832 +HG00150 HG00150 -0.84597 +HG00151 HG00151 -0.02364 +HG00154 HG00154 -0.54582 +HG00155 HG00155 -0.47041 +HG00157 HG00157 -0.43862 +HG00158 HG00158 -0.35232 +HG00159 HG00159 0.05204 +HG00160 HG00160 -0.41527 +HG00171 HG00171 -0.03907 +HG00173 HG00173 -0.31381 +HG00174 HG00174 0.20394 +HG00176 HG00176 0.19848 +HG00177 HG00177 -0.30759 +HG00178 HG00178 -0.38543 +HG00179 HG00179 0.44939 +HG00180 HG00180 -0.42793 +HG00181 HG00181 -0.48603 +HG00182 HG00182 -0.80369 +HG00183 HG00183 -0.81979 +HG00185 HG00185 0.06296 +HG00186 HG00186 0.49736 +HG00187 HG00187 0.14828 +HG00188 HG00188 -0.79714 +HG00189 HG00189 -0.59256 +HG00190 HG00190 0.34053 +HG00231 HG00231 -0.30646 +HG00232 HG00232 0.04220 +HG00233 HG00233 -0.37105 +HG00234 HG00234 -1.11476 +HG00235 HG00235 -0.15246 +HG00236 HG00236 0.70636 +HG00237 HG00237 -0.75188 +HG00238 HG00238 -0.18969 +HG00239 HG00239 -0.37072 +HG00242 HG00242 0.48414 +HG00243 HG00243 -0.37290 +HG00244 HG00244 -0.07133 +HG00245 HG00245 -0.95865 +HG00246 HG00246 -0.07026 +HG00250 HG00250 0.49498 +HG00251 HG00251 -0.04529 +HG00252 HG00252 0.12803 +HG00253 HG00253 -0.12504 +HG00254 HG00254 -0.55065 +HG00255 HG00255 -0.01281 +HG00256 HG00256 -0.77743 +HG00257 HG00257 -0.00388 +HG00258 HG00258 0.28217 +HG00259 HG00259 -0.35508 +HG00260 HG00260 0.35672 +HG00261 HG00261 -0.29455 +HG00262 HG00262 -0.68718 +HG00263 HG00263 -0.47504 +HG00264 HG00264 0.23365 +HG00265 HG00265 -0.64251 +HG00266 HG00266 -0.55016 +HG00267 HG00267 0.68200 +HG00268 HG00268 0.04794 +HG00269 HG00269 -0.13743 +HG00271 HG00271 -0.49921 +HG00272 HG00272 -0.18736 +HG00273 HG00273 0.06628 +HG00274 HG00274 -0.32641 +HG00275 HG00275 -0.65187 +HG00276 HG00276 -0.18582 +HG00277 HG00277 -0.40507 +HG00278 HG00278 0.08915 +HG00280 HG00280 -0.55485 +HG00281 HG00281 0.90753 +HG00282 HG00282 -0.77484 +HG00284 HG00284 -0.73017 +HG00285 HG00285 -0.07022 +HG00288 HG00288 -0.23622 +HG00290 HG00290 -0.14111 +HG00304 HG00304 0.38507 +HG00306 HG00306 -0.69308 +HG00308 HG00308 -0.07108 +HG00309 HG00309 -0.19712 +HG00310 HG00310 -0.20764 +HG00311 HG00311 -0.24588 +HG00313 HG00313 -0.41085 +HG00315 HG00315 0.31280 +HG00318 HG00318 0.23045 +HG00319 HG00319 -0.64949 +HG00320 HG00320 -0.88948 +HG00321 HG00321 -0.31404 +HG00323 HG00323 0.17394 +HG00324 HG00324 1.18289 +HG00325 HG00325 -0.16772 +HG00326 HG00326 0.07434 +HG00327 HG00327 0.34599 +HG00328 HG00328 -0.69993 +HG00329 HG00329 -0.69057 +HG00330 HG00330 -0.56812 +HG00331 HG00331 -0.69880 +HG00332 HG00332 0.13901 +HG00334 HG00334 -0.46679 +HG00335 HG00335 0.93259 +HG00336 HG00336 -1.10044 +HG00337 HG00337 -0.65299 +HG00338 HG00338 -0.15787 +HG00339 HG00339 0.41931 +HG00341 HG00341 -0.10396 +HG00342 HG00342 -0.20522 +HG00343 HG00343 -0.28042 +HG00344 HG00344 0.25047 +HG00345 HG00345 0.30109 +HG00346 HG00346 0.46037 +HG00349 HG00349 -0.03545 +HG00350 HG00350 -0.35190 +HG00351 HG00351 -0.47584 +HG00353 HG00353 0.22625 +HG00355 HG00355 -0.09603 +HG00356 HG00356 0.21419 +HG00357 HG00357 0.65739 +HG00358 HG00358 -0.47361 +HG00360 HG00360 -0.58188 +HG00361 HG00361 1.74684 +HG00362 HG00362 -0.13361 +HG00364 HG00364 -0.21411 +HG00365 HG00365 -0.35968 +HG00366 HG00366 -0.43481 +HG00367 HG00367 -0.15641 +HG00368 HG00368 -0.42879 +HG00369 HG00369 -0.75370 +HG00371 HG00371 -0.67004 +HG00372 HG00372 0.30939 +HG00373 HG00373 -0.73730 +HG00375 HG00375 0.29142 +HG00376 HG00376 0.14931 +HG00378 HG00378 0.12994 +HG00379 HG00379 0.09515 +HG00380 HG00380 -0.63028 +HG00381 HG00381 0.32418 +HG00382 HG00382 0.08240 +HG00383 HG00383 -0.27378 +HG00384 HG00384 -0.08469 +HG01334 HG01334 -0.21949 +HG01500 HG01500 -0.20280 +HG01501 HG01501 -0.32098 +HG01503 HG01503 0.05030 +HG01504 HG01504 0.00878 +HG01506 HG01506 0.04889 +HG01507 HG01507 -0.47852 +HG01509 HG01509 -0.33846 +HG01510 HG01510 -0.52957 +HG01512 HG01512 0.12145 +HG01513 HG01513 -0.36264 +HG01515 HG01515 0.25943 +HG01516 HG01516 -0.11546 +HG01518 HG01518 -1.10860 +HG01519 HG01519 -0.65755 +HG01521 HG01521 -0.60334 +HG01522 HG01522 -0.47614 +HG01524 HG01524 0.89222 +HG01525 HG01525 -0.14787 +HG01527 HG01527 -0.07181 +HG01528 HG01528 -0.06001 +HG01530 HG01530 0.19375 +HG01531 HG01531 -0.49815 +HG01536 HG01536 -0.07518 +HG01537 HG01537 -0.14476 +HG01602 HG01602 -0.15753 +HG01603 HG01603 0.12036 +HG01605 HG01605 0.04650 +HG01606 HG01606 -0.51138 +HG01607 HG01607 0.64130 +HG01608 HG01608 0.78675 +HG01610 HG01610 -0.64456 +HG01612 HG01612 -0.09747 +HG01613 HG01613 -0.54240 +HG01615 HG01615 0.18162 +HG01617 HG01617 -0.07963 +HG01618 HG01618 -0.39907 +HG01619 HG01619 -0.06120 +HG01620 HG01620 0.58732 +HG01623 HG01623 -0.12214 +HG01624 HG01624 -0.09805 +HG01625 HG01625 -0.51336 +HG01626 HG01626 -0.10670 +HG01628 HG01628 -0.64331 +HG01630 HG01630 -0.37201 +HG01631 HG01631 -0.49564 +HG01632 HG01632 -0.28327 +HG01668 HG01668 0.69339 +HG01669 HG01669 -0.52623 +HG01670 HG01670 -0.02273 +HG01672 HG01672 0.19882 +HG01673 HG01673 0.50754 +HG01675 HG01675 0.46832 +HG01676 HG01676 -0.05920 +HG01678 HG01678 -0.24102 +HG01679 HG01679 0.07104 +HG01680 HG01680 -0.64185 +HG01682 HG01682 -0.07133 +HG01684 HG01684 -0.84626 +HG01685 HG01685 -0.27160 +HG01686 HG01686 -0.13085 +HG01694 HG01694 -0.15248 +HG01695 HG01695 -0.15510 +HG01697 HG01697 -0.31139 +HG01699 HG01699 -0.01570 +HG01700 HG01700 -0.49375 +HG01702 HG01702 -0.15311 +HG01704 HG01704 0.11730 +HG01705 HG01705 0.09714 +HG01707 HG01707 -1.18416 +HG01708 HG01708 -0.21162 +HG01709 HG01709 -0.73792 +HG01710 HG01710 -0.14930 +HG01746 HG01746 0.43217 +HG01747 HG01747 0.30977 +HG01756 HG01756 -0.30295 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rs549952689 40.80679 20124439 A G +1 rs79543857 40.806798 20124520 G A +1 rs12401541 40.806817 20124716 T C +1 rs10917543 40.806859 20125116 C T +1 rs10917544 40.806899 20125494 C G +1 rs367579458 40.806984 20126289 C G +1 rs6692164 40.807012 20126561 C G +1 rs12117039 40.807066 20127066 C T +1 rs12144446 40.807102 20127407 T C +1 rs150184497 40.807142 20127784 G C +1 rs74057039 40.807166 20128011 T G +1 rs12117166 40.807177 20128115 G A +1 rs10917545 40.807184 20128177 G A +1 rs4655208 40.807222 20128536 C T +1 rs7546098 40.807265 20128945 G C +1 rs148212668 40.807306 20129258 A C +1 rs6701368 40.80731 20129289 C T +1 rs55825481 40.807396 20129940 G A +1 rs75612545 40.807429 20130189 C T +1 rs12119457 40.807492 20130667 T A +1 rs2314757 40.807637 20131771 T C +1 rs12562996 40.807663 20131972 G A +1 rs7555890 40.807683 20132122 G C +1 rs7518205 40.80772 20132355 C T +1 rs6658317 40.807826 20132953 T C +1 rs6697394 40.807835 20133001 C G +1 rs6697496 40.807853 20133106 A G +1 rs28688001 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︨ 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NA20828 0 0 0 -9 +NA20832 NA20832 0 0 0 -9 diff --git a/SCRIPTS/polyfun/polypred_example/bolt.betas.gz b/SCRIPTS/polyfun/polypred_example/bolt.betas.gz new file mode 100644 index 0000000..fb9e38f Binary files /dev/null and b/SCRIPTS/polyfun/polypred_example/bolt.betas.gz differ diff --git a/SCRIPTS/polyfun/polypred_example/bolt.sumstats.txt.gz b/SCRIPTS/polyfun/polypred_example/bolt.sumstats.txt.gz new file mode 100644 index 0000000..b510368 Binary files /dev/null and b/SCRIPTS/polyfun/polypred_example/bolt.sumstats.txt.gz differ diff --git a/SCRIPTS/polyfun/snpvar_meta.chr1_7.parquet b/SCRIPTS/polyfun/snpvar_meta.chr1_7.parquet new file mode 100644 index 0000000..771d85e Binary files /dev/null and b/SCRIPTS/polyfun/snpvar_meta.chr1_7.parquet differ diff --git a/SCRIPTS/polyfun/snpvar_meta.chr8_22.parquet b/SCRIPTS/polyfun/snpvar_meta.chr8_22.parquet new file mode 100644 index 0000000..a21e108 Binary files /dev/null and b/SCRIPTS/polyfun/snpvar_meta.chr8_22.parquet differ diff --git a/SCRIPTS/polyfun/test_polyfun.py b/SCRIPTS/polyfun/test_polyfun.py new file mode 100644 index 0000000..d1ceaeb --- /dev/null +++ b/SCRIPTS/polyfun/test_polyfun.py @@ -0,0 +1,321 @@ +import numpy as np; np.set_printoptions(precision=3, linewidth=200) +import pandas as pd; pd.set_option('display.width', 200) +import sys +import tempfile +import subprocess +import os +import glob +from pandas.api.types import is_string_dtype +from pandas.api.types import is_numeric_dtype + + +def compare_dfs(dir1, dir2, filename, sort_column=None): + file1 = os.path.join(dir1, filename) + file2 = os.path.join(dir2, filename) + if not os.path.exists(file1): + raise IOError('%s not found'%(file1)) + if not os.path.exists(file2): + raise IOError('%s not found'%(file2)) + if file1.endswith('.parquet'): df1 = pd.read_parquet(file1) + else: df1 = pd.read_table(file1, sep='\s+') + if file2.endswith('.parquet'): df2 = pd.read_parquet(file2) + else: df2 = pd.read_table(file2, sep='\s+') + assert np.all(df1.shape == df2.shape), 'found dimension mismatch between %s and %s'%(file1, file2) + assert np.all(df1.columns == df2.columns), 'found mismatch between %s and %s'%(file1, file2) + if sort_column is not None: + df1 = df1.sort_values(sort_column).reset_index(drop=True) + df2 = df2.sort_values(sort_column).reset_index(drop=True) + for c in df1.columns: + if c=='CREDIBLE_SET': + continue + if is_numeric_dtype(df1[c]): + if c=='Mp_STDERR': atol=1e-1 + elif c=='BETA_MEAN': atol=1e-3 + elif c=='BETA_SD': atol=1e-3 + elif c=='PIP': atol=1e-2 + else: atol=1e-4 + if c=='PIP': + assert np.corrcoef(df1[c], df2[c])[0,1]>0.97, 'found major mismatch between %s and %s in column %s'%(file1, file2, c) + pip_cutoff = 0.8 + is_large_pip = df2[c] > pip_cutoff + assert np.allclose(df1.loc[is_large_pip,c], df2.loc[is_large_pip,c], atol=atol), 'found mismatch between %s and %s in column %s'%(file1, file2, c) + else: + assert np.allclose(df1[c], df2[c], atol=atol), 'found mismatch between %s and %s in column %s'%(file1, file2, c) + else: + assert np.all(df1[c] == df2[c]), 'found mismatch between %s and %s in column %s'%(file1, file2, c) + + + +def is_susie_installed(): + try: + import rpy2 + import rpy2.robjects.numpy2ri as numpy2ri + import rpy2.robjects as ro + ro.conversion.py2ri = numpy2ri + numpy2ri.activate() + from rpy2.robjects.packages import importr + susieR = importr('susieR') + except: + return False + return True + + +def is_Ckmeans_installed(): + try: + import rpy2 + import rpy2.robjects.numpy2ri as numpy2ri + try: + from importlib import reload + reload(rpy2.robjects.numpy2ri) + except: + pass + import rpy2.robjects as ro + ro.conversion.py2ri = numpy2ri + numpy2ri.activate() + from rpy2.robjects.packages import importr + importr('Ckmeans.1d.dp') + median_seg_func = ro.r('Ckmedian.1d.dp') + mean_seg_func = ro.r('Ckmeans.1d.dp') + except: + return False + return True + + +def is_finemap_installed(finemap_exe): + if finemap_exe is None: return False + assert os.path.exists(finemap_exe), 'the FINEMAP path provided is wrong (%s)'%(finemap_exe) + assert os.access(finemap_exe, os.X_OK), 'the FINEMAP executable doesn\'t have execution permissions' + return True + + +def test_munge_sumstats(tmpdir, python3_exe): + script_path = os.path.dirname(os.path.realpath(__file__)) + example_dir = os.path.join(script_path, 'example_data') + gold_dir = os.path.join(script_path, 'gold') + script_exe = os.path.join(script_path, 'munge_polyfun_sumstats.py') + input_file = os.path.join(example_dir, 'boltlmm_sumstats.gz') + outfile = 'sumstats_munged.parquet' + output_file = os.path.join(tmpdir, outfile) + gold_file = os.path.join(gold_dir, outfile) + + cmd = '%s %s --sumstats %s --out %s --n 327209 --min-info 0.6 --min-maf 0.001'%(python3_exe, script_exe, input_file, output_file) + retval = os.system(cmd) + if retval != 0: + raise ValueError('munge_sumstats command failed when running the following command:\n%s'%(cmd)) + + compare_dfs(tmpdir, gold_dir, outfile) + + +def test_polyfun(tmpdir, python3_exe): + + if not is_Ckmeans_installed(): + print('Skipping polyfun tests becuase CkMeans is not installed') + return + + + script_path = os.path.dirname(os.path.realpath(__file__)) + example_dir = os.path.join(script_path, 'example_data') + gold_dir = os.path.join(script_path, 'gold') + script_exe = os.path.join(script_path, 'polyfun.py') + sumstats_file = os.path.join(example_dir, 'sumstats.parquet') + output_prefix = os.path.join(tmpdir, 'testrun') + ref_ld_prefix = os.path.join(example_dir, 'annotations.') + w_ld_prefix = os.path.join(example_dir, 'weights.') + plink_prefix = os.path.join(example_dir, 'reference.') + + #polyfun stage 2 + cmd = '%s %s --compute-h2-L2 --output-prefix %s --sumstats %s --ref-ld-chr %s --w-ld-chr %s --nnls-exact' % \ + (python3_exe, script_exe, output_prefix, sumstats_file, ref_ld_prefix, w_ld_prefix) + retval = os.system(cmd) + if retval != 0: + raise ValueError('PolyFun command failed when running the following command:\n%s'%(cmd)) + for outfile in ['testrun.22.bins.parquet', 'testrun.22.snpvar_ridge_constrained.gz', 'testrun.22.snpvar_ridge.gz', 'testrun.22.l2.M']: + compare_dfs(tmpdir, gold_dir, outfile) + + #polyfun stage 3 + cmd = '%s %s --compute-ldscores --output-prefix %s --bfile-chr %s --nnls-exact'% \ + (python3_exe, script_exe, output_prefix, plink_prefix) + retval = os.system(cmd) + if retval != 0: + raise ValueError('PolyFun command failed when running the following command:\n%s'%(cmd)) + + compare_dfs(tmpdir, gold_dir, 'testrun.22.l2.ldscore.parquet') + + #polyfun stage 4 + cmd = '%s %s --compute-h2-bins --output-prefix %s --sumstats %s --w-ld-chr %s --nnls-exact'% \ + (python3_exe, script_exe, output_prefix, sumstats_file, w_ld_prefix) + retval = os.system(cmd) + if retval != 0: + raise ValueError('PolyFun command failed when running the following command:\n%s'%(cmd)) + compare_dfs(tmpdir, gold_dir, 'testrun.22.snpvar_constrained.gz') + + + +def test_polyloc(tmpdir, python3_exe): + + if not is_Ckmeans_installed(): + print('Skipping polyloc tests becuase CkMeans is not installed') + return + + script_path = os.path.dirname(os.path.realpath(__file__)) + example_dir = os.path.join(script_path, 'example_data') + gold_dir = os.path.join(script_path, 'gold') + script_exe = os.path.join(script_path, 'polyloc.py') + sumstats_file = os.path.join(example_dir, 'sumstats2.parquet') + output_prefix = os.path.join(tmpdir, 'polyloc_test') + w_ld_prefix = os.path.join(example_dir, 'weights.') + plink_prefix = os.path.join(example_dir, 'reference.') + posterior_file = os.path.join(example_dir, 'posterior_betas.gz') + + #polyloc stage 1 + polyloc_cmd = ('%s %s --compute-partitions --output-prefix %s --posterior %s --bfile-chr %s --nnls-exact'% \ + (python3_exe, script_exe, output_prefix, posterior_file, plink_prefix)) + retval = os.system(polyloc_cmd) + if retval != 0: + raise ValueError('PolyLoc command failed when running the following command:\n%s'%(polyloc_cmd)) + for outfile in ['polyloc_test.22.bins.parquet', 'polyloc_test.22.l2.M']: + compare_dfs(tmpdir, gold_dir, outfile) + + + #polyloc stage 2 + polyloc_cmd = ('%s %s --compute-ldscores --output-prefix %s --bfile-chr %s --nnls-exact'% \ + (python3_exe, script_exe, output_prefix, plink_prefix)) + retval = os.system(polyloc_cmd) + if retval != 0: + raise ValueError('PolyLoc command failed when running the following command:\n%s'%(polyloc_cmd)) + compare_dfs(tmpdir, gold_dir, 'polyloc_test.22.l2.ldscore.parquet') + + #polyloc stage 3 + polyloc_cmd = ('%s %s --compute-polyloc --output-prefix %s --w-ld-chr %s --sumstats %s --nnls-exact'% \ + (python3_exe, script_exe, output_prefix, w_ld_prefix, sumstats_file)) + retval = os.system(polyloc_cmd) + if retval != 0: + raise ValueError('PolyLoc command failed when running the following command:\n%s'%(polyloc_cmd)) + for outfile in ['polyloc_test.bin_h2', 'polyloc_test.Mp']: + compare_dfs(tmpdir, gold_dir, outfile) + + + + + + +def test_extract_snpvar(tmpdir, python3_exe): + script_path = os.path.dirname(os.path.realpath(__file__)) + example_dir = os.path.join(script_path, 'example_data') + gold_dir = os.path.join(script_path, 'gold') + script_exe = os.path.join(script_path, 'extract_snpvar.py') + input_file = os.path.join(example_dir, 'snps_to_finemap.txt.gz') + outfile = 'snps_with_var.gz' + output_file = os.path.join(tmpdir, outfile) + gold_file = os.path.join(gold_dir, outfile) + + cmd = '%s %s --sumstats %s --out %s'%(python3_exe, script_exe, input_file, output_file) + retval = os.system(cmd) + if retval != 0: + raise ValueError('extract_snpvar command failed when running the following command:\n%s'%(cmd)) + compare_dfs(tmpdir, gold_dir, outfile) + + +def test_finemapper_susie(tmpdir, python3_exe): + + if not is_susie_installed(): + print('Skipping fine-mapping test because either rpy2 or SuSiE are not properly installed. Please try reinstalling rpy2 and/or SuSiE') + return + + script_path = os.path.dirname(os.path.realpath(__file__)) + example_dir = os.path.join(script_path, 'example_data') + gold_dir = os.path.join(script_path, 'gold') + script_exe = os.path.join(script_path, 'finemapper.py') + sumstats_file = os.path.join(example_dir, 'chr1.finemap_sumstats.txt.gz') + plink_file = os.path.join(example_dir, 'chr1') + outfile = 'finemap.1.46000001.49000001.gz' + output_file = os.path.join(tmpdir, outfile) + + finemapper_cmd = '%s %s \ + --geno %s \ + --sumstats %s \ + --n 383290 \ + --chr 1 \ + --start 46000001 \ + --end 49000001 \ + --method susie \ + --max-num-causal 5 \ + --out %s \ + --threads 1 \ + --verbose \ + ' \ + %(python3_exe, script_exe, plink_file, sumstats_file, output_file) + + #print(finemapper_cmd) + retval = os.system(finemapper_cmd) + if retval != 0: + raise ValueError('finemapper command failed when running the following command:\n%s'%(cmd)) + compare_dfs(tmpdir, gold_dir, outfile, sort_column='SNP') + + +def test_finemapper_finemap(tmpdir, python3_exe, finemap_exe): + + if not is_finemap_installed(finemap_exe): + print('Skipping FINEMAP test because the executable is not available. Re-run with --finemap-exe to test FINEMAP') + return + + script_path = os.path.dirname(os.path.realpath(__file__)) + example_dir = os.path.join(script_path, 'example_data') + gold_dir = os.path.join(script_path, 'gold') + script_exe = os.path.join(script_path, 'finemapper.py') + sumstats_file = os.path.join(example_dir, 'chr1.finemap_sumstats.txt.gz') + plink_file = os.path.join(example_dir, 'chr1') + outfile = 'finemap.FINEMAP.1.46000001.49000001.gz' + output_file = os.path.join(tmpdir, outfile) + + finemapper_cmd = '%s %s \ + --geno %s \ + --sumstats %s \ + --n 383290 \ + --chr 1 \ + --start 46000001 \ + --end 49000001 \ + --method finemap \ + --finemap-exe %s \ + --max-num-causal 5 \ + --out %s \ + --threads 1 \ + --verbose \ + ' \ + %(python3_exe, script_exe, plink_file, sumstats_file, finemap_exe, output_file) + + #print(finemapper_cmd) + retval = os.system(finemapper_cmd) + if retval != 0: + raise ValueError('finemapper command failed when running the following command:\n%s'%(cmd)) + compare_dfs(tmpdir, gold_dir, outfile, sort_column='SNP') + + +if __name__ == '__main__': + + import argparse + parser = argparse.ArgumentParser() + + parser.add_argument('--python3', default='python', help='python 3 executable') + parser.add_argument('--finemap-exe', default=None, help='Path to FINEMAP executable file') + args = parser.parse_args() + temp_dir = tempfile.mkdtemp() + + test_finemapper_susie(temp_dir, args.python3) + test_finemapper_finemap(temp_dir, args.python3, args.finemap_exe) + + test_polyloc(temp_dir, args.python3) + test_polyfun(temp_dir, args.python3) + test_extract_snpvar(temp_dir, args.python3) + test_munge_sumstats(temp_dir, args.python3) + + print() + print() + print() + print('---------------------------------------') + print('All tests completed successfully, hooray!!!') + print('---------------------------------------') + print() + + + \ No newline at end of file diff --git a/SCRIPTS/polyfun/ukb_regions.tsv.gz b/SCRIPTS/polyfun/ukb_regions.tsv.gz new file mode 100644 index 0000000..dc8bdfe Binary files /dev/null and b/SCRIPTS/polyfun/ukb_regions.tsv.gz differ diff --git a/metagwastoolkit.conf b/metagwastoolkit.conf index 4fb6174..3f828a0 100755 --- a/metagwastoolkit.conf +++ b/metagwastoolkit.conf @@ -80,6 +80,10 @@ HWE="1E-3" INFO="0.4" BETA="5" SE="5" +structuralvariant="YES" +CAVEATREMOVE="YES" +DF="1" +DAF="0.12" # # REQUIRED: GWAS dataset plot settings. # - for the random sampling in the P-Z plotter; if the dataset is smaller than this sample @@ -111,6 +115,7 @@ TITLEPLOT="" # REFFREQFILE -- a file containing frequencies per variant. # VINFOFILE -- a file containing per variant information. # GENESFILE -- a file containing chromosomal basepair positions per gene. +# REFERENCEPLINK-- path to the folder containing PLINK-format reference files # PARAMSFILE -- a file containing 5 fields: study name, lambda, sample size, # beta-correction factor, file locations. # ### FUTURE VERSIONS WILL HAVE AN AUTOMATIC SCRIPTER FOR THE PARAMSFILE ### @@ -122,12 +127,25 @@ REFERENCE="1Gp3" METAMODEL="RANDOM" VERBOSE="VERBOSE" RESOURCES="${METAGWASTOOLKITDIR}/RESOURCES" -DBSNPFILE="${RESOURCES}/1000Gp3v5_20130502_mvncall_integrated_v5c.EUR.FUNC.txt.gz" -REFFREQFILE="${RESOURCES}/1000Gp3v5_20130502_mvncall_integrated_v5c.EUR.FREQ.txt.gz" -VINFOFILE="${RESOURCES}/1000Gp3v5_20130502_mvncall_integrated_v5c.EUR.INFO.txt.gz" +DBSNPFILE="${RESOURCES}/1000Gp3v5_20130502_mvncall_integrated_v5c.${POPULATION}.FUNC.txt.gz" +REFFREQFILE="${RESOURCES}/1000Gp3v5_20130502_mvncall_integrated_v5c.${POPULATION}.FREQ.txt.gz" +VINFOFILE="${RESOURCES}/1000Gp3v5_20130502_mvncall_integrated_v5c.${POPULATION}.INFO.txt.gz" GENESFILE="${RESOURCES}/gencode_v19_GRCh37_hg19_Feb2009.txt.gz" -REFERENCEVCF="${RESOURCES}/1000Gp3v5_EUR" +REFERENCEPLINK="/hpc/dhl_ec/data/references/1000G/Phase3/PLINK_format" PARAMSFILE="${PROJECTDIR}/${OUTPUTDIRNAME}/metagwastoolkit.${SUBPROJECTDIRNAME}.params" +TRAITFILE="${RESOURCES}/traits/METAGWASTOOLKIT.TRAITS.txt" +SNPLIST="${METAGWASTOOLKITDIR}/SCRIPTS/ldsc/w_hm3.snplist" +REF_LD="${RESOURCES}/${POPULATION}_w_ld_chr/" +POLY_REF="${RESOURCES}" +GSMR_REF="${RESOURCES}/gsmr/gsmr_ref_data.${POPULATION}.txt" +GWASLABPLOT="YES" +PERFORM_QC="YES" +ONLY_QC="YES" +SELECT_LEADS="YES" +PERFORM_LDSC="YES" +PERFORM_POLYFUN="YES" +PERFORM_GSMR="YES" + # # REQUIRED: CLUMP settings. # CLUMP_P1 # e.g.5.0e-06 Significance threshold for index SNPs @@ -144,8 +162,8 @@ CLUMP_P2="0.05" CLUMP_R2="0.05" CLUMP_KB="1000" CLUMP_FIELD="P_FIXED" -CLUMP_SNP_FIELD="VARIANTID" -LDMAP="--pop EUR --build hg19 --source 1000G_March2012" +CLUMP_SNP_FIELD="RSID" +LDMAP="--pop ${POPULATION} --build hg19 --source 1000G_March2012" LOCUSZOOM_SETTINGS="ldColors=\"#595A5C,#4C81BF,#1396D8,#C5D220,#F59D10,red,#9A3480\" showRecomb=TRUE drawMarkerNames=FALSE refsnpTextSize=1.0 showRug=FALSE showAnnot=TRUE showRefsnpAnnot=TRUE showGenes=TRUE clean=TRUE bigDiamond=TRUE refsnpLineWidth=2 axisSize=1.25 axisTextSize=1.25 refsnpLineWidth=1.25 geneFontSize=1.25" LZRANGE="250" # diff --git a/metagwastoolkit.pipeline.analysis.sh b/metagwastoolkit.pipeline.analysis.sh new file mode 100644 index 0000000..b29e1d7 --- /dev/null +++ b/metagwastoolkit.pipeline.analysis.sh @@ -0,0 +1,235 @@ +#!/bin/bash + +# SLURM settings +#SBATCH --job-name=pipeline # Job name +#SBATCH --mail-type=FAIL # Mail events (NONE, BEGIN, END, FAIL, ALL) +#SBATCH --mail-user=e.j.a.smulders-2@umcutrecht.nl # Where to send mail +#SBATCH --nodes=1 # Run on one node +#SBATCH --ntasks=1 # Run on a single CPU +#SBATCH --mem=128G # Job memory request +#SBATCH --gres=tmpspace:128G # the amount of temporary diskspace per node +#SBATCH --time=72:00:00 # Time limit hrs:min:sec +#SBATCH --output=pipeline.analysis.out # Standard output and error log + +############################## +## DOWNSTREAM GWAS ANALYSIS ## +############################## + + +####### Input data of phenotypes and traits needs to be in cojo formatfor both LDSC and GSMR, GWASCatalog format for PolyFun ###### +####### check gwas2cojo.run.sh for file conversion ########################### + + + +python3_exe="/hpc/local/Rocky8/dhl_ec/software/mambaforge3/envs/polyfun/bin/python3" + +METAGWASTOOLKIT="/hpc/local/Rocky8/dhl_ec/software/MetaGWASToolKit" +PROJECTNAME="PROJECTNAME" +SCRIPTS="${METAGWASTOOLKIT}/SCRIPTS_own" +PROJECTDIR="${METAGWASTOOLKIT}/EXAMPLE" +SUBPROJECTDIRNAME="MODEL1" + + +data_analysed="PHENOTYPE" +PERFORM_QC="YES" +MAKE_FIGURES="YES" + # ONLY -QC if yes, it is expected that a .pkl file already exists +ONLY_QC="YES" +SELECT_LEADS="YES" +PHENOTYPE_FILE="${PROJECTDIR}/${PROJECTNAME}.PHENOTYPES.txt" +TRAIT_FILE="${PROJECTDIR}/${PROJECTNAME}.TRAITS.txt" + +POPULATION="EUR" +###LDSC +# path to map with ld +REF="${METAGWASTOOLKIT}/RESOURCES" +REF_LD="${REF}/eur_w_ld_chr/" +# path to merge allele snplist +SNPLIST="${REF}/ldsc/w_hm3.snplist" + + +#polyfun +min_maf=0.005 +max_num_causal=1 +pip_cutoff=0.50 +CHUNKSIZE=500000 + + +#GSMR reference +GSMR_REF="${REF}/gsmr/gsmr_ref_data_eur.txt" + +rsid="rsid" +n="n" +a1="a1" +a2="a2" +p="p" +eaf="eaf" + +# data preparation +if [ "$data_analysed" == "PHENOTYPE" ]; then + if [ -n "$PHENOTYPE_FILE" ]; then + sed -i 's/\r$//' "$PHENOTYPE_FILE" + PHENOTYPES=() + SAMPLESIZES=() + PATHS=() + while IFS=' ' read -r phenotype sample_size path; do + PHENOTYPES+=("$phenotype") + SAMPLESIZES+=("$sample_size") + PATHS+=("$path") + done < "$PHENOTYPE_FILE" + + if [ ${#PHENOTYPES[@]} -eq 0 ]; then + echo "Phenotype file is empty or not properly formatted." + exit 1 + fi + else + echo "Phenotype file (-p) is required." + exit 1 + fi + + for (( i=0; i<${#PHENOTYPES[@]}; i++ )); do + PHENOTYPE="${PHENOTYPES[i]}" + SAMPLE_SIZE="${SAMPLESIZES[i]}" + INPUT="${PATHS[i]}" + + mkdir -p "${PROJECTDIR}/${PHENOTYPE}" + mkdir -p "${PROJECTDIR}/${PHENOTYPE}/input" + echo "Phenotype: ${PHENOTYPE}" + echo "Sample Size: ${SAMPLE_SIZE}" + echo "Input file: ${INPUT}" + zcat "${PROJECTDIR}/${PHENOTYPE}/${INPUT}" | head + + # Parsing + #Rscript ${SCRIPTS}/pipeline.parser.R -p ${PROJECTDIR}/${PHENOTYPE} -d ${INPUT} -o input + #GWASLAB + source /hpc/local/Rocky8/dhl_ec/software/mambaforge3/bin/activate gwaslab_env + python3 ${SCRIPTS}/pipeline.gwaslab.py -g ${PHENOTYPE} -d ${PROJECTDIR}/${PHENOTYPE} -i ${INPUT} -p ${POPULATION} -r ${REF} --qc ${PERFORM_QC} --figures ${MAKE_FIGURES} --onlyqc {ONLY_QC} --leads ${SELECT_LEADS} + #COJO + zcat "${PROJECTDIR}/${PHENOTYPE}/GWASCatalog/${PHENOTYPE}.b37.gwaslab.qc.ssf.tsv.gz" | head + mkdir -p ${PROJECTDIR}/${PHENOTYPE} + mkdir -p ${PROJECTDIR}/${PHENOTYPE}/input + mkdir -p ${PROJECTDIR}/${PHENOTYPE}/gsmr + mkdir -p ${PROJECTDIR}/${PHENOTYPE}/polyfun + mkdir -p ${PROJECTDIR}/${PHENOTYPE}/ldsc + ${python3_exe} /hpc/local/Rocky8/dhl_ec/software/gwas2cojo/gwas2cojo.py \ + --gen:build hg19 \ + --gen ${RESOURCES}/1kGp3v5b.ref.allfreq.noCN_noINS_noSS_noESV_noMultiAllelic.sumstats.txt.gz \ + --gwas ${PROJECTDIR}/${PHENOTYPE}/GWASCatalog/${PHENOTYPE}.b37.gwaslab.qc.ssf.tsv.gz \ + --gen:ident ID --gen:chr CHROM --gen:bp POS --gen:other REF --gen:effect ALT --gen:eaf EUR_AF \ + --gwas:chr chromosome --gwas:bp base_pair_location --gwas:other other_allele --gwas:effect effect_allele \ + --gwas:beta beta --gwas:se standard_error --gwas:p p_value \ + --gwas:freq effect_allele_frequency --gwas:n n --gwas:build hg19 \ + --out ${PROJECTDIR}/${PHENOTYPE}/input/${PHENOTYPE}.cojo + ${python3_exe} /hpc/local/Rocky8/dhl_ec/software/gwas2cojo/gwas2cojo-verify.py ${PROJECTDIR}/${PHENOTYPE}/input/${PHENOTYPE}.cojo + gzip -vf ${PROJECTDIR}/${PHENOTYPE}/input/${PHENOTYPE}.cojo + done +fi + + +#LDSC +if [ "$data_analysed" == "PHENOTYPE" ]; then + if [ -n "$PHENOTYPE_FILE" ]; then + sed -i 's/\r$//' "$PHENOTYPE_FILE" + PHENOTYPES=() + SAMPLESIZES=() + PATHS=() + while IFS=' ' read -r phenotype sample_size path; do + PHENOTYPES+=("$phenotype") + SAMPLESIZES+=("$sample_size") + PATHS+=("$path") + done < "$PHENOTYPE_FILE" + + if [ ${#PHENOTYPES[@]} -eq 0 ]; then + echo "Phenotype file is empty or not properly formatted." + exit 1 + fi + else + echo "Phenotype file (-p) is required." + exit 1 + fi + for (( i=0; i<${#PHENOTYPES[@]}; i++ )); do + PHENOTYPE="${PHENOTYPES[i]}" + #PHENOPATH="${PATHS[i]}" + #echo "${PHENOTYPE}" + sbatch ${DATA}/ldsc/ldsc.run.sh -w "${data_analysed}" -d "${PROJECTDIR}" -j "${PROJECTNAME}" -p "${PHENOTYPE}" -t "${TRAIT_FILE}" -l "${REF}/eur_w_ld_chr/" -h "${SNPLIST}" -r ${rsid} -n ${n} -a ${a1} -b ${a2} -v ${p} -f ${eaf} -c ${CHUNKSIZE} + done +fi +# +Check if trait file is provided and read the contents if data_analysed is TRAIT +if [ "$data_analysed" == "TRAIT" ] && [ -n "$TRAIT_FILE" ]; then + TRAITS=() + PATHS=() + while IFS= read -r line || [ -n "$line" ]; do + # Use read to parse line into trait and path + read -r trait path <<< "$line" + TRAITS+=("$trait") + PATHS+=("$path") + done < "$TRAIT_FILE" + + if [ ${#TRAITS[@]} -eq 0 ]; then + echo "Trait file is empty or not properly formatted." + exit 1 + fi + for (( i=0; i<${#TRAITS[@]}; i++ )); do + TRAIT="${TRAITS[i]}" + TRAITPATH="${PATHS[i]}" + echo "Analyzing trait : ${TRAIT}" + sbatch ${SCRIPTS}/ldsc/ldsc.run.sh -w "${data_analysed}" -d "${PROJECTDIR}" -j "${PROJECTNAME}" -p "${PHENOTYPE_FILE}" -t "${TRAIT}" -i "${TRAITPATH}" -l "${REF}/eur_w_ld_chr/" -h "${SNPLIST}" -r ${rsid} -n ${n} -a ${a1} -b ${a2} -v ${p} -f ${eaf} -c ${CHUNKSIZE} + done +fi + +#PolyFun +if [ "$data_analysed" == "PHENOTYPE" ]; then + if [ -n "$PHENOTYPE_FILE" ]; then + sed -i 's/\r$//' "$PHENOTYPE_FILE" + PHENOTYPES=() + SAMPLESIZES=() + PATHS=() + while IFS=' ' read -r phenotype sample_size path; do + PHENOTYPES+=("$phenotype") + SAMPLESIZES+=("$sample_size") + PATHS+=("$path") + done < "$PHENOTYPE_FILE" + + if [ ${#PHENOTYPES[@]} -eq 0 ]; then + echo "Phenotype file is empty or not properly formatted." + exit 1 + fi + else + echo "Phenotype file (-p) is required." + exit 1 + fi + for (( i=0; i<${#PHENOTYPES[@]}; i++ )); do + PHENOTYPE="${PHENOTYPES[i]}" + SAMPLESIZE="${SAMPLESIZES[i]}" + sbatch ${SCRIPTS}/polyfun/polyfun.run.sh -p "${PHENOTYPE}" -s "${SAMPLESIZE}" -d "${PROJECTDIR}" -j "${PROJECTNAME}" -l "${REF}" -m "${min_maf}" -x "${max_num_causal}" -c "${pip_cutoff}" + done +fi + +#GSMR +if [ "$data_analysed" == "PHENOTYPE" ]; then + if [ -n "$PHENOTYPE_FILE" ]; then + sed -i 's/\r$//' "$PHENOTYPE_FILE" + PHENOTYPES=() + SAMPLESIZES=() + PATHS=() + while IFS=' ' read -r phenotype sample_size path; do + PHENOTYPES+=("$phenotype") + SAMPLESIZES+=("$sample_size") + PATHS+=("$path") + done < "$PHENOTYPE_FILE" + + if [ ${#PHENOTYPES[@]} -eq 0 ]; then + echo "Phenotype file is empty or not properly formatted." + exit 1 + fi + else + echo "Phenotype file (-p) is required." + exit 1 + fi + for (( i=0; i<${#PHENOTYPES[@]}; i++ )); do + PHENOTYPE="${PHENOTYPES[i]}" + echo "${PHENOTYPE}" + sbatch ${SCRIPTS}/gsmr/gsmr.analysis.sh -t "${TRAIT_FILE}" -d "${PROJECTDIR}" -j "${PROJECTNAME}" -p "${PHENOTYPE}" -r "${GSMR_REF}" + done +fi \ No newline at end of file diff --git a/metagwastoolkit.run.sh b/metagwastoolkit.run.sh index f32a072..e657d09 100755 --- a/metagwastoolkit.run.sh +++ b/metagwastoolkit.run.sh @@ -5,9 +5,11 @@ #SBATCH --job-name=metagwastoolkit # the name of the job #SBATCH -o path_to_projectdir/subdir/metagwastoolkit.prep.log # the log file of this job #SBATCH --error path_to_projectdir/subdir/metagwastoolkit.prep.errors # the error file of this job -#SBATCH --time=01:00:00 # the amount of time the job will take: -t [min] OR -t [days-hh:mm:ss] -#SBATCH --mem=8G # the amount of memory you think the script will consume, found on: https://wiki.bioinformatics.umcutrecht.nl/bin/view/HPC/SlurmScheduler +#SBATCH --time=24:00:00 # the amount of time the job will take: -t [min] OR -t [days-hh:mm:ss] +#SBATCH --mem=128G # the amount of memory you think the script will consume, found on: https://wiki.bioinformatics.umcutrecht.nl/bin/view/HPC/SlurmScheduler #SBATCH --gres=tmpspace:128G # the amount of temporary diskspace per node +#SBATCH --nodes=8 #run on one node +#SBATCH --ntasks=1 # Run on a single CPU #SBATCH --mail-user=s.w.vanderlaan-2@umcutrecht.nl # where should be mailed to? #SBATCH --mail-type=FAIL # when do you want to receive a mail from your job? Valid type values are NONE, BEGIN, END, FAIL, REQUEUE # or ALL (equivalent to BEGIN, END, FAIL, INVALID_DEPEND, REQUEUE, and STAGE_OUT), @@ -33,6 +35,10 @@ PROJECTNAME="EXAMPLEPHENOTYPE" PROJECTDIR="${METAGWASTOOLKIT}/EXAMPLE" SUBPROJECTDIRNAME="MODEL1" PYTHON3="/hpc/local/Rocky8/dhl_ec/software/mambaforge3/bin/python3" +METAMODEL="FIXED" # FIXED, SQRTN, or RANDOM. Should match "CLUMP_FIELD" variable from the .conf file. +CONFILE"${PROJECTDIR}/metagwastoolkit.conf" +FILESLIST="${PROJECTDIR}/metagwastoolkit.files.list" +POP="POPULATION" echo "" echo " PERFORM META-ANALYSIS OF GENOME-WIDE ASSOCIATION STUDIES" @@ -47,7 +53,7 @@ echo "" echo "FIRST step: prepare GWAS." ### DEBUGGING ### ${SCRIPTS}/metagwastoolkit.prep.sh ${PROJECTDIR}/metagwastoolkit.conf ${PROJECTDIR}/metagwastoolkit.files.list.test -${SCRIPTS}/metagwastoolkit.prep.sh ${PROJECTDIR}/metagwastoolkit.conf ${PROJECTDIR}/metagwastoolkit.files.list +${SCRIPTS}/metagwastoolkit.prep.sh ${CONFILE} ${FILESLIST} ### Note: After visual inspection of diagnostic plots per cohort (see note above), the next ### steps can be uncommented and executed one-by-one. It is advisable to always @@ -56,15 +62,11 @@ ${SCRIPTS}/metagwastoolkit.prep.sh ${PROJECTDIR}/metagwastoolkit.conf ${PROJECTD echo "" echo "SECOND step: prepare meta-analysis." -# ${SCRIPTS}/metagwastoolkit.prepmeta.sh ${PROJECTDIR}/metagwastoolkit.conf ${PROJECTDIR}/metagwastoolkit.files.list +# ${SCRIPTS}/metagwastoolkit.prepmeta.sh ${CONFILE} ${FILESLIST} echo "" echo "THIRD step: meta-analysis." -# ${SCRIPTS}/metagwastoolkit.meta.sh ${PROJECTDIR}/metagwastoolkit.conf ${PROJECTDIR}/metagwastoolkit.files.list - - - - +# ${SCRIPTS}/metagwastoolkit.meta.sh $${CONFILE} ${FILESLIST} ### BELOW WILL LIKELY BE OBSOLETE AS EVERYTHING CAN BE DONE USING GWASLAB FUNCTIONS ### @@ -80,20 +82,20 @@ echo "THIRD step: meta-analysis." # # echo "" # echo "> creating list of variants (only needs to be done once!)" -# echo 'VARIANTID RSID' > ${RESOURCES}/1000Gp3v5_20130502_mvncall_integrated_v5b.EUR.EUR.VARIANTID2RSID.txt -# ### zcat ${RESOURCES}/1000Gp3v5_20130502_mvncall_integrated_v5b.EUR.EUR.INFO.txt.gz | awk '{ print $1, $2 }' | tail -n +2 >> ${RESOURCES}/1000Gp3v5_20130502_mvncall_integrated_v5b.EUR.EUR.VARIANTID2RSID.txt -# zcat ${RESOURCES}/1000Gp3v5_EUR/1kGp3v5b.ref.allfreq.noCN_noINS_noSS_noESV_noMultiAllelic.sumstats.txt.gz | awk '{ if($7<0.5) { print "chr"$1":"$2":"$5"_"$4, $3} else { print "chr"$1":"$2":"$4"_"$5, $3 } }' | tail -n +2 >> ${RESOURCES}/1000Gp3v5_20130502_mvncall_integrated_v5b.EUR.EUR.VARIANTID2RSID.txt -# gzip -fv ${RESOURCES}/1000Gp3v5_20130502_mvncall_integrated_v5b.EUR.EUR.VARIANTID2RSID.txt +# echo 'VARIANTID RSID' > ${RESOURCES}/1000Gp3v5_20130502_mvncall_integrated_v5b.only_SNPS_INDELS.EUR.VARIANTID2RSID.txt +# zcat ${RESOURCES}/1000Gp3v5_20130502_mvncall_integrated_v5b.only_SNPS_INDELS.EUR.INFO.txt.gz | awk '{ print $1, $2 }' | tail -n +2 >> ${RESOURCES}/1000Gp3v5_20130502_mvncall_integrated_v5b.only_SNPS_INDELS.EUR.VARIANTID2RSID.txt +# ### zcat ${RESOURCES}/1000Gp3v5_EUR/1kGp3v5b.ref.allfreq.noCN_noINS_noSS_noESV_noMultiAllelic.sumstats.txt.gz | awk '{ if($7<0.5) { print "chr"$1":"$2":"$5"_"$4, $3} else { print "chr"$1":"$2":"$4"_"$5, $3 } }' | tail -n +2 >> ${RESOURCES}/1000Gp3v5_20130502_mvncall_integrated_v5b.only_SNPS_INDELS.EUR.VARIANTID2RSID.txt +# gzip -fv ${RESOURCES}/1000Gp3v5_20130502_mvncall_integrated_v5b.only_SNPS_INDELS.EUR.VARIANTID2RSID.txt # # echo "" # echo "> counting list of variants" -# zcat ${RESOURCES}/1000Gp3v5_20130502_mvncall_integrated_v5b.EUR.EUR.VARIANTID2RSID.txt.gz | wc -l +# zcat ${RESOURCES}/1000Gp3v5_20130502_mvncall_integrated_v5b.only_SNPS_INDELS.EUR.VARIANTID2RSID.txt.gz | wc -l # # echo "" # echo "> merging list of variants with summary statistics" # perl ${SCRIPTS}/mergeTables.pl \ # --file1 ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/meta.results.${PROJECTNAME}.1Gp3.EUR.summary.txt.gz \ -# --file2 ${RESOURCES}/1000Gp3v5_20130502_mvncall_integrated_v5b.EUR.EUR.VARIANTID2RSID.txt.gz \ +# --file2 ${RESOURCES}/1000Gp3v5_20130502_mvncall_integrated_v5b.only_SNPS_INDELS.EUR.VARIANTID2RSID.txt.gz \ # --index VARIANTID --format GZIPB > ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/foo # # echo "" @@ -101,7 +103,7 @@ echo "THIRD step: meta-analysis." # cat ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/foo | awk ' $3 != "NA" ' > ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/meta.results.${PROJECTNAME}.1Gp3.EUR.summary.rsids.txt # # echo "" -# echo "> removing intermediate file, getting a head, counting results, and gzipping" +# echo "> removing intermediate file" # rm -v ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/foo # # echo "" @@ -123,10 +125,43 @@ echo "THIRD step: meta-analysis." # echo "> include only variants with no caveats" ### already done ! -### mv -v ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/meta.results.${PROJECTNAME}.1Gp3.EUR.summary.txt.gz ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/meta.results.${PROJECTNAME}.1Gp3.EUR.summary.originalID.txt.gz +# mv -v ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/meta.results.${PROJECTNAME}.1Gp3.EUR.summary.txt.gz ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/meta.results.${PROJECTNAME}.1Gp3.EUR.summary.originalID.txt.gz + + +### Determining the type of data to parse depending on the chosen metamodel +if [[ ${METAMODEL} = "FIXED" ]]; then + BETA="BETA_FIXED" + SE="SE_FIXED" + BETA_LOWER="BETA_LOWER_FIXED" + BETA_UPPER="BETA_UPPER_FIXED" + ZSCORE="Z_FIXED" + PVALUE="P_FIXED" +elif [[ ${METAMODEL} = "SQRTN" ]]; then + BETA="BETA_FIXED" + SE="SE_FIXED" + BETA_LOWER="BETA_LOWER_FIXED" + BETA_UPPER="BETA_UPPER_FIXED" + ZSCORE="Z_SQRTN" + PVALUE="P_SQRTN" +elif [[ ${METAMODEL} = "RANDOM" ]]; then + BETA="BETA_RANDOM" + SE="SE_RANDOM" + BETA_LOWER="BETA_LOWER_RANDOM" + BETA_UPPER="BETA_UPPER_RANDOM" + ZSCORE="Z_RANDOM" + PVALUE="P_RANDOM" +else + echo "Incorrect or no METAMODEL variable specified, defaulting to a FIXED model." + BETA="BETA_FIXED" + SE="SE_FIXED" + BETA_LOWER="BETA_LOWER_FIXED" + BETA_UPPER="BETA_UPPER_FIXED" + ZSCORE="Z_FIXED" + PVALUE="P_FIXED" +fi # zcat ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/meta.results.${PROJECTNAME}.1Gp3.EUR.summary.rsids.txt.gz | \ -# perl ${SCRIPTS}/parseTable.pl --col RSID,VARIANTID,CHR,POS,CODEDALLELE,OTHERALLELE,CAF,N_EFF,BETA_FIXED,SE_FIXED,BETA_LOWER_FIXED,BETA_UPPER_FIXED,Z_FIXED,P_FIXED,COCHRANS_Q,P_COCHRANS_Q,I_SQUARED,TAU_SQUARED,DF,DIRECTIONS,GENES_250KB,NEAREST_GENE,VARIANT_FUNCTION,CAVEAT > ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/meta.results.${PROJECTNAME}.1Gp3.EUR.summary.rsid.filter.txt +# perl ${SCRIPTS}/parseTable.pl --col RSID,VARIANTID,CHR,POS,CODEDALLELE,OTHERALLELE,CAF,N_EFF,${BETA},${SE},${BETA_LOWER},${BETA_UPPER},${ZSCORE},${PVALUE},COCHRANS_Q,P_COCHRANS_Q,I_SQUARED,TAU_SQUARED,DF,DIRECTIONS,GENES_250KB,NEAREST_GENE,VARIANT_FUNCTION,CAVEAT > ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/meta.results.${PROJECTNAME}.1Gp3.EUR.summary.rsid.filter.txt # # gzip -vf ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/meta.results.${PROJECTNAME}.1Gp3.EUR.summary.rsid.filter.txt # @@ -138,7 +173,7 @@ echo "THIRD step: meta-analysis." # # echo "Filtering on DF>0, meaning only 1 cohort was included in the meta-result; we require a minimum of 2." # cat ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/meta.results.${PROJECTNAME}.1Gp3.EUR.summary.filtered_incl_non_rsID.txt | head -1 > ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/meta.results.${PROJECTNAME}.1Gp3.EUR.summary.txt -# cat ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/meta.results.${PROJECTNAME}.1Gp3.EUR.summary.filtered_incl_non_rsID.txt | awk '$19 > 0' | grep "rs" > ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/meta.results.${PROJECTNAME}.1Gp3.EUR.summary.txt +# cat ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/meta.results.${PROJECTNAME}.1Gp3.EUR.summary.filtered_incl_non_rsID.txt | awk '$19 > 0' | grep "rs" >> ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/meta.results.${PROJECTNAME}.1Gp3.EUR.summary.txt # cat ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/meta.results.${PROJECTNAME}.1Gp3.EUR.summary.txt | wc -l # # gzip -vf ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/meta.results.${PROJECTNAME}.1Gp3.EUR.summary.rsid.filter.badVariants.txt @@ -147,13 +182,47 @@ echo "THIRD step: meta-analysis." # # echo "" # echo "FIFTH step: result clumping." -# ${SCRIPTS}/metagwastoolkit.clump.sh ${PROJECTDIR}/metagwastoolkit.conf ${PROJECTDIR}/metagwastoolkit.files.list -# +# ${SCRIPTS}/metagwastoolkit.clump.sh ${CONFILE} ${FILESLIST} # echo "" # echo "SIXTH step: prepare and perform downstream analyses." # Note that rsIDs are expected! -# ${SCRIPTS}/metagwastoolkit.downstream.sh ${PROJECTDIR}/metagwastoolkit.conf ${PROJECTDIR}/metagwastoolkit.files.list -# +# ${SCRIPTS}/metagwastoolkit.downstream.sh ${CONFILE} ${FILESLIST} + +###################### EMMA DOWNSTREAM PIPELINE ###################### + +#### GWASLAB + +echo "SEVENTH step: perform downstream analysis and QC in GWASLAB." + + +bash ${SCRIPTS}/metagwastoolkit.gwaslab.sh ${CONFILE} ${FILESLIST} + + +echo "EIGHT step: prepare and perform downstream analyses tools - GWAS2COJO, LDSC, PolyFun, GSMR." + +### GWAS2COJO +MAX_VALUE=$(zcat ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/GWASCatalog/${PROJECTNAME}.b37.gwaslab.qc.ssf.tsv.gz | awk 'NR > 1 { if ($11 > max) max = $11 } END { print int(max) }') +echo "${PROJECTNAME} ${MAX_VALUE}" > ${PROJECTDIR}/${PROJECTNAME}.PHENOTYPES.txt + +mkdir -p ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/input + ${PYTHON3} /hpc/local/Rocky8/dhl_ec/software/gwas2cojo/gwas2cojo.py \ + --gen:build hg19 \ + --gen ${RESOURCES}/1kGp3v5b.ref.allfreq.noCN_noINS_noSS_noESV_noMultiAllelic.sumstats.txt.gz \ + --gwas ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/GWASCatalog/${PROJECTNAME}.b37.gwaslab.qc.ssf.tsv.gz \ + --gen:ident ID --gen:chr CHROM --gen:bp POS --gen:other REF --gen:effect ALT --gen:eaf ${POP}_AF \ +--gwas:chr chromosome --gwas:bp base_pair_location --gwas:other other_allele --gwas:effect effect_allele \ + --gwas:beta beta --gwas:se standard_error --gwas:p p_value \ + --gwas:freq effect_allele_frequency --gwas:n n --gwas:build hg19 \ + --out ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/input/${PROJECTNAME}.cojo \ + --report ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/input/${PROJECTNAME}.report + ${PYTHON3} /hpc/local/Rocky8/dhl_ec/software/gwas2cojo/gwas2cojo-verify.py ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/input/${PROJECTNAME}.cojo + gzip -vf ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/input/${PROJECTNAME}.cojo + gzip -vf ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/input/${PROJECTNAME}.report + +### LDSC, POLYFUN and GSMR +bash ${SCRIPTS}/metagwastoolkit.downstream.analysis.sh ${CONFILE} ${FILESLIST} + + # echo "" # echo "Converting filtered meta-analysis summary results using [gwas2cojo]." @@ -163,7 +232,7 @@ echo "THIRD step: meta-analysis." # --gwas ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/meta.results.${PROJECTNAME}.1Gp3.EUR.summary.txt.gz \ # --gen:ident ID --gen:chr CHROM --gen:bp POS --gen:other REF --gen:effect ALT --gen:eaf EUR_AF \ # --gwas:chr CHR --gwas:bp POS --gwas:other OTHERALLELE --gwas:effect CODEDALLELE \ -# --gwas:beta BETA_FIXED --gwas:se SE_FIXED --gwas:p P_FIXED \ +# --gwas:beta ${BETA} --gwas:se ${SE} --gwas:p ${PVALUE} \ # --gwas:freq CAF --gwas:n N_EFF --gwas:build hg19 \ # --fmid 0 --fclose 0 \ # --out ${PROJECTDIR}/${SUBPROJECTDIRNAME}/META/meta.results.${PROJECTNAME}.1Gp3.EUR.summary.gwas2cojo.txt \ diff --git a/pipeline.analysis.sh b/pipeline.analysis.sh new file mode 100644 index 0000000..421d48d --- /dev/null +++ b/pipeline.analysis.sh @@ -0,0 +1,266 @@ +#!/bin/bash + +# SLURM settings +#SBATCH --job-name=genius_chd # Job name +#SBATCH --mail-type=FAIL # Mail events (NONE, BEGIN, END, FAIL, ALL) +#SBATCH --mail-user=e.j.a.smulders-2@umcutrecht.nl # Where to send mail +#SBATCH --nodes=1 # Run on one node +#SBATCH --ntasks=1 # Run on a single CPU +#SBATCH --mem=128G # Job memory request +#SBATCH --gres=tmpspace:128G # the amount of temporary diskspace per node +#SBATCH --time=24:00:00 # Time limit hrs:min:sec +#SBATCH --output=genius_chd.analysis.out # Standard output and error log + +############################## +## DOWNSTREAM GWAS ANALYSIS ## +############################## + + +####### Input data of phenotypes and traits needs to be in cojo formatfor both LDSC and GSMR, GWASCatalog format for PolyFun ###### +####### check gwas2cojo.run.sh for file conversion ########################### + + + +python3_exe="/hpc/local/Rocky8/dhl_ec/software/mambaforge3/envs/polyfun/bin/python3" + +DATA="/hpc/dhl_ec/esmulders" +PROJECTNAME="PROJECTNAME" +SCRIPTS="${DATA}/SCRIPTS_own" + +data_analysed="PHENOTYPE" + +PHENOTYPE_FILE="${DATA}/${PROJECTNAME}.PHENOTYPES.txt" +TRAIT_FILE="${DATA}/${PROJECTNAME}.TRAITS.txt" + +POPULATION="EUR" +###LDSC +# path to map with ld +REF="${DATA}/references" +REF_LD="${REF}/eur_w_ld_chr/" +# path to merge allele snplist +SNPLIST="${DATA}/ldsc/w_hm3.snplist" + + +#polyfun +min_maf=0.005 +max_num_causal=1 +pip_cutoff=0.50 +CHUNKSIZE=500000 + + +#GSMR reference +GSMR_REF="/hpc/dhl_ec/esmulders/gsmr/gsmr_ref_data_eur.txt" + +#r="rsid" +#n="n" +#a="effect_allele" +#b="other_allele" +#v="p_value" +#f="effect_allele_frequency" + +RESOURCES=/hpc/local/Rocky8/dhl_ec/software/MetaGWASToolKit/RESOURCES +rsid="rsid" +n="n" +a1="a1" +a2="a2" +p="p" +eaf="eaf" + +# data preparation +if [ "$data_analysed" == "PHENOTYPE" ]; then + if [ -n "$PHENOTYPE_FILE" ]; then + sed -i 's/\r$//' "$PHENOTYPE_FILE" + PHENOTYPES=() + SAMPLESIZES=() + PATHS=() + while IFS=' ' read -r phenotype sample_size path; do + PHENOTYPES+=("$phenotype") + SAMPLESIZES+=("$sample_size") + PATHS+=("$path") + done < "$PHENOTYPE_FILE" + + if [ ${#PHENOTYPES[@]} -eq 0 ]; then + echo "Phenotype file is empty or not properly formatted." + exit 1 + fi + else + echo "Phenotype file (-p) is required." + exit 1 + fi + + for (( i=0; i<${#PHENOTYPES[@]}; i++ )); do + PHENOTYPE="${PHENOTYPES[i]}" + SAMPLE_SIZE="${SAMPLESIZES[i]}" + INPUT="${PATHS[i]}" + + mkdir -p "${DATA}/${PROJECTNAME}/${PHENOTYPE}" + mkdir -p "${DATA}/${PROJECTNAME}/${PHENOTYPE}/input" + echo "Phenotype: ${PHENOTYPE}" + echo "Sample Size: ${SAMPLE_SIZE}" + echo "Input file: ${INPUT}" + zcat "${DATA}/${PROJECTNAME}/${PHENOTYPE}/${INPUT}" | head + + # Parsing + Rscript ${SCRIPTS}/pipeline.parser.R -p ${DATA}/${PROJECTNAME}/${PHENOTYPE} -d ${INPUT} -o input + #GWASLAB + source /hpc/local/Rocky8/dhl_ec/software/mambaforge3/bin/activate gwaslab_env + python3 ${SCRIPTS}/pipeline.gwaslab.py -g ${PHENOTYPE} -d ${DATA}/${PROJECTNAME}/${PHENOTYPE} -i ${INPUT} -p ${POPULATION} -r ${REF} + #COJO + zcat "${DATA}/${PROJECTNAME}/${PHENOTYPE}/GWASCatalog/${PHENOTYPE}.b37.gwaslab.qc.ssf.tsv.gz" | head + mkdir -p ${DATA}/${PROJECTNAME}/${PHENOTYPE} + mkdir -p ${DATA}/${PROJECTNAME}/${PHENOTYPE}/input + mkdir -p ${DATA}/${PROJECTNAME}/${PHENOTYPE}/gsmr + mkdir -p ${DATA}/${PROJECTNAME}/${PHENOTYPE}/polyfun + mkdir -p ${DATA}/${PROJECTNAME}/${PHENOTYPE}/ldsc + ${python3_exe} /hpc/local/Rocky8/dhl_ec/software/gwas2cojo/gwas2cojo.py \ + --gen:build hg19 \ + --gen ${RESOURCES}/1kGp3v5b.ref.allfreq.noCN_noINS_noSS_noESV_noMultiAllelic.sumstats.txt.gz \ + --gwas ${DATA}/${PROJECTNAME}/${PHENOTYPE}/GWASCatalog/${PHENOTYPE}.b37.gwaslab.qc.ssf.tsv.gz \ + --gen:ident ID --gen:chr CHROM --gen:bp POS --gen:other REF --gen:effect ALT --gen:eaf EUR_AF \ + --gwas:chr chromosome --gwas:bp base_pair_location --gwas:other other_allele --gwas:effect effect_allele \ + --gwas:beta beta --gwas:se standard_error --gwas:p p_value \ + --gwas:freq effect_allele_frequency --gwas:n n --gwas:build hg19 \ + --out ${DATA}/${PROJECTNAME}/${PHENOTYPE}/input/${PHENOTYPE}.cojo + ${python3_exe} /hpc/local/Rocky8/dhl_ec/software/gwas2cojo/gwas2cojo-verify.py ${DATA}/${PROJECTNAME}/${PHENOTYPE}/input/${PHENOTYPE}.cojo + gzip -vf ${DATA}/${PROJECTNAME}/${PHENOTYPE}/input/${PHENOTYPE}.cojo + done +fi + +if [ "$data_analysed" == "PHENOTYPE" ]; then + if [ -n "$PHENOTYPE_FILE" ]; then + PHENOTYPES=() + PATHS=() + while IFS=' ' read -r phenotype sample_size; do + PHENOTYPES+=("$phenotype") + SAMPLESIZES+=("$sample_size") + done < "$PHENOTYPE_FILE" + if [ ${#PHENOTYPES[@]} -eq 0 ]; then + echo "Phenotype file is empty or not properly formatted." + exit 1 + fi + else + echo "Phenotype file (-p) is required." + exit 1 + fi + for (( i=0; i<${#PHENOTYPES[@]}; i++ )); do + PHENOTYPE="${PHENOTYPES[i]}" + PHENOPATH="${PATHS[i]}" + echo "${PHENOTYPE}" + echo "${PHENOPATH}" + mkdir -p ${DATA}/${PROJECTNAME}/${PHENOTYPE} + mkdir -p ${DATA}/${PROJECTNAME}/${PHENOTYPE}/input + mkdir -p ${DATA}/${PROJECTNAME}/${PHENOTYPE}/gsmr + mkdir -p ${DATA}/${PROJECTNAME}/${PHENOTYPE}/polyfun + mkdir -p ${DATA}/${PROJECTNAME}/${PHENOTYPE}/ldsc + ${python3_exe} /hpc/local/Rocky8/dhl_ec/software/gwas2cojo/gwas2cojo.py \ + --gen:build hg19 \ + --gen ${RESOURCES}/1kGp3v5b.ref.allfreq.noCN_noINS_noSS_noESV_noMultiAllelic.sumstats.txt.gz \ + --gwas ${DATA}/${PROJECTNAME}/${PHENOTYPE}/parsed/${INPUT} \ + --gen:ident ID --gen:chr CHROM --gen:bp POS --gen:other REF --gen:effect ALT --gen:eaf EUR_AF \ + --gwas:chr chromosome --gwas:bp base_pair_location --gwas:other other_allele --gwas:effect effect_allele \ + --gwas:beta beta --gwas:se standard_error --gwas:p p_value \ + --gwas:freq effect_allele_frequency --gwas:n n --gwas:build hg19 \ + --out ${DATA}/${PROJECTNAME}/${PHENOTYPE}/input/${PHENOTYPE}.cojo + ${python3_exe} /hpc/local/Rocky8/dhl_ec/software/gwas2cojo/gwas2cojo-verify.py ${DATA}/${PROJECTNAME}/${PHENOTYPE}/input/${PHENOTYPE}.cojo + gzip -vf ${DATA}/${PROJECTNAME}/${PHENOTYPE}/input/${PHENOTYPE}.cojo + done +fi + + +#LDSC +if [ "$data_analysed" == "PHENOTYPE" ]; then + if [ -n "$PHENOTYPE_FILE" ]; then + PHENOTYPES=() + #PATHS=() + while IFS=' ' read -r phenotype sample_size; do + PHENOTYPES+=("$phenotype") + SAMPLESIZES+=("$sample_size") + PATHS+=("$path") + done < "$PHENOTYPE_FILE" + if [ ${#PHENOTYPES[@]} -eq 0 ]; then + echo "Phenotype file is empty or not properly formatted." + exit 1 + fi + else + echo "Phenotype file (-p) is required." + exit 1 + fi + for (( i=0; i<${#PHENOTYPES[@]}; i++ )); do + PHENOTYPE="${PHENOTYPES[i]}" + #PHENOPATH="${PATHS[i]}" + #echo "${PHENOTYPE}" + sbatch ${DATA}/ldsc/ldsc.run.sh -w "${data_analysed}" -d "${DATA}" -j "${PROJECTNAME}" -p "${PHENOTYPE}" -t "${TRAIT_FILE}" -l "${REF}/eur_w_ld_chr/" -h "${SNPLIST}" -r ${rsid} -n ${n} -a ${a1} -b ${a2} -v ${p} -f ${eaf} -c ${CHUNKSIZE} + done +fi + +Check if trait file is provided and read the contents if data_analysed is TRAIT +if [ "$data_analysed" == "TRAIT" ] && [ -n "$TRAIT_FILE" ]; then + TRAITS=() + PATHS=() + while IFS= read -r line || [ -n "$line" ]; do + # Use read to parse line into trait and path + read -r trait path <<< "$line" + TRAITS+=("$trait") + PATHS+=("$path") + done < "$TRAIT_FILE" + + if [ ${#TRAITS[@]} -eq 0 ]; then + echo "Trait file is empty or not properly formatted." + exit 1 + fi + + for (( i=0; i<${#TRAITS[@]}; i++ )); do + TRAIT="${TRAITS[i]}" + TRAITPATH="${PATHS[i]}" + echo "Analyzing trait : ${TRAIT}" + sbatch ${DATA}/ldsc/ldsc.run.sh -w "${data_analysed}" -d "${DATA}" -j "${PROJECTNAME}" -p "${PHENOTYPE_FILE}" -t "${TRAIT}" -i "${TRAITPATH}" -l "${REF}/eur_w_ld_chr/" -h "${SNPLIST}" -r ${rsid} -n ${n} -a ${a1} -b ${a2} -v ${p} -f ${eaf} -c ${CHUNKSIZE} + done +fi + +#PolyFun +if [ "$data_analysed" == "PHENOTYPE" ]; then + if [ -n "$PHENOTYPE_FILE" ]; then + PHENOTYPES=() + SAMPLESIZES=() + while IFS=' ' read -r phenotype sample_size; do + PHENOTYPES+=("$phenotype") + SAMPLESIZES+=("$sample_size") + done < "$PHENOTYPE_FILE" + if [ ${#PHENOTYPES[@]} -eq 0 ]; then + echo "Phenotype file is empty or not properly formatted." + exit 1 + fi + else + echo "Phenotype file (-p) is required." + exit 1 + fi + for (( i=0; i<${#PHENOTYPES[@]}; i++ )); do + PHENOTYPE="${PHENOTYPES[i]}" + SAMPLESIZE="${SAMPLESIZES[i]}" + sbatch ${DATA}/polyfun/polyfun.run.sh -p "${PHENOTYPE}" -s "${SAMPLESIZE}" -d "${DATA}" -j "${PROJECTNAME}" -l "${REF}" -m "${min_maf}" -x "${max_num_causal}" -c "${pip_cutoff}" + done +fi + +#GSMR +if [ "$data_analysed" == "PHENOTYPE" ]; then + if [ -n "$PHENOTYPE_FILE" ]; then + PHENOTYPES=() + while IFS=' ' read -r phenotype sample_size; do + PHENOTYPES+=("$phenotype") + done < "$PHENOTYPE_FILE" + + if [ ${#PHENOTYPES[@]} -eq 0 ]; then + echo "Phenotype file is empty or not properly formatted." + exit 1 + fi + else + echo "Phenotype file (-p) is required." + exit 1 + fi + + for (( i=0; i<${#PHENOTYPES[@]}; i++ )); do + PHENOTYPE="${PHENOTYPES[i]}" + echo "${PHENOTYPE}" + sbatch ${DATA}/gsmr/gsmr.analysis.sh -t "${TRAIT_FILE}" -d "${DATA}" -j "${PROJECTNAME}" -p "${PHENOTYPE}" -r "${GSMR_REF}" + done +fi \ No newline at end of file