5_7_meta_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n\\njupyter nbconvert 5_association.ipynb --to script\\npython 5_association.py albumin 2 0\\n\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "import logging\n",
    "import re\n",
    "import pathlib\n",
    "\n",
    "import datetime\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pyplink import PyPlink\n",
    "import seaborn as sns\n",
    "\n",
    "import statsmodels.api as sm\n",
    "\n",
    "import matplotlib.patches as patches\n",
    "import matplotlib\n",
    "\n",
    "from basic_tools import *\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "jupyter nbconvert 5_association.ipynb --to script\n",
    "python 5_association.py albumin 2 0\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plink_KCHIP_HLA_AA_SNP_1000G=PyPlink(plink_KCHIP_HLA_AA_SNP_1000G_path)\n",
    "plink_KCHIP_HLA_AA_SNP_1000G_fam=plink_KCHIP_HLA_AA_SNP_1000G.get_fam().astype({'fid':str,'iid':str}).rename(columns={'fid':'FID','iid':'IID'})\n",
    "plink_KCHIP_HLA_AA_SNP_1000G_bim=plink_KCHIP_HLA_AA_SNP_1000G.get_bim()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gene_bed_path='data/mart_export.txt'\n",
    "#gene_bed=pd.read_csv(gene_bed_path,sep='\\t')\n",
    "\n",
    "#ENSG00000206337"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gene_bed[gene_bed['Gene stable ID']=='ENSG00000206337']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(209, 33)\n",
      "(207, 33)\n",
      "(207, 33)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ch6845/tools/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "gene_bed_path='data/mart_export.txt'\n",
    "gene_bed=pd.read_csv(gene_bed_path,sep='\\t')\n",
    "gene_bed=gene_bed.drop(columns='Exon stable ID')\n",
    "gene_bed=gene_bed[(gene_bed['Gene start (bp)']>=plink_KCHIP_HLA_AA_SNP_1000G_bim.pos.min())&(gene_bed['Gene end (bp)']<=plink_KCHIP_HLA_AA_SNP_1000G_bim.pos.max())]\n",
    "gene_bed=gene_bed.merge(right=pd.read_csv('data/mart_export_grch38.txt',sep='\\t')[['Gene stable ID','Gene start (bp)','Gene end (bp)']].rename(columns=(lambda x:x+'38')),left_on='Gene stable ID',right_on='Gene stable ID38',how='left',)\n",
    "\n",
    "gene_bed=gene_bed[(gene_bed['Transcript type']=='protein_coding')]\n",
    "gene_bed=gene_bed[~gene_bed.duplicated(['Gene name','Gene start (bp)','Gene end (bp)'])]\n",
    "print(gene_bed.shape)\n",
    "gene_bed=gene_bed[~gene_bed.duplicated(['Gene name'])]\n",
    "print(gene_bed.shape)\n",
    "\n",
    "print(gene_bed.shape)\n",
    "\n",
    "gene_assign=plink_KCHIP_HLA_AA_SNP_1000G_bim[['pos']]\n",
    "\n",
    "for idx,row in gene_bed.iterrows():\n",
    "    gene_assign[row['Gene name']]=0\n",
    "    \n",
    "for idx,row in gene_bed.iterrows():    \n",
    "    gene_assign[row['Gene name']][(gene_assign['pos']>=row['Gene start (bp)'])&(gene_assign['pos']<=row['Gene end (bp)'])]=1\n",
    "\n",
    "#gene_assign.columns=gene_assign.columns.str.replace('HLA-','HLA_')        \n",
    "    \n",
    "HLA_names=np.unique([i[0].split('_')[1] for i in plink_KCHIP_HLA_AA_SNP_1000G_bim[plink_KCHIP_HLA_AA_SNP_1000G_bim.index.str.contains('HLA_')].index.str.split('*')])\n",
    "\n",
    "for HLA_name in HLA_names:\n",
    "    gene_select=gene_assign[gene_assign.index.str.contains('HLA_'+HLA_name)|gene_assign.index.str.contains('SNPS_'+HLA_name)|gene_assign.index.str.contains('AA_'+HLA_name)]#print(gene_select.sort_values('pos').iloc[0],gene_select.sort_values('pos').iloc[-1])\n",
    "    HLA_name='HLA-{}'.format(HLA_name)\n",
    "    gene_assign[HLA_name][(gene_assign['pos']>=gene_select['pos'].min())&(gene_assign['pos']<=gene_select['pos'].max())]=1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gene_bed38[['Gene stable ID','Gene start (bp)','Gene end (bp)']].rename(columns=lambda x:x+'aa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gene_bed38[['Gene stable ID','Gene start (bp)','Gene end (bp)']].rename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ch6845/tools/miniconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3049: DtypeWarning: Columns (9,11,12,13,23,27,29,30,31) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n",
      "/home/ch6845/tools/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "/home/ch6845/tools/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:17: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "catalog=pd.read_csv('data/ebi_alternative_0708.tsv',sep='\\t')\n",
    "catalog['DISEASE/TRAIT']=catalog['DISEASE/TRAIT'].str.lower()\n",
    "catalog['MAPPED_TRAIT']=catalog['MAPPED_TRAIT'].str.lower()\n",
    "catalog_chr6=catalog[catalog['CHR_ID']=='6']\n",
    "catalog_chr6['CHR_POS']=catalog_chr6['CHR_POS'].astype(int)\n",
    "catalog_chr6.columns\n",
    "\n",
    "catalog_chr6_short=catalog_chr6.drop(columns=['DATE ADDED TO CATALOG','REPLICATION SAMPLE SIZE','DATE','REGION','FIRST AUTHOR','JOURNAL','CHR_ID','LINK','MERGED','RISK ALLELE FREQUENCY','PVALUE_MLOG','STRONGEST SNP-RISK ALLELE','95% CI (TEXT)','OR or BETA','PLATFORM [SNPS PASSING QC]','MAPPED_TRAIT_URI','GENOTYPING TECHNOLOGY','STUDY ACCESSION'])\n",
    "catalog_chr6_short=catalog_chr6_short[(catalog_chr6_short['CHR_POS'].astype(int)>plink_KCHIP_HLA_AA_SNP_1000G_bim.pos.min()) & (catalog_chr6_short['CHR_POS']<plink_KCHIP_HLA_AA_SNP_1000G_bim.pos.max())]\n",
    "\n",
    "len(catalog_chr6_short['DISEASE/TRAIT'].unique()),len(catalog_chr6_short['MAPPED_TRAIT'].unique())\n",
    "\n",
    "catalog_chr6_short=catalog_chr6_short[catalog_chr6_short['P-VALUE'].astype(float)<5e-8]\n",
    "\n",
    "japan_data=pd.read_csv('data/japan.tsv',sep='\\t',index_col=0)\n",
    "japan_data['DISEASE/TRAIT']=japan_data['DISEASE/TRAIT'].str.lower()\n",
    "catalog_chr6_short=pd.concat([catalog_chr6_short,japan_data])\n",
    "\n",
    "#catalog_chr6_short['GENE_CUSTOM'][(catalog_chr6_short['PUBMEDID']==30692682)]=catalog_chr6_short['CHR_POS'][(catalog_chr6_short['PUBMEDID']==30692682)].map(lambda x: pos_to_gene(x,mode='37'))    \n",
    "\n",
    "#catalog_chr6_short[(catalog_chr6_short['PUBMEDID']==30692682)]#['GENE_CUSTOM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['type i diabetes mellitus', 'ulcerative colitis', 'lung carcinoma',\n",
       "       'chronic lymphocytic leukemia', 'protein measurement',\n",
       "       'lung adenocarcinoma', \"crohn's disease\", 'psoriasis',\n",
       "       'body height', 'pulmonary function measurement, fev/fec ratio',\n",
       "       'biliary liver cirrhosis', 'rheumatoid arthritis',\n",
       "       'membranous glomerulonephritis',\n",
       "       'hepatitis c induced liver cirrhosis', 'hepatocellular carcinoma',\n",
       "       'multiple sclerosis', 'drug-induced liver injury',\n",
       "       'hiv-1 infection', 'asthma', 'nasopharyngeal neoplasm',\n",
       "       'pulmonary function measurement, forced expiratory volume',\n",
       "       'age-related macular degeneration',\n",
       "       'stevens-johnson syndrome, toxic epidermal necrolysis',\n",
       "       'renal system measurement', 'lipid measurement',\n",
       "       'inflammatory bowel disease', 'platelet count',\n",
       "       'seasonal allergic rhinitis', 'leukocyte count',\n",
       "       \"ulcerative colitis, crohn's disease\", 'vitiligo',\n",
       "       'systemic lupus erythematosus', 'juvenile idiopathic arthritis',\n",
       "       'serum igg glycosylation measurement', 'ankylosing spondylitis',\n",
       "       'celiac disease', 'testicular carcinoma', \"parkinson's disease\",\n",
       "       'hepatitis b virus infection', 'bipolar disorder',\n",
       "       'response to interferon beta',\n",
       "       'low density lipoprotein cholesterol measurement',\n",
       "       'total cholesterol measurement', 'triglyceride measurement',\n",
       "       'high density lipoprotein cholesterol measurement',\n",
       "       'schizophrenia', 'systemic scleroderma', 'leprosy', 'lymphoma',\n",
       "       'body weight', 'neoplasm of mature b-cells', 'osteosarcoma',\n",
       "       'pulmonary function measurement, smoking behaviour measurement, fev/fec ratio',\n",
       "       'lumbar disc degeneration', 'hpv seropositivity',\n",
       "       'neonatal systemic lupus erythematosus',\n",
       "       'myeloperoxidase measurement', \"behcet's syndrome\",\n",
       "       'dengue hemorrhagic fever', 'monocyte count', 'eosinophil count',\n",
       "       'atopic eczema', 'wet macular degeneration',\n",
       "       'atrophic macular degeneration',\n",
       "       'chronic hepatitis c virus infection', 'age at menopause',\n",
       "       \"alzheimer's disease\", 'serum ige measurement',\n",
       "       'cervical carcinoma', 'immune system disease',\n",
       "       'antibody measurement, epstein-barr virus infection',\n",
       "       'attention deficit hyperactivity disorder, unipolar depression, schizophrenia, autism spectrum disorder, bipolar disorder',\n",
       "       'multiple sclerosis, oligoclonal band measurement',\n",
       "       'kidney disease', 'hypothyroidism', 'iga glomerulonephritis',\n",
       "       'coronary heart disease', 'phosphorus measurement',\n",
       "       'response to vaccine, cytokine measurement', 'hodgkins lymphoma',\n",
       "       'response to reverse transcriptase inhibitor', 'graves disease',\n",
       "       'systolic blood pressure', 'hypertension',\n",
       "       'diastolic blood pressure',\n",
       "       'body mass index, fasting blood insulin measurement',\n",
       "       'osteoarthritis, knee',\n",
       "       'anti-neutrophil antibody associated vasculitis', 'allergy',\n",
       "       'narcolepsy with cataplexy', 'body mass index',\n",
       "       'metabolic syndrome', 'chronic hepatitis b virus infection',\n",
       "       'response to vaccine', 'phospholipid measurement',\n",
       "       'prostate carcinoma', 'alopecia areata',\n",
       "       'mucocutaneous lymph node syndrome',\n",
       "       'granulomatosis with polyangiitis', 'type ii diabetes mellitus',\n",
       "       'lymphocyte count', 'sjogren syndrome',\n",
       "       'marginal zone b-cell lymphoma',\n",
       "       'malignant epithelial tumor of ovary',\n",
       "       'monocyte percentage of leukocytes', 'myopia',\n",
       "       'joint hypermobility measurement', 'reticulocyte count',\n",
       "       'hip circumference',\n",
       "       'treatment refractory schizophrenia, drug-induced agranulocytosis, response to clozapine',\n",
       "       'cutaneous psoriasis measurement, psoriasis', 'psoriasis vulgaris',\n",
       "       'psoriatic arthritis', 'takayasu arteritis', 'multiple myeloma',\n",
       "       'multiple myeloma, monoclonal gammopathy',\n",
       "       'trichloroethylene-induced hypersensitivity', 'myositis',\n",
       "       'primary biliary cirrhosis', 'platelet crit',\n",
       "       'granulocyte percentage of myeloid white cells',\n",
       "       'susceptibility to childhood ear infection measurement',\n",
       "       'tonsillectomy risk measurement', 'hemoglobin measurement',\n",
       "       'platelet component distribution width',\n",
       "       'eosinophil percentage of granulocytes',\n",
       "       'forced expiratory volume', 'basophil count', 'breast carcinoma',\n",
       "       'igg galactosylation measurement',\n",
       "       'bmi-adjusted waist circumference',\n",
       "       'susceptibility to mycobacterium tuberculosis infection measurement',\n",
       "       'susceptibility to plantar warts measurement',\n",
       "       'nodular sclerosis hodgkin lymphoma',\n",
       "       'hodgkins lymphoma, mixed cellularity',\n",
       "       'smoking behavior, bmi-adjusted waist circumference',\n",
       "       'testicular germ cell tumor', 'depressive symptom measurement',\n",
       "       'smoking behavior, body mass index',\n",
       "       'systemic juvenile idiopathic arthritis, polyarticular juvenile idiopathic arthritis, rheumatoid factor negative, oligoarticular juvenile idiopathic arthritis',\n",
       "       'susceptibility to shingles measurement',\n",
       "       'susceptibility to chickenpox measurement',\n",
       "       'susceptibility to cold sores measurement',\n",
       "       'hepatitis c virus infection, hepatocellular carcinoma',\n",
       "       'susceptibility to mumps measurement',\n",
       "       'schizophrenia, autism spectrum disorder',\n",
       "       'smoking status measurement, lung carcinoma',\n",
       "       'susceptibility to mononucleosis measurement',\n",
       "       'susceptibility to hepatitis b infection measurement',\n",
       "       'susceptibility to strep throat measurement',\n",
       "       'susceptibility to scarlet fever measurement',\n",
       "       'susceptibility to pneumonia measurement',\n",
       "       'bmi-adjusted waist-hip ratio, physical activity measurement',\n",
       "       'physical activity measurement, body mass index',\n",
       "       'complement factor b measurement',\n",
       "       'mhc class i polypeptide-related sequence a measurement',\n",
       "       'mhc class i polypeptide-related sequence b measurement',\n",
       "       'bmi-adjusted waist circumference, physical activity measurement',\n",
       "       'colorectal health',\n",
       "       'bmi-adjusted waist-hip ratio, sex interaction measurement, age at assessment',\n",
       "       'gp41 c34 peptide, hiv measurement',\n",
       "       'trefoil factor 3 measurement',\n",
       "       'neutrophil collagenase measurement', 'granzyme a measurement',\n",
       "       'complement c4 measurement',\n",
       "       'smoking status measurement, systolic blood pressure',\n",
       "       'smoking status measurement, diastolic blood pressure',\n",
       "       'hodgkins lymphoma, multiple myeloma, chronic lymphocytic leukemia',\n",
       "       'bmi-adjusted waist-hip ratio',\n",
       "       'alpha-2-macroglobulin receptor-associated protein measurement',\n",
       "       'complement c4b measurement',\n",
       "       'rheumatoid arthritis, smoking status measurement, acpa-positive rheumatoid arthritis',\n",
       "       'birth weight', 'smoking behavior, bmi-adjusted waist-hip ratio',\n",
       "       'squamous cell lung carcinoma',\n",
       "       'mosquito bite reaction itch intensity measurement',\n",
       "       'mosquito bite reaction itch intensity measurement, mosquito bite reaction size measurement',\n",
       "       'mosquito bite reaction size measurement',\n",
       "       'perceived unattractiveness to mosquitos measurement',\n",
       "       'acute graft vs. host disease', 'wellbeing measurement',\n",
       "       'neuroticism measurement', 'sclerosing cholangitis',\n",
       "       'frontotemporal dementia', 'cd4:cd8 lymphocyte ratio',\n",
       "       'cd8-positive t-lymphocyte count', 'autoimmune hepatitis',\n",
       "       'infant body height', 'blood metabolite measurement',\n",
       "       'sleep duration', 'typhoid fever',\n",
       "       'laryngeal squamous cell carcinoma',\n",
       "       'antinuclear antibody measurement', 'aids',\n",
       "       'beta-2 microglobulin measurement', 'sarcoidosis',\n",
       "       'allergic sensitization measurement', 'anterior uveitis',\n",
       "       'anterior uveitis, ankylosing spondylitis',\n",
       "       'narcolepsy with cataplexy, age at onset', \"barrett's esophagus\",\n",
       "       'age at menarche',\n",
       "       'response to thiopurine, thiopurine immunosuppressant-induced pancreatitis, inflammatory bowel disease',\n",
       "       'total blood protein measurement', 'white wine liking measurement',\n",
       "       'autoimmune thyroid disease, type i diabetes mellitus',\n",
       "       'cutaneous lupus erythematosus', 'visceral leishmaniasis',\n",
       "       'chronic obstructive pulmonary disease, surfactant protein d measurement',\n",
       "       'chronic hepatitis c virus infection, cryoglobulinemia',\n",
       "       'seasonal allergic rhinitis, asthma',\n",
       "       'bmi-adjusted hip circumference', 'diffuse large b-cell lymphoma',\n",
       "       'vogt-koyanagi-harada disease', 'waist circumference',\n",
       "       'peanut allergy measurement', 'multiple sclerosis, igg index',\n",
       "       'response to triamcinolone acetonide, intraocular pressure change measurement',\n",
       "       'response to thioamide, graves disease, drug-induced agranulocytosis',\n",
       "       'response to anti-thyroid drug, drug-induced agranulocytosis',\n",
       "       'response to drug, drug-induced agranulocytosis',\n",
       "       'migraine disorder',\n",
       "       'hiv-1 infection, hiv viral set point measurement',\n",
       "       'collagenous colitis',\n",
       "       'epstein barr virus nuclear antigen 1 igg measurement',\n",
       "       'neutrophil count', 'influenza a (h1n1)',\n",
       "       'staphylococcus aureus infection', 'srage measurement',\n",
       "       'cystic fibrosis, lung disease severity measurement',\n",
       "       'cancer, response to pazopanib, serum alanine aminotransferase measurement',\n",
       "       'late-onset myasthenia gravis', 'fev/fec ratio',\n",
       "       'chronic obstructive pulmonary disease',\n",
       "       'rheumatoid arthritis, anti-citrullinated protein antibody seropositivity, rheumatoid factor seropositivity measurement',\n",
       "       'fibrinogen measurement', 'neutrophil percentage of granulocytes',\n",
       "       'basophil count, eosinophil count', 'myeloid white cell count',\n",
       "       'neutrophil count, basophil count',\n",
       "       'acute-on-chronic liver failure, hepatitis b virus infection',\n",
       "       'red blood cell distribution width', 'gas trapping measurement',\n",
       "       'emphysema imaging measurement',\n",
       "       'atrophic macular degeneration, age-related macular degeneration, wet macular degeneration',\n",
       "       'squamous cell carcinoma', 'mean corpuscular volume',\n",
       "       'eosinophil percentage of leukocytes', 'hematocrit',\n",
       "       'triglyceride measurement, c-reactive protein measurement',\n",
       "       'c-reactive protein measurement, high density lipoprotein cholesterol measurement',\n",
       "       'low density lipoprotein cholesterol measurement, c-reactive protein measurement',\n",
       "       'total cholesterol measurement, c-reactive protein measurement',\n",
       "       'response to thioamide, drug-induced agranulocytosis',\n",
       "       'mean corpuscular hemoglobin',\n",
       "       'lymphocyte percentage of leukocytes', 'mean platelet volume',\n",
       "       'basophil percentage of granulocytes',\n",
       "       'basophil percentage of leukocytes',\n",
       "       'systemic juvenile idiopathic arthritis',\n",
       "       'pharynx cancer, oral cavity cancer', 'oropharynx cancer',\n",
       "       'mean corpuscular hemoglobin concentration',\n",
       "       'neutrophil count, eosinophil count', 'granulocyte count',\n",
       "       'neutrophil percentage of leukocytes', 'erythrocyte count',\n",
       "       'natural cytotoxicity triggering receptor 3 measurement',\n",
       "       'grip strength measurement', 'high altitude adaptation',\n",
       "       'age-related macular degeneration, disease progression measurement',\n",
       "       \"esophageal adenocarcinoma, barrett's esophagus\", 'intelligence',\n",
       "       'coronary artery disease', 'tuberculosis', 'endometriosis',\n",
       "       'pulse pressure measurement', 'physical activity measurement',\n",
       "       'blood protein measurement', 'keratinocyte carcinoma',\n",
       "       \"crohn's disease, disease prognosis measurement\",\n",
       "       'atrial fibrillation', 'allergy, asthma',\n",
       "       'glomerular filtration rate', 'temporal arteritis',\n",
       "       'multiple sclerosis, systolic blood pressure',\n",
       "       'thyroid stimulating hormone measurement',\n",
       "       'cerebrospinal fluid biomarker measurement',\n",
       "       'basal cell carcinoma', 'smoking initiation',\n",
       "       '17-hydroxyprogesterone measurement',\n",
       "       'gastroesophageal reflux disease',\n",
       "       'colorectal cancer, colorectal adenoma',\n",
       "       'serum non-albumin protein measurement',\n",
       "       'feeling emotionally hurt measurement', 'worry measurement',\n",
       "       'creatinine measurement',\n",
       "       \"alzheimer's disease, family history of alzheimer’s disease\",\n",
       "       'bilirubin measurement', 'chloride measurement',\n",
       "       'aspartate aminotransferase measurement',\n",
       "       'serum alanine aminotransferase measurement',\n",
       "       'alkaline phosphatase measurement',\n",
       "       'l lactate dehydrogenase measurement',\n",
       "       'mood instability measurement', 'feeling miserable measurement',\n",
       "       'guilt measurement', 'heart rate response to exercise',\n",
       "       'childhood onset asthma',\n",
       "       'autoimmune hepatitis, sclerosing cholangitis',\n",
       "       'feeling nervous measurement', 'unipolar depression',\n",
       "       'unipolar depression, schizophrenia',\n",
       "       'alcohol dependence measurement',\n",
       "       'glomerular filtration rate, diabetes mellitus',\n",
       "       'alcohol drinking, high density lipoprotein cholesterol measurement',\n",
       "       'parental longevity', 'triglyceride measurement, alcohol drinking',\n",
       "       'age at onset, asthma',\n",
       "       'low density lipoprotein cholesterol measurement, alcohol drinking',\n",
       "       'multiple sclerosis, response to interferon beta, anti-drug antibody measurement',\n",
       "       'heel bone mineral density', 'henoch-schoenlein purpura',\n",
       "       'epstein barr virus nuclear antigen-1 seropositivity',\n",
       "       'anti-epstein barr virus antibody measurement', 'dermatomyositis',\n",
       "       'selective iga deficiency disease',\n",
       "       'schizophrenia, bipolar disorder, response to lithium ion',\n",
       "       'response to sulfasalazine, drug-induced agranulocytosis',\n",
       "       'c-peptide measurement, type i diabetes mellitus',\n",
       "       'albumin:globulin ratio measurement', 'serum albumin measurement',\n",
       "       'handedness', 'nephrotic syndrome', 'health study participation',\n",
       "       'social interaction measurement', 'pemphigus',\n",
       "       'pemphigus vulgaris', 'post-traumatic stress disorder',\n",
       "       'visceral adipose tissue measurement',\n",
       "       'base metabolic rate measurement',\n",
       "       'urinary albumin to creatinine ratio',\n",
       "       'non-small cell lung carcinoma', 'sphingolipid measurement',\n",
       "       'low density lipoprotein cholesterol measurement, alcohol consumption measurement',\n",
       "       'alcohol consumption measurement, high density lipoprotein cholesterol measurement',\n",
       "       'triglyceride measurement, alcohol consumption measurement',\n",
       "       'attention deficit hyperactivity disorder, asthma',\n",
       "       'unipolar depression, asthma',\n",
       "       'rheumatoid arthritis, joint damage measurement',\n",
       "       'microalbuminuria, type i diabetes mellitus',\n",
       "       'body fat percentage', 'peripheral arterial disease',\n",
       "       'wheat allergic reaction', 'uterine fibroid',\n",
       "       'pulmonary arterial hypertension', 'smoking behavior',\n",
       "       'risk-taking behaviour', 'eosinophilic esophagitis',\n",
       "       'bipolar disorder, body mass index', 'factor viii measurement',\n",
       "       'lean body mass', 'risky sexual behaviour measurement',\n",
       "       'hair color', 'remission, hepatitis c virus infection',\n",
       "       'von willebrand factor measurement', 'churg-strauss syndrome',\n",
       "       'balding measurement', 'female reproductive system disease',\n",
       "       'triglyceride measurement, physical activity',\n",
       "       'body fat distribution', 'colorectal cancer',\n",
       "       'severe aplastic anemia', 'appendicular lean mass',\n",
       "       'advanced glycation end-product measurement',\n",
       "       'interleukin 16 measurement', 'atopic asthma',\n",
       "       'unipolar depression, mood disorder', 'ccl19 measurement',\n",
       "       'cataract', 'fasting blood glucose measurement',\n",
       "       'serum metabolite measurement', 'intraocular pressure measurement',\n",
       "       'bone density',\n",
       "       'hepatitis b virus infection, hepatocellular carcinoma',\n",
       "       'allergic rhinitis', 'dermatomyositis, juvenile dermatomyositis',\n",
       "       'polymyositis', 'angiopoietin-1 receptor measurement',\n",
       "       'alcohol consumption measurement',\n",
       "       'graves disease, thyrotoxic periodic paralysis',\n",
       "       'interleukin-1 beta measurement', 'dna methylation',\n",
       "       'c-reactive protein measurement', 'uric acid measurement',\n",
       "       'peptic ulcer and gastro-oesophageal reflux disease (gord) drug use measurement',\n",
       "       'drugs used in diabetes use measurement',\n",
       "       'calcium channel blocker use measurement', 'adult onset asthma',\n",
       "       'white matter integrity', nan,\n",
       "       'smoking status measurement, forced expiratory volume',\n",
       "       'abnormality of refraction', 'body height at birth',\n",
       "       'pemphigus foliaceus', 'smoking status measurement',\n",
       "       'chronotype measurement',\n",
       "       'adult onset asthma, type ii diabetes mellitus',\n",
       "       'body mass index, asthma', 'self reported educational attainment',\n",
       "       'alcohol use disorder measurement, alcohol dependence',\n",
       "       'resting heart rate, chronic obstructive pulmonary disease',\n",
       "       'hmg coa reductase inhibitor use measurement',\n",
       "       'drugs affecting bone structure and mineralization use measurement',\n",
       "       'aspirin use measurement', 'anilide use measurement',\n",
       "       'immunosuppressant use measurement',\n",
       "       'non-steroidal anti-inflammatory and antirheumatic product use measurement',\n",
       "       'inhalant adrenergic use measurement',\n",
       "       'thyroid preparation use measurement',\n",
       "       'glucocorticoid use measurement', 'antihistamine use measurement',\n",
       "       'hiv-1 infection, viral load', 'urate measurement',\n",
       "       'serum iga measurement', 'serum immunoglobulin measurement',\n",
       "       'immunoglobulin isotype switching measurement',\n",
       "       'serum igm measurement', 'serum igg measurement',\n",
       "       'carpal tunnel syndrome',\n",
       "       'latent autoimmune diabetes in adults, type ii diabetes mellitus',\n",
       "       'type i diabetes mellitus, latent autoimmune diabetes in adults',\n",
       "       'birth weight, parental genotype effect measurement',\n",
       "       'neuromyelitis optica', 'aqp4-igg-positive neuromyelitis optica',\n",
       "       'age at onset, myopia', 'eczema, allergic rhinitis',\n",
       "       'eczema, allergic rhinitis, asthma',\n",
       "       'white matter microstructure measurement', 'vital capacity',\n",
       "       'nicotine dependence symptom count', 'insomnia measurement',\n",
       "       'rosacea severity measurement', 'anxiety', 'telomere length',\n",
       "       'age at onset, myopia, refractive error measurement', 'eczema',\n",
       "       'qrs duration', 'cups of coffee per day measurement',\n",
       "       'frontal fibrosing alopecia',\n",
       "       'primary biliary cirrhosis, antinuclear antibody measurement',\n",
       "       'oral ulcer', 'herpes zoster',\n",
       "       'anti-rubella virus igg measurement',\n",
       "       'body weights and measures, body height',\n",
       "       'body weights and measures', 'obese body mass index status',\n",
       "       'self rated health', 'chronic rhinosinusitis',\n",
       "       'nasal cavity polyp', 'neurotic disorder',\n",
       "       'triple-negative breast cancer', 'multisite chronic pain',\n",
       "       'severe cutaneous adverse reaction, response to carbamazepine',\n",
       "       'coffee consumption measurement',\n",
       "       'schizophrenia, intelligence, self reported educational attainment',\n",
       "       'anti-anoctamin 2 antibody measurement',\n",
       "       'neutrophil count, mean platelet volume, leukocyte count, erythrocyte count, mean corpuscular hemoglobin concentration, mean corpuscular volume, monocyte count, basophil count, platelet count, red blood cell distribution width, eosinophil count',\n",
       "       'peak expiratory flow', 'varicose veins',\n",
       "       'osteoarthritis, hip, osteoarthritis, knee', 'osteoarthritis',\n",
       "       'anti-tetanus toxoid igg measurement, response to vaccine',\n",
       "       'anorexia nervosa, obsessive-compulsive disorder, attention deficit hyperactivity disorder, tourette syndrome, unipolar depression, schizophrenia, autism spectrum disorder, bipolar disorder',\n",
       "       'cognitive function measurement',\n",
       "       'extranodal nasal nk/t cell lymphoma',\n",
       "       'anti-hepatitis b virus surface antigen igg measurement, response to vaccine',\n",
       "       'mathematical ability', 'waist-hip ratio'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catalog_chr6_short['MAPPED_TRAIT'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#japan_data.sort_values('P-VALUE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pos_to_gene(pos,mode='38'):\n",
    "    if mode=='38':\n",
    "        start='Gene start (bp)38'\n",
    "        end='Gene end (bp)38'\n",
    "    elif mode=='37':\n",
    "        start='Gene start (bp)'\n",
    "        end='Gene end (bp)'    \n",
    "    else:\n",
    "        raise\n",
    "        \n",
    "    gene_bed_select=gene_bed[(gene_bed[start]<pos) & (pos<gene_bed[end])]\n",
    "    if gene_bed_select.shape[0]>0:\n",
    "        return ','.join(gene_bed_select['Gene name'])\n",
    "    \n",
    "    front_back=[]\n",
    "    gene_bed_select=gene_bed[gene_bed[end]<pos]\n",
    "    gene_bed_select.loc[:,'dist']=(gene_bed_select[end]-pos).abs()\n",
    "    if gene_bed_select.shape[0]>0:\n",
    "        front_back.append(gene_bed_select.sort_values('dist')['Gene name'].iloc[0])\n",
    "    \n",
    "    gene_bed_select=gene_bed[gene_bed[start]>pos]\n",
    "    gene_bed_select.loc[:,'dist']=(gene_bed_select[start]-pos).abs()\n",
    "    if gene_bed_select.shape[0]>0:\n",
    "        front_back.append(gene_bed_select.sort_values('dist')['Gene name'].iloc[0])\n",
    "        \n",
    "    return ','.join(front_back)\n",
    "\n",
    "catalog_chr6_short['GENE_CUSTOM']=np.nan\n",
    "catalog_chr6_short['GENE_CUSTOM'][(catalog_chr6_short['PUBMEDID']!=30692682)]=catalog_chr6_short['CHR_POS'][(catalog_chr6_short['PUBMEDID']!=30692682)].map(lambda x: pos_to_gene(x,mode='38'))    \n",
    "catalog_chr6_short['GENE_CUSTOM'][(catalog_chr6_short['PUBMEDID']==30692682)]=catalog_chr6_short['CHR_POS'][(catalog_chr6_short['PUBMEDID']==30692682)].map(lambda x: pos_to_gene(x,mode='37'))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_unique=gene_bed['Gene name'].unique()\n",
    "def gene_bed_match(gene):\n",
    "    if type(gene)==float:\n",
    "        return ''\n",
    "    gene_list=[i.strip() for i in gene.split(',')]\n",
    "    return ','.join(list(filter(lambda x: x in gene_unique,gene_list)))\n",
    "    \n",
    "catalog_chr6_short['REPORTED_GENE_CUSTOM']=catalog_chr6_short['REPORTED GENE(S)'].map(gene_bed_match)\n",
    "catalog_chr6_short['GENE_CUSTOM_REPORTED_GENE_CUSTOM']=catalog_chr6_short[['GENE_CUSTOM','REPORTED_GENE_CUSTOM']].apply(lambda x: np.unique((x[0]+','+x[1]).strip(',').split(',')),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "catalog_chr6_short.to_csv('data/ebi_alternative_0708_munged.tsv',sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_all=pd.read_csv('data/result_concat_3_top_gene_annot.tsv',sep='\\t')\n",
    "#phenotype_name_significant_list=result_all[result_all['P_significant']==1]['phenotype_name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>phenotype_name</th>\n",
       "      <th>samples(case/control)</th>\n",
       "      <th>step</th>\n",
       "      <th>condition</th>\n",
       "      <th>marker_name</th>\n",
       "      <th>note</th>\n",
       "      <th>term</th>\n",
       "      <th>POS</th>\n",
       "      <th>...</th>\n",
       "      <th>Z</th>\n",
       "      <th>coef</th>\n",
       "      <th>std</th>\n",
       "      <th>chisq</th>\n",
       "      <th>df</th>\n",
       "      <th>P</th>\n",
       "      <th>P_significant</th>\n",
       "      <th>gene_genehow</th>\n",
       "      <th>gene</th>\n",
       "      <th>genehow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>68140</td>\n",
       "      <td>611109</td>\n",
       "      <td>t2_diabetes</td>\n",
       "      <td>8662/80451</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6:34185749_C/T</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>34185749</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.90815</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.910210e-12</td>\n",
       "      <td>1</td>\n",
       "      <td>('HMGA1', 'strong_LD')</td>\n",
       "      <td>HMGA1</td>\n",
       "      <td>strong_LD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>35161</td>\n",
       "      <td>350590</td>\n",
       "      <td>t2_diabetes</td>\n",
       "      <td>8662/80451</td>\n",
       "      <td>2</td>\n",
       "      <td>6:34185749_C/T</td>\n",
       "      <td>6:32181182_G/A</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>32181182</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.82877</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.373770e-06</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  Unnamed: 0.1 phenotype_name samples(case/control)  step  \\\n",
       "138       68140        611109    t2_diabetes            8662/80451     1   \n",
       "139       35161        350590    t2_diabetes            8662/80451     2   \n",
       "\n",
       "          condition     marker_name               note term       POS  ...  \\\n",
       "138             NaN  6:34185749_C/T  unphased bialleic  ADD  34185749  ...   \n",
       "139  6:34185749_C/T  6:32181182_G/A  unphased bialleic  ADD  32181182  ...   \n",
       "\n",
       "           Z coef  std  chisq  df             P  P_significant  \\\n",
       "138 -6.90815  NaN  NaN    NaN NaN  4.910210e-12              1   \n",
       "139 -4.82877  NaN  NaN    NaN NaN  1.373770e-06              0   \n",
       "\n",
       "               gene_genehow   gene    genehow  \n",
       "138  ('HMGA1', 'strong_LD')  HMGA1  strong_LD  \n",
       "139                     NaN    NaN        NaN  \n",
       "\n",
       "[2 rows x 26 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_all[result_all['phenotype_name']=='t2_diabetes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "phenotype_catalog_mapping={\n",
    "    'FEV_predicted':{'include_list':['fev','expiratory'],'exclude_list':['ratio','fever']},\n",
    "    'FVC_predicted':{'include_list':['fvc','vital'],'exclude_list':['ratio']},\n",
    "    'albumin':{'include_list':['serum albumin level','albumin'],'exclude_list':['non-albumin','ratio','diabetes']},\n",
    "    'allergic_disease':{'include_list':['allerg','atopic derma'],'exclude_list':[]},\n",
    "    'alt':{'include_list':['alanine'],'exclude_list':['cancer']},\n",
    "    'ast':{'include_list':['aspartat'],'exclude_list':['cancer']},\n",
    "    'blood_in_urine':{'include_list':['hematuria','uri'],'exclude_list':[]},\n",
    "    'bmi':{'include_list':['bmi','body mass index'],'exclude_list':['adjusted','blood','urate','bipolar','multiple','physical','smok']},\n",
    "    'bun':{'include_list':['bun','nitr'],'exclude_list':[]},\n",
    "    'creatinine':{'include_list':['creatinine'],'exclude_list':['ratio']},\n",
    "    't2_diabetes':{'include_list':['diabetes'],'exclude_list':[',','medica','type 1']},\n",
    "    'diastolic_blood_pressure':{'include_list':['diastolic'],'exclude_list':['time']},\n",
    "    'systolic_blood_pressure':{'include_list':['systolic'],'exclude_list':['time']},\n",
    "    'grip_strength':{'include_list':['grip'],'exclude_list':['time']},\n",
    "    'hba1c':{'include_list':['a1c'],'exclude_list':[]},\n",
    "    'hdl_cholesterol':{'include_list':['hdl','high density'],'exclude_list':[]},\n",
    "    'height':{'include_list':['height'],'exclude_list':['growth']},\n",
    "    'hematocrit':{'include_list':['hematocrit','hema'],'exclude_list':['growth']},\n",
    "    'hemoglobin':{'include_list':['hemoglobin'],'exclude_list':['a1c']},\n",
    "    'hip_circumference':{'include_list':['hip'],'exclude_list':['ratio','knee']},\n",
    "    'hscrp':{'include_list':['hscrp','c-reactive'],'exclude_list':['low','or']},\n",
    "    'hyperlipidemia':{'include_list':['cholesterol'],'exclude_list':[]},\n",
    "    'hypertension':{'include_list':['hypertension'],'exclude_list':['to']},\n",
    "    'mch':{'include_list':['corpuscular','mch'],'exclude_list':['volume','concentration','mchc']},\n",
    "    'mcv':{'include_list':['corpuscular','mcv'],'exclude_list':['hemo']},\n",
    "    'menopause_age':{'include_list':['menopause'],'exclude_list':[]},\n",
    "    'platelet':{'include_list':['platelet'],'exclude_list':['trait','width','crit']},\n",
    "    'protein_in_blood':{'include_list':['protein'],'exclude_list':['immuno','cholesterol','quantita','disease','wheat']},\n",
    "    'pulse':{'include_list':['pulse'],'exclude_list':[]},\n",
    "    'rbc':{'include_list':['red','erythrocy'],'exclude_list':['medication','width','lobe','tissue']},\n",
    "    'thyroid_disease':{'include_list':['thyroi'],'exclude_list':['diabetes','medi','drug']},\n",
    "    'total_cholesterol':{'include_list':['cholesterol'],'exclude_list':[]},\n",
    "    'triglyceride':{'include_list':['triglycer'],'exclude_list':[]},\n",
    "    'wbc':{'include_list':['white'],'exclude_list':['micro','wine']},\n",
    "    'weight':{'include_list':['weight'],'exclude_list':['urate']},\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Allele</th>\n",
       "      <th>CHR_POS</th>\n",
       "      <th>CNV</th>\n",
       "      <th>CONTEXT</th>\n",
       "      <th>Category</th>\n",
       "      <th>DISEASE/TRAIT</th>\n",
       "      <th>DOWNSTREAM_GENE_DISTANCE</th>\n",
       "      <th>DOWNSTREAM_GENE_ID</th>\n",
       "      <th>INITIAL SAMPLE SIZE</th>\n",
       "      <th>INTERGENIC</th>\n",
       "      <th>...</th>\n",
       "      <th>REPORTED GENE(S)</th>\n",
       "      <th>SNPS</th>\n",
       "      <th>SNP_GENE_IDS</th>\n",
       "      <th>SNP_ID_CURRENT</th>\n",
       "      <th>STUDY</th>\n",
       "      <th>UPSTREAM_GENE_DISTANCE</th>\n",
       "      <th>UPSTREAM_GENE_ID</th>\n",
       "      <th>GENE_CUSTOM</th>\n",
       "      <th>REPORTED_GENE_CUSTOM</th>\n",
       "      <th>GENE_CUSTOM_REPORTED_GENE_CUSTOM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Allele, CHR_POS, CNV, CONTEXT, Category, DISEASE/TRAIT, DOWNSTREAM_GENE_DISTANCE, DOWNSTREAM_GENE_ID, INITIAL SAMPLE SIZE, INTERGENIC, MAPPED_GENE, MAPPED_TRAIT, P-VALUE, P-VALUE (TEXT), PUBMEDID, REPORTED GENE(S), SNPS, SNP_GENE_IDS, SNP_ID_CURRENT, STUDY, UPSTREAM_GENE_DISTANCE, UPSTREAM_GENE_ID, GENE_CUSTOM, REPORTED_GENE_CUSTOM, GENE_CUSTOM_REPORTED_GENE_CUSTOM]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 25 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catalog_chr6_short[catalog_chr6_short['DISEASE/TRAIT'].str.contains('nitr')|catalog_chr6_short['MAPPED_TRAIT'].str.contains('nitr')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "for phenotype_name in result_all[result_all['P_significant']==1]['phenotype_name'].unique():\n",
    "    if phenotype_name!='glucose_in_blood':\n",
    "        continue\n",
    "    print(phenotype_name)\n",
    "    \n",
    "    catalog_temp=catalog_chr6_short.copy()\n",
    "    catalog_check=np.zeros(catalog_temp.shape[0]).astype(bool)\n",
    "    for include in phenotype_catalog_mapping[phenotype_name]['include_list']:\n",
    "        catalog_check=catalog_check|(catalog_temp['DISEASE/TRAIT'].str.contains(include)|catalog_temp['MAPPED_TRAIT'].str.contains(include))\n",
    "    #print(temp[['DISEASE/TRAIT','MAPPED_TRAIT']])\n",
    "    for exclude in phenotype_catalog_mapping[phenotype_name]['exclude_list']:\n",
    "        catalog_check=catalog_check&(~(catalog_temp['DISEASE/TRAIT'].str.contains(exclude)|catalog_temp['MAPPED_TRAIT'].str.contains(exclude)))\n",
    "    \n",
    "    print(catalog_temp[catalog_check][['PUBMEDID','DISEASE/TRAIT','MAPPED_TRAIT']])\n",
    "    #or i in catalog_temp[catalog_check][['DISEASE/TRAIT','MAPPED_TRAIT']].iterrows():\n",
    "    #   print(i[1].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['type 1 diabetes' 'type i diabetes mellitus']\n",
      "['type 1 diabetes' 'type i diabetes mellitus']\n",
      "['type 1 diabetes' 'type i diabetes mellitus']\n",
      "['type 1 diabetes' 'type i diabetes mellitus']\n",
      "['type 2 diabetes' 'type ii diabetes mellitus']\n",
      "['type 2 diabetes' 'type ii diabetes mellitus']\n",
      "['type 2 diabetes' 'type ii diabetes mellitus']\n",
      "['type 2 diabetes' 'type ii diabetes mellitus']\n",
      "['type 2 diabetes' 'type ii diabetes mellitus']\n",
      "['type 1 diabetes' 'type i diabetes mellitus']\n",
      "['type 2 diabetes' 'type ii diabetes mellitus']\n",
      "['type 2 diabetes' 'type ii diabetes mellitus']\n",
      "['type 2 diabetes' 'type ii diabetes mellitus']\n",
      "['type 2 diabetes' 'type ii diabetes mellitus']\n",
      "['type 2 diabetes (adjusted for bmi)' 'type ii diabetes mellitus']\n",
      "['type 1 diabetes' 'type i diabetes mellitus']\n",
      "['type 2 diabetes (adjusted for bmi)' 'type ii diabetes mellitus']\n",
      "['type 2 diabetes' 'type ii diabetes mellitus']\n",
      "['type 2 diabetes' 'type ii diabetes mellitus']\n",
      "['type 2 diabetes (adjusted for bmi)' 'type ii diabetes mellitus']\n",
      "['type 2 diabetes' 'type ii diabetes mellitus']\n",
      "['type 2 diabetes' 'type ii diabetes mellitus']\n",
      "['type 2 diabetes' 'type ii diabetes mellitus']\n",
      "['type 2 diabetes' 'type ii diabetes mellitus']\n",
      "['type 2 diabetes' 'type ii diabetes mellitus']\n",
      "['fulminant type 1 diabetes' 'type i diabetes mellitus']\n",
      "['type 1 diabetes mellitus' nan]\n",
      "['type 2 diabetes mellitus' nan]\n"
     ]
    }
   ],
   "source": [
    "for i in catalog_temp[catalog_check][['DISEASE/TRAIT','MAPPED_TRAIT']].iterrows():\n",
    "    print(i[1].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "#catalog_chr6_short[catalog_chr6_short['PUBMEDID']==25751624]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-119-baa8f17de265>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-119-baa8f17de265>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    &(result_all_pub['step']==1)\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    " &(result_all_pub['step']==1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "phenotype_info=pd.read_csv('data/phenotype_pubname.tsv',sep='\\t',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 729,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_all_pub=result_all.copy()\n",
    "result_all_pub['No.independent signals']=result_all_pub['phenotype_name'].map(lambda x: result_all[(result_all['P_significant']==1) & (result_all['phenotype_name']==x)].shape[0])\n",
    "result_all_pub=result_all_pub[(result_all_pub['P_significant']==1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 730,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_all_pub['phenotype_category']=phenotype_info.loc[result_all_pub['phenotype_name']]['phenotype_category'].values\n",
    "result_all_pub['phenotype_type']=phenotype_info.loc[result_all_pub['phenotype_name']]['phenotype_type'].values\n",
    "result_all_pub['phenotype_name_publication']=phenotype_info.loc[result_all_pub['phenotype_name']]['phenotype_name_publication'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 731,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_all_pub['A1/A2']=result_all_pub['A1']+'/'+result_all_pub['A2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "#result_all_pub_save[result_all_pub['phenotype_name']=='t2_diabetes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>phenotype_name</th>\n",
       "      <th>samples(case/control)</th>\n",
       "      <th>step</th>\n",
       "      <th>condition</th>\n",
       "      <th>marker_name</th>\n",
       "      <th>note</th>\n",
       "      <th>term</th>\n",
       "      <th>POS</th>\n",
       "      <th>...</th>\n",
       "      <th>df</th>\n",
       "      <th>P</th>\n",
       "      <th>P_significant</th>\n",
       "      <th>gene_genehow</th>\n",
       "      <th>gene</th>\n",
       "      <th>genehow</th>\n",
       "      <th>No.independent signals</th>\n",
       "      <th>phenotype_category</th>\n",
       "      <th>phenotype_type</th>\n",
       "      <th>phenotype_name_publication</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68111</td>\n",
       "      <td>475104</td>\n",
       "      <td>FEV_predicted</td>\n",
       "      <td>7440</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6:34172055_C/A</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>34172055</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.556790e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>('HMGA1', 'strong_LD')</td>\n",
       "      <td>HMGA1</td>\n",
       "      <td>strong_LD</td>\n",
       "      <td>1</td>\n",
       "      <td>Anthropometric QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Forced expiratory volume</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>68167</td>\n",
       "      <td>475496</td>\n",
       "      <td>FVC_predicted</td>\n",
       "      <td>7441</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6:34205465_G/GGAGCCC</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>34205465</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.583340e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>('HMGA1', 'on_gene')</td>\n",
       "      <td>HMGA1</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>1</td>\n",
       "      <td>Anthropometric QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Forced vital capacity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12169</td>\n",
       "      <td>154</td>\n",
       "      <td>albumin</td>\n",
       "      <td>125069</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HLA_C</td>\n",
       "      <td>phased multialleic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29910247</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.614026e-21</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-C', 'on_gene')</td>\n",
       "      <td>HLA-C</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>2</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Albumin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>68169</td>\n",
       "      <td>1630320</td>\n",
       "      <td>albumin</td>\n",
       "      <td>125069</td>\n",
       "      <td>2</td>\n",
       "      <td>HLA_C</td>\n",
       "      <td>6:34205822_T/C</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>34205822</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.389020e-17</td>\n",
       "      <td>1</td>\n",
       "      <td>('HMGA1', 'on_gene')</td>\n",
       "      <td>HMGA1</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>2</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Albumin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>53742</td>\n",
       "      <td>481860</td>\n",
       "      <td>allergic_disease</td>\n",
       "      <td>9273/80451</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6:32632237_C/T</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>32632237</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.567660e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-DQB1', 'on_gene')</td>\n",
       "      <td>HLA-DQB1</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>1</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>binary</td>\n",
       "      <td>Allergic disease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>30408</td>\n",
       "      <td>272754</td>\n",
       "      <td>alt</td>\n",
       "      <td>120788</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6:31351530_T/C</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>31351530</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.920490e-11</td>\n",
       "      <td>1</td>\n",
       "      <td>('MICA', 'moderate_LD')</td>\n",
       "      <td>MICA</td>\n",
       "      <td>moderate_LD</td>\n",
       "      <td>2</td>\n",
       "      <td>Liver-related QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Alanine aminotransferase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>68119</td>\n",
       "      <td>678800</td>\n",
       "      <td>alt</td>\n",
       "      <td>120788</td>\n",
       "      <td>2</td>\n",
       "      <td>6:31351530_T/C</td>\n",
       "      <td>6:34178764_A/C</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>34178764</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.362960e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>('HMGA1', 'strong_LD')</td>\n",
       "      <td>HMGA1</td>\n",
       "      <td>strong_LD</td>\n",
       "      <td>2</td>\n",
       "      <td>Liver-related QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Alanine aminotransferase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>41462</td>\n",
       "      <td>217</td>\n",
       "      <td>ast</td>\n",
       "      <td>121162</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HLA_DRB1</td>\n",
       "      <td>phased multialleic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32546547</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.731522e-20</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-DRB1', 'on_gene')</td>\n",
       "      <td>HLA-DRB1</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>2</td>\n",
       "      <td>Liver-related QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Aspartate transaminase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>62234</td>\n",
       "      <td>1797971</td>\n",
       "      <td>ast</td>\n",
       "      <td>121162</td>\n",
       "      <td>2</td>\n",
       "      <td>HLA_DRB1</td>\n",
       "      <td>SNPS_DPB1_8680_33052498_intron2_A</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>33052498</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.651550e-10</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-DPB1', 'on_gene')</td>\n",
       "      <td>HLA-DPB1</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>2</td>\n",
       "      <td>Liver-related QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Aspartate transaminase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>11573</td>\n",
       "      <td>92584</td>\n",
       "      <td>blood_in_urine</td>\n",
       "      <td>1817/15280</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6:29896680_A/G</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>29896680</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.992310e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-A', 'moderate_LD')</td>\n",
       "      <td>HLA-A</td>\n",
       "      <td>moderate_LD</td>\n",
       "      <td>1</td>\n",
       "      <td>Kidney-related disease</td>\n",
       "      <td>binary</td>\n",
       "      <td>Blood in urine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>50675</td>\n",
       "      <td>454491</td>\n",
       "      <td>bmi</td>\n",
       "      <td>124539</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SNPS_DQA1_3115_32608350_intron1</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>32608350</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.391600e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-DQA1', 'on_gene')</td>\n",
       "      <td>HLA-DQA1</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>2</td>\n",
       "      <td>Anthropometric QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Body mass index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4332</td>\n",
       "      <td>95304</td>\n",
       "      <td>bmi</td>\n",
       "      <td>124539</td>\n",
       "      <td>2</td>\n",
       "      <td>HLA_DQA1</td>\n",
       "      <td>6:29190624_G/A</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>29190624</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.910210e-10</td>\n",
       "      <td>1</td>\n",
       "      <td>('TRIM27', 'strong_LD')</td>\n",
       "      <td>TRIM27</td>\n",
       "      <td>strong_LD</td>\n",
       "      <td>2</td>\n",
       "      <td>Anthropometric QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Body mass index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>18880</td>\n",
       "      <td>169281</td>\n",
       "      <td>bun</td>\n",
       "      <td>124563</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6:30737591_G/C</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>30737591</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.118390e-12</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-E', 'moderate_LD')</td>\n",
       "      <td>HLA-E</td>\n",
       "      <td>moderate_LD</td>\n",
       "      <td>1</td>\n",
       "      <td>Kidney-related QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Blood urea nitrogen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>12169</td>\n",
       "      <td>154</td>\n",
       "      <td>creatinine</td>\n",
       "      <td>125191</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HLA_C</td>\n",
       "      <td>phased multialleic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29910247</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.771576e-35</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-C', 'on_gene')</td>\n",
       "      <td>HLA-C</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>3</td>\n",
       "      <td>Kidney-related QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Creatinine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>35229</td>\n",
       "      <td>843048</td>\n",
       "      <td>creatinine</td>\n",
       "      <td>125191</td>\n",
       "      <td>2</td>\n",
       "      <td>HLA_C</td>\n",
       "      <td>6:32188909_C/T</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>32188909</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.816660e-11</td>\n",
       "      <td>1</td>\n",
       "      <td>('NOTCH4', 'on_gene')</td>\n",
       "      <td>NOTCH4</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>3</td>\n",
       "      <td>Kidney-related QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Creatinine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48755</td>\n",
       "      <td>1214850</td>\n",
       "      <td>creatinine</td>\n",
       "      <td>125191</td>\n",
       "      <td>3</td>\n",
       "      <td>6:32188996_G/A,HLA_C</td>\n",
       "      <td>6:32593432_G/A</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>32593432</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.023260e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-DQA1', 'moderate_LD')</td>\n",
       "      <td>HLA-DQA1</td>\n",
       "      <td>moderate_LD</td>\n",
       "      <td>3</td>\n",
       "      <td>Kidney-related QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Creatinine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>35113</td>\n",
       "      <td>315099</td>\n",
       "      <td>diastolic_blood_pressure</td>\n",
       "      <td>124766</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AX-11365330</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>32171683</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.873610e-11</td>\n",
       "      <td>1</td>\n",
       "      <td>('NOTCH4', 'on_gene')</td>\n",
       "      <td>NOTCH4</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>1</td>\n",
       "      <td>Blood-pressure QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Diastolic blood pressure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>30923</td>\n",
       "      <td>215747</td>\n",
       "      <td>grip_strength</td>\n",
       "      <td>59747</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6:31371397_C/T</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>31371397</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.605620e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>('MICA', 'on_gene')</td>\n",
       "      <td>MICA</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>1</td>\n",
       "      <td>Anthropometric QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Grip strength</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>30602</td>\n",
       "      <td>274500</td>\n",
       "      <td>hba1c</td>\n",
       "      <td>56650</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6:31358559_C/T</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>31358559</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.253110e-10</td>\n",
       "      <td>1</td>\n",
       "      <td>('MICA', 'moderate_LD')</td>\n",
       "      <td>MICA</td>\n",
       "      <td>moderate_LD</td>\n",
       "      <td>1</td>\n",
       "      <td>Metabolic QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Hemoglobin A1c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>41462</td>\n",
       "      <td>217</td>\n",
       "      <td>hdl_cholesterol</td>\n",
       "      <td>124772</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HLA_DRB1</td>\n",
       "      <td>phased multialleic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32546547</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.037667e-16</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-DRB1', 'on_gene')</td>\n",
       "      <td>HLA-DRB1</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>3</td>\n",
       "      <td>Metabolic QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>HDL cholesterol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>70102</td>\n",
       "      <td>2026027</td>\n",
       "      <td>hdl_cholesterol</td>\n",
       "      <td>124772</td>\n",
       "      <td>2</td>\n",
       "      <td>HLA_DRB1</td>\n",
       "      <td>6:34810172_T/C</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>34810172</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.394150e-10</td>\n",
       "      <td>1</td>\n",
       "      <td>('UHRF1BP1', 'on_gene')</td>\n",
       "      <td>UHRF1BP1</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>3</td>\n",
       "      <td>Metabolic QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>HDL cholesterol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>21025</td>\n",
       "      <td>628620</td>\n",
       "      <td>hdl_cholesterol</td>\n",
       "      <td>124772</td>\n",
       "      <td>3</td>\n",
       "      <td>6:34813077_C/G,HLA_DRB1</td>\n",
       "      <td>AX-41949565</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>30998742</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.508770e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>('MUC22', 'on_gene')</td>\n",
       "      <td>MUC22</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>3</td>\n",
       "      <td>Metabolic QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>HDL cholesterol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>68172</td>\n",
       "      <td>611397</td>\n",
       "      <td>height</td>\n",
       "      <td>125264</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6:34207474_T/TAA</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>34207474</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.047770e-76</td>\n",
       "      <td>1</td>\n",
       "      <td>('HMGA1', 'on_gene')</td>\n",
       "      <td>HMGA1</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>5</td>\n",
       "      <td>Anthropometric QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Height</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>12169</td>\n",
       "      <td>154</td>\n",
       "      <td>height</td>\n",
       "      <td>125264</td>\n",
       "      <td>2</td>\n",
       "      <td>6:34207474_T/TAA</td>\n",
       "      <td>HLA_C</td>\n",
       "      <td>phased multialleic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29910247</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.324062e-46</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-C', 'on_gene')</td>\n",
       "      <td>HLA-C</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>5</td>\n",
       "      <td>Anthropometric QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Height</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2251</td>\n",
       "      <td>56275</td>\n",
       "      <td>height</td>\n",
       "      <td>125264</td>\n",
       "      <td>3</td>\n",
       "      <td>6:34207474_T/TAA,HLA_C</td>\n",
       "      <td>6:28660290_G/A</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>28660290</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.333020e-15</td>\n",
       "      <td>1</td>\n",
       "      <td>('SCAND3', 'nearest')</td>\n",
       "      <td>SCAND3</td>\n",
       "      <td>nearest</td>\n",
       "      <td>5</td>\n",
       "      <td>Anthropometric QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Height</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>70462</td>\n",
       "      <td>1825798</td>\n",
       "      <td>height</td>\n",
       "      <td>125264</td>\n",
       "      <td>4</td>\n",
       "      <td>6:28659863_C/T,6:34207474_T/TAA,HLA_C</td>\n",
       "      <td>6:34967349_G/A</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>34967349</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.561390e-12</td>\n",
       "      <td>1</td>\n",
       "      <td>('TAF11', 'nearest')</td>\n",
       "      <td>TAF11</td>\n",
       "      <td>nearest</td>\n",
       "      <td>5</td>\n",
       "      <td>Anthropometric QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Height</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>18984</td>\n",
       "      <td>510678</td>\n",
       "      <td>height</td>\n",
       "      <td>125264</td>\n",
       "      <td>5</td>\n",
       "      <td>6:28659863_C/T,6:34207474_T/TAA,6:34967349_G/A...</td>\n",
       "      <td>6:30754092_G/C</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>30754092</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.432410e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>('IER3', 'nearest')</td>\n",
       "      <td>IER3</td>\n",
       "      <td>nearest</td>\n",
       "      <td>5</td>\n",
       "      <td>Anthropometric QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Height</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>35031</td>\n",
       "      <td>314361</td>\n",
       "      <td>hematocrit</td>\n",
       "      <td>87158</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6:32149801_C/T</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>32149801</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.197860e-13</td>\n",
       "      <td>1</td>\n",
       "      <td>('RNF5,AGER', 'on_gene')</td>\n",
       "      <td>RNF5,AGER</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>3</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Hematocrit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>29189</td>\n",
       "      <td>291010</td>\n",
       "      <td>hematocrit</td>\n",
       "      <td>87158</td>\n",
       "      <td>2</td>\n",
       "      <td>6:32149801_C/T</td>\n",
       "      <td>SNPS_B_836_31324100_exon3</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>31324100</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.049540e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-B', 'on_gene')</td>\n",
       "      <td>HLA-B</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>3</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Hematocrit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>7442</td>\n",
       "      <td>267912</td>\n",
       "      <td>hematocrit</td>\n",
       "      <td>87158</td>\n",
       "      <td>3</td>\n",
       "      <td>6:32149801_C/T,HLA_B</td>\n",
       "      <td>6:29698050_A/G</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>29698050</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.911020e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-F', 'on_gene')</td>\n",
       "      <td>HLA-F</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>3</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Hematocrit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>29249</td>\n",
       "      <td>262404</td>\n",
       "      <td>hypertension</td>\n",
       "      <td>25614/80451</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SNPS_B_439_31324497_exon2_G</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>31324497</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.284830e-13</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-B', 'on_gene')</td>\n",
       "      <td>HLA-B</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>1</td>\n",
       "      <td>Cardiovascular disease</td>\n",
       "      <td>binary</td>\n",
       "      <td>Hypertension</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>25182</td>\n",
       "      <td>200824</td>\n",
       "      <td>mch</td>\n",
       "      <td>77812</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SNPS_C_788_31239061_exon3</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>31239061</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.255530e-16</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-C', 'on_gene')</td>\n",
       "      <td>HLA-C</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>1</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Mean corpuscular hemoglobin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>12143</td>\n",
       "      <td>126</td>\n",
       "      <td>mcv</td>\n",
       "      <td>77977</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HLA_B</td>\n",
       "      <td>phased multialleic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29910247</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.840923e-22</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-B', 'on_gene')</td>\n",
       "      <td>HLA-B</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>1</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Mean corpuscular volume</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>42633</td>\n",
       "      <td>297563</td>\n",
       "      <td>menopause_age</td>\n",
       "      <td>44843</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SNPS_DRB1_9798_32547722_intron5_T</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>32547722</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.076440e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-DRB1', 'on_gene')</td>\n",
       "      <td>HLA-DRB1</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>1</td>\n",
       "      <td>Other QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Menopause age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>65065</td>\n",
       "      <td>583434</td>\n",
       "      <td>platelet</td>\n",
       "      <td>86087</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AX-41961031</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>33542523</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.579220e-69</td>\n",
       "      <td>1</td>\n",
       "      <td>('BAK1', 'on_gene')</td>\n",
       "      <td>BAK1</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>5</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Platelet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>12143</td>\n",
       "      <td>126</td>\n",
       "      <td>platelet</td>\n",
       "      <td>86087</td>\n",
       "      <td>2</td>\n",
       "      <td>AX-41961031</td>\n",
       "      <td>HLA_B</td>\n",
       "      <td>phased multialleic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29910247</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.652724e-46</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-B', 'on_gene')</td>\n",
       "      <td>HLA-B</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>5</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Platelet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>65061</td>\n",
       "      <td>2333592</td>\n",
       "      <td>platelet</td>\n",
       "      <td>86087</td>\n",
       "      <td>3</td>\n",
       "      <td>AX-41961031,HLA_B</td>\n",
       "      <td>6:33541507_A/G</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>33541507</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.105480e-33</td>\n",
       "      <td>1</td>\n",
       "      <td>('BAK1', 'on_gene')</td>\n",
       "      <td>BAK1</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>5</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Platelet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>41462</td>\n",
       "      <td>217</td>\n",
       "      <td>platelet</td>\n",
       "      <td>86087</td>\n",
       "      <td>4</td>\n",
       "      <td>6:33541507_A/G,AX-41961031,HLA_B</td>\n",
       "      <td>HLA_DRB1</td>\n",
       "      <td>phased multialleic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32546547</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.656085e-22</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-DRB1', 'on_gene')</td>\n",
       "      <td>HLA-DRB1</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>5</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Platelet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>68140</td>\n",
       "      <td>3870357</td>\n",
       "      <td>platelet</td>\n",
       "      <td>86087</td>\n",
       "      <td>5</td>\n",
       "      <td>6:33541507_A/G,AX-41961031,HLA_B,HLA_DRB1</td>\n",
       "      <td>6:34185749_C/T</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>34185749</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.061680e-11</td>\n",
       "      <td>1</td>\n",
       "      <td>('HMGA1', 'strong_LD')</td>\n",
       "      <td>HMGA1</td>\n",
       "      <td>strong_LD</td>\n",
       "      <td>5</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Platelet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>36593</td>\n",
       "      <td>291928</td>\n",
       "      <td>protein_in_blood</td>\n",
       "      <td>24307</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6:32324081_A/AT</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>32324081</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.617760e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>('C6orf10', 'on_gene')</td>\n",
       "      <td>C6orf10</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>1</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein in blood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>6018</td>\n",
       "      <td>54162</td>\n",
       "      <td>pulse</td>\n",
       "      <td>121124</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AX-11397307</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>29522712</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.098060e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>('UBD', 'strong_LD')</td>\n",
       "      <td>UBD</td>\n",
       "      <td>strong_LD</td>\n",
       "      <td>1</td>\n",
       "      <td>Other QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Pulse rate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>12143</td>\n",
       "      <td>126</td>\n",
       "      <td>rbc</td>\n",
       "      <td>87356</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HLA_B</td>\n",
       "      <td>phased multialleic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29910247</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>9.225597e-26</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-B', 'on_gene')</td>\n",
       "      <td>HLA-B</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>4</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Red blood cell count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>68169</td>\n",
       "      <td>2377550</td>\n",
       "      <td>rbc</td>\n",
       "      <td>87356</td>\n",
       "      <td>2</td>\n",
       "      <td>HLA_B</td>\n",
       "      <td>6:34205822_T/C</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>34205822</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.610360e-11</td>\n",
       "      <td>1</td>\n",
       "      <td>('HMGA1', 'on_gene')</td>\n",
       "      <td>HMGA1</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>4</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Red blood cell count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>34906</td>\n",
       "      <td>1252944</td>\n",
       "      <td>rbc</td>\n",
       "      <td>87356</td>\n",
       "      <td>3</td>\n",
       "      <td>6:34205822_T/C,HLA_B</td>\n",
       "      <td>6:32109294_A/G</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>32109294</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.529120e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>('ATF6B', 'strong_LD')</td>\n",
       "      <td>ATF6B</td>\n",
       "      <td>strong_LD</td>\n",
       "      <td>4</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Red blood cell count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>13468</td>\n",
       "      <td>495689</td>\n",
       "      <td>rbc</td>\n",
       "      <td>87356</td>\n",
       "      <td>4</td>\n",
       "      <td>6:32109294_A/G,6:34205822_T/C,HLA_B</td>\n",
       "      <td>6:29938478_A/G</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>29938478</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.935550e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-A', 'moderate_LD')</td>\n",
       "      <td>HLA-A</td>\n",
       "      <td>moderate_LD</td>\n",
       "      <td>4</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Red blood cell count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>35113</td>\n",
       "      <td>315099</td>\n",
       "      <td>systolic_blood_pressure</td>\n",
       "      <td>124467</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AX-11365330</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>32171683</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.505260e-10</td>\n",
       "      <td>1</td>\n",
       "      <td>('NOTCH4', 'on_gene')</td>\n",
       "      <td>NOTCH4</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>1</td>\n",
       "      <td>Blood-pressure QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Systolic blood pressure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>68140</td>\n",
       "      <td>611109</td>\n",
       "      <td>t2_diabetes</td>\n",
       "      <td>8662/80451</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6:34185749_C/T</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>34185749</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.910210e-12</td>\n",
       "      <td>1</td>\n",
       "      <td>('HMGA1', 'strong_LD')</td>\n",
       "      <td>HMGA1</td>\n",
       "      <td>strong_LD</td>\n",
       "      <td>1</td>\n",
       "      <td>Metabolic diseases</td>\n",
       "      <td>binary</td>\n",
       "      <td>Type 2 diabetes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>41462</td>\n",
       "      <td>217</td>\n",
       "      <td>thyroid_disease</td>\n",
       "      <td>5139/77411</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HLA_DRB1</td>\n",
       "      <td>phased multialleic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32546547</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.477362e-33</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-DRB1', 'on_gene')</td>\n",
       "      <td>HLA-DRB1</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>2</td>\n",
       "      <td>Thyroid-related diseases</td>\n",
       "      <td>binary</td>\n",
       "      <td>Thyroid disease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>12143</td>\n",
       "      <td>126</td>\n",
       "      <td>thyroid_disease</td>\n",
       "      <td>5139/77411</td>\n",
       "      <td>2</td>\n",
       "      <td>HLA_DRB1</td>\n",
       "      <td>HLA_B</td>\n",
       "      <td>phased multialleic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29910247</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.665947e-14</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-B', 'on_gene')</td>\n",
       "      <td>HLA-B</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>2</td>\n",
       "      <td>Thyroid-related diseases</td>\n",
       "      <td>binary</td>\n",
       "      <td>Thyroid disease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>52055</td>\n",
       "      <td>201</td>\n",
       "      <td>total_cholesterol</td>\n",
       "      <td>125007</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HLA_DQB1</td>\n",
       "      <td>phased multialleic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32627241</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.027162e-38</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-DQB1', 'on_gene')</td>\n",
       "      <td>HLA-DQB1</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>1</td>\n",
       "      <td>Metabolic QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Total cholesterol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>52055</td>\n",
       "      <td>201</td>\n",
       "      <td>triglyceride</td>\n",
       "      <td>123426</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HLA_DQB1</td>\n",
       "      <td>phased multialleic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32627241</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.266323e-51</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-DQB1', 'on_gene')</td>\n",
       "      <td>HLA-DQB1</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>2</td>\n",
       "      <td>Metabolic QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Triglyceride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>68140</td>\n",
       "      <td>1561723</td>\n",
       "      <td>triglyceride</td>\n",
       "      <td>123426</td>\n",
       "      <td>2</td>\n",
       "      <td>HLA_DQB1</td>\n",
       "      <td>6:34185749_C/T</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>34185749</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.171770e-13</td>\n",
       "      <td>1</td>\n",
       "      <td>('HMGA1', 'strong_LD')</td>\n",
       "      <td>HMGA1</td>\n",
       "      <td>strong_LD</td>\n",
       "      <td>2</td>\n",
       "      <td>Metabolic QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Triglyceride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>12169</td>\n",
       "      <td>154</td>\n",
       "      <td>wbc</td>\n",
       "      <td>86639</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HLA_C</td>\n",
       "      <td>phased multialleic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29910247</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.804949e-61</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-C', 'on_gene')</td>\n",
       "      <td>HLA-C</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>4</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>White blood cell count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>42718</td>\n",
       "      <td>1022256</td>\n",
       "      <td>wbc</td>\n",
       "      <td>86639</td>\n",
       "      <td>2</td>\n",
       "      <td>HLA_C</td>\n",
       "      <td>SNPS_DRB1_9773_32547747_intron5_G</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>32547747</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.368470e-19</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-DRB1', 'on_gene')</td>\n",
       "      <td>HLA-DRB1</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>4</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>White blood cell count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>31961</td>\n",
       "      <td>1401796</td>\n",
       "      <td>wbc</td>\n",
       "      <td>86639</td>\n",
       "      <td>3</td>\n",
       "      <td>HLA_C,HLA_DRB1</td>\n",
       "      <td>6:31428911_A/G</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>31428911</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.839100e-10</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-B', 'moderate_LD')</td>\n",
       "      <td>HLA-B</td>\n",
       "      <td>moderate_LD</td>\n",
       "      <td>4</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>White blood cell count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>68250</td>\n",
       "      <td>3060495</td>\n",
       "      <td>wbc</td>\n",
       "      <td>86639</td>\n",
       "      <td>4</td>\n",
       "      <td>6:31428911_A/G,HLA_C,HLA_DRB1</td>\n",
       "      <td>6:34234974_A/G</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>34234974</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.601910e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>('HMGA1', 'strong_LD')</td>\n",
       "      <td>HMGA1</td>\n",
       "      <td>strong_LD</td>\n",
       "      <td>4</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>White blood cell count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>68167</td>\n",
       "      <td>611352</td>\n",
       "      <td>weight</td>\n",
       "      <td>124693</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6:34205465_G/GGAGCCC</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>34205465</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.129760e-36</td>\n",
       "      <td>1</td>\n",
       "      <td>('HMGA1', 'on_gene')</td>\n",
       "      <td>HMGA1</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>4</td>\n",
       "      <td>Anthropometric QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Body Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>46981</td>\n",
       "      <td>468200</td>\n",
       "      <td>weight</td>\n",
       "      <td>124693</td>\n",
       "      <td>2</td>\n",
       "      <td>6:34205465_G/GGAGCCC</td>\n",
       "      <td>6:32569297_A/G</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>32569297</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.330940e-16</td>\n",
       "      <td>1</td>\n",
       "      <td>('HLA-DQA1', 'moderate_LD')</td>\n",
       "      <td>HLA-DQA1</td>\n",
       "      <td>moderate_LD</td>\n",
       "      <td>4</td>\n",
       "      <td>Anthropometric QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Body Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>3225</td>\n",
       "      <td>77400</td>\n",
       "      <td>weight</td>\n",
       "      <td>124693</td>\n",
       "      <td>3</td>\n",
       "      <td>6:32569297_A/G,6:34205465_G/GGAGCCC,HLA_DQA1</td>\n",
       "      <td>6:28861320_A/G</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>28861320</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.906660e-12</td>\n",
       "      <td>1</td>\n",
       "      <td>('TRIM27', 'strong_LD')</td>\n",
       "      <td>TRIM27</td>\n",
       "      <td>strong_LD</td>\n",
       "      <td>4</td>\n",
       "      <td>Anthropometric QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Body Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>20878</td>\n",
       "      <td>520175</td>\n",
       "      <td>weight</td>\n",
       "      <td>124693</td>\n",
       "      <td>4</td>\n",
       "      <td>6:28861320_A/G,6:32569297_A/G,6:34205465_G/GGA...</td>\n",
       "      <td>6:30990019_A/G</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>30990019</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.906670e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>('MUC22', 'on_gene')</td>\n",
       "      <td>MUC22</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>4</td>\n",
       "      <td>Anthropometric QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Body Weight</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  Unnamed: 0.1            phenotype_name samples(case/control)  \\\n",
       "1         68111        475104             FEV_predicted                  7440   \n",
       "3         68167        475496             FVC_predicted                  7441   \n",
       "7         12169           154                   albumin                125069   \n",
       "8         68169       1630320                   albumin                125069   \n",
       "10        53742        481860          allergic_disease            9273/80451   \n",
       "12        30408        272754                       alt                120788   \n",
       "13        68119        678800                       alt                120788   \n",
       "17        41462           217                       ast                121162   \n",
       "18        62234       1797971                       ast                121162   \n",
       "24        11573         92584            blood_in_urine            1817/15280   \n",
       "26        50675        454491                       bmi                124539   \n",
       "27         4332         95304                       bmi                124539   \n",
       "32        18880        169281                       bun                124563   \n",
       "45        12169           154                creatinine                125191   \n",
       "46        35229        843048                creatinine                125191   \n",
       "47        48755       1214850                creatinine                125191   \n",
       "49        35113        315099  diastolic_blood_pressure                124766   \n",
       "58        30923        215747             grip_strength                 59747   \n",
       "61        30602        274500                     hba1c                 56650   \n",
       "63        41462           217           hdl_cholesterol                124772   \n",
       "64        70102       2026027           hdl_cholesterol                124772   \n",
       "65        21025        628620           hdl_cholesterol                124772   \n",
       "68        68172        611397                    height                125264   \n",
       "69        12169           154                    height                125264   \n",
       "70         2251         56275                    height                125264   \n",
       "71        70462       1825798                    height                125264   \n",
       "72        18984        510678                    height                125264   \n",
       "74        35031        314361                hematocrit                 87158   \n",
       "75        29189        291010                hematocrit                 87158   \n",
       "76         7442        267912                hematocrit                 87158   \n",
       "..          ...           ...                       ...                   ...   \n",
       "88        29249        262404              hypertension           25614/80451   \n",
       "94        25182        200824                       mch                 77812   \n",
       "97        12143           126                       mcv                 77977   \n",
       "100       42633        297563             menopause_age                 44843   \n",
       "109       65065        583434                  platelet                 86087   \n",
       "110       12143           126                  platelet                 86087   \n",
       "111       65061       2333592                  platelet                 86087   \n",
       "112       41462           217                  platelet                 86087   \n",
       "113       68140       3870357                  platelet                 86087   \n",
       "116       36593        291928          protein_in_blood                 24307   \n",
       "119        6018         54162                     pulse                121124   \n",
       "122       12143           126                       rbc                 87356   \n",
       "123       68169       2377550                       rbc                 87356   \n",
       "124       34906       1252944                       rbc                 87356   \n",
       "125       13468        495689                       rbc                 87356   \n",
       "134       35113        315099   systolic_blood_pressure                124467   \n",
       "136       68140        611109               t2_diabetes            8662/80451   \n",
       "139       41462           217           thyroid_disease            5139/77411   \n",
       "140       12143           126           thyroid_disease            5139/77411   \n",
       "143       52055           201         total_cholesterol                125007   \n",
       "146       52055           201              triglyceride                123426   \n",
       "147       68140       1561723              triglyceride                123426   \n",
       "153       12169           154                       wbc                 86639   \n",
       "154       42718       1022256                       wbc                 86639   \n",
       "155       31961       1401796                       wbc                 86639   \n",
       "156       68250       3060495                       wbc                 86639   \n",
       "158       68167        611352                    weight                124693   \n",
       "159       46981        468200                    weight                124693   \n",
       "160        3225         77400                    weight                124693   \n",
       "161       20878        520175                    weight                124693   \n",
       "\n",
       "     step                                          condition  \\\n",
       "1       1                                                NaN   \n",
       "3       1                                                NaN   \n",
       "7       1                                                NaN   \n",
       "8       2                                              HLA_C   \n",
       "10      1                                                NaN   \n",
       "12      1                                                NaN   \n",
       "13      2                                     6:31351530_T/C   \n",
       "17      1                                                NaN   \n",
       "18      2                                           HLA_DRB1   \n",
       "24      1                                                NaN   \n",
       "26      1                                                NaN   \n",
       "27      2                                           HLA_DQA1   \n",
       "32      1                                                NaN   \n",
       "45      1                                                NaN   \n",
       "46      2                                              HLA_C   \n",
       "47      3                               6:32188996_G/A,HLA_C   \n",
       "49      1                                                NaN   \n",
       "58      1                                                NaN   \n",
       "61      1                                                NaN   \n",
       "63      1                                                NaN   \n",
       "64      2                                           HLA_DRB1   \n",
       "65      3                            6:34813077_C/G,HLA_DRB1   \n",
       "68      1                                                NaN   \n",
       "69      2                                   6:34207474_T/TAA   \n",
       "70      3                             6:34207474_T/TAA,HLA_C   \n",
       "71      4              6:28659863_C/T,6:34207474_T/TAA,HLA_C   \n",
       "72      5  6:28659863_C/T,6:34207474_T/TAA,6:34967349_G/A...   \n",
       "74      1                                                NaN   \n",
       "75      2                                     6:32149801_C/T   \n",
       "76      3                               6:32149801_C/T,HLA_B   \n",
       "..    ...                                                ...   \n",
       "88      1                                                NaN   \n",
       "94      1                                                NaN   \n",
       "97      1                                                NaN   \n",
       "100     1                                                NaN   \n",
       "109     1                                                NaN   \n",
       "110     2                                        AX-41961031   \n",
       "111     3                                  AX-41961031,HLA_B   \n",
       "112     4                   6:33541507_A/G,AX-41961031,HLA_B   \n",
       "113     5          6:33541507_A/G,AX-41961031,HLA_B,HLA_DRB1   \n",
       "116     1                                                NaN   \n",
       "119     1                                                NaN   \n",
       "122     1                                                NaN   \n",
       "123     2                                              HLA_B   \n",
       "124     3                               6:34205822_T/C,HLA_B   \n",
       "125     4                6:32109294_A/G,6:34205822_T/C,HLA_B   \n",
       "134     1                                                NaN   \n",
       "136     1                                                NaN   \n",
       "139     1                                                NaN   \n",
       "140     2                                           HLA_DRB1   \n",
       "143     1                                                NaN   \n",
       "146     1                                                NaN   \n",
       "147     2                                           HLA_DQB1   \n",
       "153     1                                                NaN   \n",
       "154     2                                              HLA_C   \n",
       "155     3                                     HLA_C,HLA_DRB1   \n",
       "156     4                      6:31428911_A/G,HLA_C,HLA_DRB1   \n",
       "158     1                                                NaN   \n",
       "159     2                               6:34205465_G/GGAGCCC   \n",
       "160     3       6:32569297_A/G,6:34205465_G/GGAGCCC,HLA_DQA1   \n",
       "161     4  6:28861320_A/G,6:32569297_A/G,6:34205465_G/GGA...   \n",
       "\n",
       "                           marker_name                note term       POS  \\\n",
       "1                       6:34172055_C/A   unphased bialleic  ADD  34172055   \n",
       "3                 6:34205465_G/GGAGCCC   unphased bialleic  ADD  34205465   \n",
       "7                                HLA_C  phased multialleic  NaN  29910247   \n",
       "8                       6:34205822_T/C   unphased bialleic  ADD  34205822   \n",
       "10                      6:32632237_C/T   unphased bialleic  ADD  32632237   \n",
       "12                      6:31351530_T/C   unphased bialleic  ADD  31351530   \n",
       "13                      6:34178764_A/C   unphased bialleic  ADD  34178764   \n",
       "17                            HLA_DRB1  phased multialleic  NaN  32546547   \n",
       "18   SNPS_DPB1_8680_33052498_intron2_A   unphased bialleic  ADD  33052498   \n",
       "24                      6:29896680_A/G   unphased bialleic  ADD  29896680   \n",
       "26     SNPS_DQA1_3115_32608350_intron1   unphased bialleic  ADD  32608350   \n",
       "27                      6:29190624_G/A   unphased bialleic  ADD  29190624   \n",
       "32                      6:30737591_G/C   unphased bialleic  ADD  30737591   \n",
       "45                               HLA_C  phased multialleic  NaN  29910247   \n",
       "46                      6:32188909_C/T   unphased bialleic  ADD  32188909   \n",
       "47                      6:32593432_G/A   unphased bialleic  ADD  32593432   \n",
       "49                         AX-11365330   unphased bialleic  ADD  32171683   \n",
       "58                      6:31371397_C/T   unphased bialleic  ADD  31371397   \n",
       "61                      6:31358559_C/T   unphased bialleic  ADD  31358559   \n",
       "63                            HLA_DRB1  phased multialleic  NaN  32546547   \n",
       "64                      6:34810172_T/C   unphased bialleic  ADD  34810172   \n",
       "65                         AX-41949565   unphased bialleic  ADD  30998742   \n",
       "68                    6:34207474_T/TAA   unphased bialleic  ADD  34207474   \n",
       "69                               HLA_C  phased multialleic  NaN  29910247   \n",
       "70                      6:28660290_G/A   unphased bialleic  ADD  28660290   \n",
       "71                      6:34967349_G/A   unphased bialleic  ADD  34967349   \n",
       "72                      6:30754092_G/C   unphased bialleic  ADD  30754092   \n",
       "74                      6:32149801_C/T   unphased bialleic  ADD  32149801   \n",
       "75           SNPS_B_836_31324100_exon3   unphased bialleic  ADD  31324100   \n",
       "76                      6:29698050_A/G   unphased bialleic  ADD  29698050   \n",
       "..                                 ...                 ...  ...       ...   \n",
       "88         SNPS_B_439_31324497_exon2_G   unphased bialleic  ADD  31324497   \n",
       "94           SNPS_C_788_31239061_exon3   unphased bialleic  ADD  31239061   \n",
       "97                               HLA_B  phased multialleic  NaN  29910247   \n",
       "100  SNPS_DRB1_9798_32547722_intron5_T   unphased bialleic  ADD  32547722   \n",
       "109                        AX-41961031   unphased bialleic  ADD  33542523   \n",
       "110                              HLA_B  phased multialleic  NaN  29910247   \n",
       "111                     6:33541507_A/G   unphased bialleic  ADD  33541507   \n",
       "112                           HLA_DRB1  phased multialleic  NaN  32546547   \n",
       "113                     6:34185749_C/T   unphased bialleic  ADD  34185749   \n",
       "116                    6:32324081_A/AT   unphased bialleic  ADD  32324081   \n",
       "119                        AX-11397307   unphased bialleic  ADD  29522712   \n",
       "122                              HLA_B  phased multialleic  NaN  29910247   \n",
       "123                     6:34205822_T/C   unphased bialleic  ADD  34205822   \n",
       "124                     6:32109294_A/G   unphased bialleic  ADD  32109294   \n",
       "125                     6:29938478_A/G   unphased bialleic  ADD  29938478   \n",
       "134                        AX-11365330   unphased bialleic  ADD  32171683   \n",
       "136                     6:34185749_C/T   unphased bialleic  ADD  34185749   \n",
       "139                           HLA_DRB1  phased multialleic  NaN  32546547   \n",
       "140                              HLA_B  phased multialleic  NaN  29910247   \n",
       "143                           HLA_DQB1  phased multialleic  NaN  32627241   \n",
       "146                           HLA_DQB1  phased multialleic  NaN  32627241   \n",
       "147                     6:34185749_C/T   unphased bialleic  ADD  34185749   \n",
       "153                              HLA_C  phased multialleic  NaN  29910247   \n",
       "154  SNPS_DRB1_9773_32547747_intron5_G   unphased bialleic  ADD  32547747   \n",
       "155                     6:31428911_A/G   unphased bialleic  ADD  31428911   \n",
       "156                     6:34234974_A/G   unphased bialleic  ADD  34234974   \n",
       "158               6:34205465_G/GGAGCCC   unphased bialleic  ADD  34205465   \n",
       "159                     6:32569297_A/G   unphased bialleic  ADD  32569297   \n",
       "160                     6:28861320_A/G   unphased bialleic  ADD  28861320   \n",
       "161                     6:30990019_A/G   unphased bialleic  ADD  30990019   \n",
       "\n",
       "     ...    df             P  P_significant                 gene_genehow  \\\n",
       "1    ...   NaN  5.556790e-09              1       ('HMGA1', 'strong_LD')   \n",
       "3    ...   NaN  2.583340e-08              1         ('HMGA1', 'on_gene')   \n",
       "7    ...  15.0  5.614026e-21              1         ('HLA-C', 'on_gene')   \n",
       "8    ...   NaN  7.389020e-17              1         ('HMGA1', 'on_gene')   \n",
       "10   ...   NaN  1.567660e-08              1      ('HLA-DQB1', 'on_gene')   \n",
       "12   ...   NaN  6.920490e-11              1      ('MICA', 'moderate_LD')   \n",
       "13   ...   NaN  4.362960e-08              1       ('HMGA1', 'strong_LD')   \n",
       "17   ...  20.0  1.731522e-20              1      ('HLA-DRB1', 'on_gene')   \n",
       "18   ...   NaN  7.651550e-10              1      ('HLA-DPB1', 'on_gene')   \n",
       "24   ...   NaN  2.992310e-08              1     ('HLA-A', 'moderate_LD')   \n",
       "26   ...   NaN  3.391600e-09              1      ('HLA-DQA1', 'on_gene')   \n",
       "27   ...   NaN  9.910210e-10              1      ('TRIM27', 'strong_LD')   \n",
       "32   ...   NaN  4.118390e-12              1     ('HLA-E', 'moderate_LD')   \n",
       "45   ...  15.0  2.771576e-35              1         ('HLA-C', 'on_gene')   \n",
       "46   ...   NaN  8.816660e-11              1        ('NOTCH4', 'on_gene')   \n",
       "47   ...   NaN  1.023260e-08              1  ('HLA-DQA1', 'moderate_LD')   \n",
       "49   ...   NaN  2.873610e-11              1        ('NOTCH4', 'on_gene')   \n",
       "58   ...   NaN  4.605620e-08              1          ('MICA', 'on_gene')   \n",
       "61   ...   NaN  5.253110e-10              1      ('MICA', 'moderate_LD')   \n",
       "63   ...  20.0  5.037667e-16              1      ('HLA-DRB1', 'on_gene')   \n",
       "64   ...   NaN  6.394150e-10              1      ('UHRF1BP1', 'on_gene')   \n",
       "65   ...   NaN  1.508770e-08              1         ('MUC22', 'on_gene')   \n",
       "68   ...   NaN  2.047770e-76              1         ('HMGA1', 'on_gene')   \n",
       "69   ...  15.0  1.324062e-46              1         ('HLA-C', 'on_gene')   \n",
       "70   ...   NaN  7.333020e-15              1        ('SCAND3', 'nearest')   \n",
       "71   ...   NaN  7.561390e-12              1         ('TAF11', 'nearest')   \n",
       "72   ...   NaN  1.432410e-09              1          ('IER3', 'nearest')   \n",
       "74   ...   NaN  9.197860e-13              1     ('RNF5,AGER', 'on_gene')   \n",
       "75   ...   NaN  1.049540e-09              1         ('HLA-B', 'on_gene')   \n",
       "76   ...   NaN  9.911020e-09              1         ('HLA-F', 'on_gene')   \n",
       "..   ...   ...           ...            ...                          ...   \n",
       "88   ...   NaN  2.284830e-13              1         ('HLA-B', 'on_gene')   \n",
       "94   ...   NaN  1.255530e-16              1         ('HLA-C', 'on_gene')   \n",
       "97   ...  26.0  3.840923e-22              1         ('HLA-B', 'on_gene')   \n",
       "100  ...   NaN  3.076440e-09              1      ('HLA-DRB1', 'on_gene')   \n",
       "109  ...   NaN  1.579220e-69              1          ('BAK1', 'on_gene')   \n",
       "110  ...  26.0  1.652724e-46              1         ('HLA-B', 'on_gene')   \n",
       "111  ...   NaN  1.105480e-33              1          ('BAK1', 'on_gene')   \n",
       "112  ...  20.0  1.656085e-22              1      ('HLA-DRB1', 'on_gene')   \n",
       "113  ...   NaN  4.061680e-11              1       ('HMGA1', 'strong_LD')   \n",
       "116  ...   NaN  2.617760e-08              1       ('C6orf10', 'on_gene')   \n",
       "119  ...   NaN  3.098060e-08              1         ('UBD', 'strong_LD')   \n",
       "122  ...  26.0  9.225597e-26              1         ('HLA-B', 'on_gene')   \n",
       "123  ...   NaN  6.610360e-11              1         ('HMGA1', 'on_gene')   \n",
       "124  ...   NaN  1.529120e-08              1       ('ATF6B', 'strong_LD')   \n",
       "125  ...   NaN  2.935550e-08              1     ('HLA-A', 'moderate_LD')   \n",
       "134  ...   NaN  2.505260e-10              1        ('NOTCH4', 'on_gene')   \n",
       "136  ...   NaN  4.910210e-12              1       ('HMGA1', 'strong_LD')   \n",
       "139  ...  20.0  6.477362e-33              1      ('HLA-DRB1', 'on_gene')   \n",
       "140  ...  26.0  3.665947e-14              1         ('HLA-B', 'on_gene')   \n",
       "143  ...  14.0  1.027162e-38              1      ('HLA-DQB1', 'on_gene')   \n",
       "146  ...  14.0  2.266323e-51              1      ('HLA-DQB1', 'on_gene')   \n",
       "147  ...   NaN  1.171770e-13              1       ('HMGA1', 'strong_LD')   \n",
       "153  ...  15.0  2.804949e-61              1         ('HLA-C', 'on_gene')   \n",
       "154  ...   NaN  6.368470e-19              1      ('HLA-DRB1', 'on_gene')   \n",
       "155  ...   NaN  1.839100e-10              1     ('HLA-B', 'moderate_LD')   \n",
       "156  ...   NaN  1.601910e-08              1       ('HMGA1', 'strong_LD')   \n",
       "158  ...   NaN  5.129760e-36              1         ('HMGA1', 'on_gene')   \n",
       "159  ...   NaN  6.330940e-16              1  ('HLA-DQA1', 'moderate_LD')   \n",
       "160  ...   NaN  6.906660e-12              1      ('TRIM27', 'strong_LD')   \n",
       "161  ...   NaN  3.906670e-08              1         ('MUC22', 'on_gene')   \n",
       "\n",
       "          gene      genehow  No.independent signals        phenotype_category  \\\n",
       "1        HMGA1    strong_LD                       1        Anthropometric QTL   \n",
       "3        HMGA1      on_gene                       1        Anthropometric QTL   \n",
       "7        HLA-C      on_gene                       2               Protein QTL   \n",
       "8        HMGA1      on_gene                       2               Protein QTL   \n",
       "10    HLA-DQB1      on_gene                       1   Immune-related diseases   \n",
       "12        MICA  moderate_LD                       2         Liver-related QTL   \n",
       "13       HMGA1    strong_LD                       2         Liver-related QTL   \n",
       "17    HLA-DRB1      on_gene                       2         Liver-related QTL   \n",
       "18    HLA-DPB1      on_gene                       2         Liver-related QTL   \n",
       "24       HLA-A  moderate_LD                       1    Kidney-related disease   \n",
       "26    HLA-DQA1      on_gene                       2        Anthropometric QTL   \n",
       "27      TRIM27    strong_LD                       2        Anthropometric QTL   \n",
       "32       HLA-E  moderate_LD                       1        Kidney-related QTL   \n",
       "45       HLA-C      on_gene                       3        Kidney-related QTL   \n",
       "46      NOTCH4      on_gene                       3        Kidney-related QTL   \n",
       "47    HLA-DQA1  moderate_LD                       3        Kidney-related QTL   \n",
       "49      NOTCH4      on_gene                       1        Blood-pressure QTL   \n",
       "58        MICA      on_gene                       1        Anthropometric QTL   \n",
       "61        MICA  moderate_LD                       1             Metabolic QTL   \n",
       "63    HLA-DRB1      on_gene                       3             Metabolic QTL   \n",
       "64    UHRF1BP1      on_gene                       3             Metabolic QTL   \n",
       "65       MUC22      on_gene                       3             Metabolic QTL   \n",
       "68       HMGA1      on_gene                       5        Anthropometric QTL   \n",
       "69       HLA-C      on_gene                       5        Anthropometric QTL   \n",
       "70      SCAND3      nearest                       5        Anthropometric QTL   \n",
       "71       TAF11      nearest                       5        Anthropometric QTL   \n",
       "72        IER3      nearest                       5        Anthropometric QTL   \n",
       "74   RNF5,AGER      on_gene                       3         Hematological QTL   \n",
       "75       HLA-B      on_gene                       3         Hematological QTL   \n",
       "76       HLA-F      on_gene                       3         Hematological QTL   \n",
       "..         ...          ...                     ...                       ...   \n",
       "88       HLA-B      on_gene                       1    Cardiovascular disease   \n",
       "94       HLA-C      on_gene                       1         Hematological QTL   \n",
       "97       HLA-B      on_gene                       1         Hematological QTL   \n",
       "100   HLA-DRB1      on_gene                       1                 Other QTL   \n",
       "109       BAK1      on_gene                       5         Hematological QTL   \n",
       "110      HLA-B      on_gene                       5         Hematological QTL   \n",
       "111       BAK1      on_gene                       5         Hematological QTL   \n",
       "112   HLA-DRB1      on_gene                       5         Hematological QTL   \n",
       "113      HMGA1    strong_LD                       5         Hematological QTL   \n",
       "116    C6orf10      on_gene                       1               Protein QTL   \n",
       "119        UBD    strong_LD                       1                 Other QTL   \n",
       "122      HLA-B      on_gene                       4         Hematological QTL   \n",
       "123      HMGA1      on_gene                       4         Hematological QTL   \n",
       "124      ATF6B    strong_LD                       4         Hematological QTL   \n",
       "125      HLA-A  moderate_LD                       4         Hematological QTL   \n",
       "134     NOTCH4      on_gene                       1        Blood-pressure QTL   \n",
       "136      HMGA1    strong_LD                       1        Metabolic diseases   \n",
       "139   HLA-DRB1      on_gene                       2  Thyroid-related diseases   \n",
       "140      HLA-B      on_gene                       2  Thyroid-related diseases   \n",
       "143   HLA-DQB1      on_gene                       1             Metabolic QTL   \n",
       "146   HLA-DQB1      on_gene                       2             Metabolic QTL   \n",
       "147      HMGA1    strong_LD                       2             Metabolic QTL   \n",
       "153      HLA-C      on_gene                       4         Hematological QTL   \n",
       "154   HLA-DRB1      on_gene                       4         Hematological QTL   \n",
       "155      HLA-B  moderate_LD                       4         Hematological QTL   \n",
       "156      HMGA1    strong_LD                       4         Hematological QTL   \n",
       "158      HMGA1      on_gene                       4        Anthropometric QTL   \n",
       "159   HLA-DQA1  moderate_LD                       4        Anthropometric QTL   \n",
       "160     TRIM27    strong_LD                       4        Anthropometric QTL   \n",
       "161      MUC22      on_gene                       4        Anthropometric QTL   \n",
       "\n",
       "     phenotype_type   phenotype_name_publication  \n",
       "1        continuous     Forced expiratory volume  \n",
       "3        continuous        Forced vital capacity  \n",
       "7        continuous                      Albumin  \n",
       "8        continuous                      Albumin  \n",
       "10           binary             Allergic disease  \n",
       "12       continuous     Alanine aminotransferase  \n",
       "13       continuous     Alanine aminotransferase  \n",
       "17       continuous       Aspartate transaminase  \n",
       "18       continuous       Aspartate transaminase  \n",
       "24           binary               Blood in urine  \n",
       "26       continuous              Body mass index  \n",
       "27       continuous              Body mass index  \n",
       "32       continuous          Blood urea nitrogen  \n",
       "45       continuous                   Creatinine  \n",
       "46       continuous                   Creatinine  \n",
       "47       continuous                   Creatinine  \n",
       "49       continuous     Diastolic blood pressure  \n",
       "58       continuous                Grip strength  \n",
       "61       continuous               Hemoglobin A1c  \n",
       "63       continuous              HDL cholesterol  \n",
       "64       continuous              HDL cholesterol  \n",
       "65       continuous              HDL cholesterol  \n",
       "68       continuous                       Height  \n",
       "69       continuous                       Height  \n",
       "70       continuous                       Height  \n",
       "71       continuous                       Height  \n",
       "72       continuous                       Height  \n",
       "74       continuous                   Hematocrit  \n",
       "75       continuous                   Hematocrit  \n",
       "76       continuous                   Hematocrit  \n",
       "..              ...                          ...  \n",
       "88           binary                 Hypertension  \n",
       "94       continuous  Mean corpuscular hemoglobin  \n",
       "97       continuous      Mean corpuscular volume  \n",
       "100      continuous                Menopause age  \n",
       "109      continuous                     Platelet  \n",
       "110      continuous                     Platelet  \n",
       "111      continuous                     Platelet  \n",
       "112      continuous                     Platelet  \n",
       "113      continuous                     Platelet  \n",
       "116      continuous             Protein in blood  \n",
       "119      continuous                   Pulse rate  \n",
       "122      continuous         Red blood cell count  \n",
       "123      continuous         Red blood cell count  \n",
       "124      continuous         Red blood cell count  \n",
       "125      continuous         Red blood cell count  \n",
       "134      continuous      Systolic blood pressure  \n",
       "136          binary              Type 2 diabetes  \n",
       "139          binary              Thyroid disease  \n",
       "140          binary              Thyroid disease  \n",
       "143      continuous            Total cholesterol  \n",
       "146      continuous                 Triglyceride  \n",
       "147      continuous                 Triglyceride  \n",
       "153      continuous       White blood cell count  \n",
       "154      continuous       White blood cell count  \n",
       "155      continuous       White blood cell count  \n",
       "156      continuous       White blood cell count  \n",
       "158      continuous                  Body Weight  \n",
       "159      continuous                  Body Weight  \n",
       "160      continuous                  Body Weight  \n",
       "161      continuous                  Body Weight  \n",
       "\n",
       "[66 rows x 30 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_all_pub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Unnamed: 0.1', 'phenotype_name', 'samples(case/control)',\n",
       "       'step', 'condition', 'marker_name', 'note', 'term', 'POS', 'A1', 'A2',\n",
       "       'A1_freq_case', 'A1_freq_control', 'multi_allele', 'nobs', 'Z', 'coef',\n",
       "       'std', 'chisq', 'df', 'P', 'P_significant', 'gene_genehow', 'gene',\n",
       "       'genehow', 'No.independent signals', 'phenotype_category',\n",
       "       'phenotype_type', 'phenotype_name_publication'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_all_pub.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 724,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_all_pub_save=result_all_pub.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 728,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1            C/A\n",
       "3      G/GGAGCCC\n",
       "7            NaN\n",
       "8            C/T\n",
       "10           C/T\n",
       "12           C/T\n",
       "13           A/C\n",
       "17           NaN\n",
       "18           p/a\n",
       "24           A/G\n",
       "26           ./A\n",
       "27           A/G\n",
       "32           C/G\n",
       "45           NaN\n",
       "46           T/C\n",
       "47           A/G\n",
       "49           C/T\n",
       "58           T/C\n",
       "61           T/C\n",
       "63           NaN\n",
       "64           C/T\n",
       "65           G/T\n",
       "68         T/TAA\n",
       "69           NaN\n",
       "70           G/A\n",
       "71           A/G\n",
       "72           C/G\n",
       "74           T/C\n",
       "75           G/T\n",
       "76           G/A\n",
       "         ...    \n",
       "88           p/a\n",
       "94           T/C\n",
       "97           NaN\n",
       "100          a/p\n",
       "109          C/T\n",
       "110          NaN\n",
       "111          A/G\n",
       "112          NaN\n",
       "113          C/T\n",
       "116         AT/A\n",
       "119          G/A\n",
       "122          NaN\n",
       "123          C/T\n",
       "124          G/A\n",
       "125          A/G\n",
       "134          C/T\n",
       "136          C/T\n",
       "139          NaN\n",
       "140          NaN\n",
       "143          NaN\n",
       "146          NaN\n",
       "147          C/T\n",
       "153          NaN\n",
       "154          a/p\n",
       "155          A/G\n",
       "156          G/A\n",
       "158    G/GGAGCCC\n",
       "159          A/G\n",
       "160          A/G\n",
       "161          G/A\n",
       "Length: 66, dtype: object"
      ]
     },
     "execution_count": 728,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_all_pub_save['']=result_all_pub_save['A1']+'/'+result_all_pub_save['A2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_all_pub_step1=result_all_pub[result_all_pub['step']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_all_pub_step1_save=result_all_pub_step1[['phenotype_type','phenotype_category','phenotype_name_publication','samples(case/control)',\n",
    "       'No.independent signals', 'marker_name', 'POS', 'A1', 'A2','gene','A1_freq_case', 'A1_freq_control','coef','std', 'chisq', 'df', 'P']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_all_pub_step1_save.sort_values(['phenotype_type','phenotype_category']).to_csv(\"result_pub_significant_step1.tsv\",sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['P_significant', 'Unnamed: 0', 'Unnamed: 0.1', 'Z', 'condition',\n",
       "       'gene_genehow', 'genehow', 'multi_allele', 'nobs', 'note',\n",
       "       'phenotype_name', 'step', 'term'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 551,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_all_pub.columns.difference(result_all_pub_step1_save.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_all_pub_save=result_all_pub[['phenotype_type','phenotype_category','phenotype_name_publication','samples(case/control)',\n",
    "       'step', 'marker_name', 'POS', 'A1', 'A2','gene','A1_freq_case', 'A1_freq_control','coef','std', 'chisq', 'df', 'P']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_all_pub_save.sort_values(['phenotype_type','phenotype_category']).to_csv(\"result_pub_significant_stepall.tsv\",sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "#result_all_pub_save.sort_values('phenotype_category').sort_values('phenotype_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "#result_all_pub_save.to_csv(\"result_pub_significant_step1.tsv\",sep='\\t')\n",
    "#result_all_pub_save"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Novelty check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3       [HLA-DQA2, HLA-DQB1, HLA-DRB1]\n",
       "57                 [HLA-DRA, HLA-DRB5]\n",
       "285                             [APOM]\n",
       "378     [HLA-DQA1, HLA-DRB1, HLA-DRB5]\n",
       "383               [HLA-DQA1, HLA-DRB1]\n",
       "390               [HLA-DQA1, HLA-DRB1]\n",
       "398                             [APOM]\n",
       "401                   [SCAND3, TRIM27]\n",
       "563                             [TNXB]\n",
       "602    [HLA-DQA2, LST1, LTA, LTB, TNF]\n",
       "666                [HLA-C, MICA, MICB]\n",
       "667                     [HLA-B, HLA-C]\n",
       "694                             [TNXB]\n",
       "705                      [GRM4, HMGA1]\n",
       "736                       [AGER, PPT2]\n",
       "784               [HLA-DQA2, HLA-DQB1]\n",
       "797                           [CCHCR1]\n",
       "800                [HLA-DRA, HLA-DRB5]\n",
       "860     [HLA-DQA1, HLA-DQA2, HLA-DQB1]\n",
       "872                         [HLA-DQA1]\n",
       "873                            [HLA-B]\n",
       "874                          [C6orf10]\n",
       "875                            [STK19]\n",
       "876                            [TCF19]\n",
       "877                   [C6orf15, MUC22]\n",
       "878                           [PRRC2A]\n",
       "879                           [PRRC2A]\n",
       "880                           [GPANK1]\n",
       "881                            [BTNL2]\n",
       "882                [C6orf15, PSORS1C1]\n",
       "                    ...               \n",
       "71                          [HLA-DRB1]\n",
       "72                          [HLA-DQB1]\n",
       "73                      [HLA-B, HLA-C]\n",
       "74                          [HLA-DPB1]\n",
       "75              [C6orf1, HMGA1, NUDT3]\n",
       "76                          [HLA-DRB1]\n",
       "77                      [HLA-B, HLA-C]\n",
       "78                          [HLA-DPB1]\n",
       "79                             [HLA-B]\n",
       "80                             [HLA-C]\n",
       "81                          [HLA-DPB1]\n",
       "82                          [HLA-DRB1]\n",
       "83                             [HLA-C]\n",
       "84                      [HLA-B, HLA-C]\n",
       "85                        [MICA, MICB]\n",
       "86                [HLA-DQA1, HLA-DRB1]\n",
       "87           [CCHCR1, HLA-B, PSORS1C1]\n",
       "88                      [HLA-E, RPP21]\n",
       "89                    [CDSN, PSORS1C1]\n",
       "90                      [HCG27, HLA-C]\n",
       "91                            [ZNF165]\n",
       "92                      [HCG27, HLA-C]\n",
       "93                            [ZNF165]\n",
       "94                      [BAK1, GGNBP1]\n",
       "95                              [BAK1]\n",
       "96                      [HCG27, HLA-C]\n",
       "97                              [TNXB]\n",
       "98                            [ZNF165]\n",
       "99                       [HLA-B, MICA]\n",
       "100                      [HLA-B, MICA]\n",
       "Name: GENE_CUSTOM_REPORTED_GENE_CUSTOM, Length: 3244, dtype: object"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catalog_temp['GENE_CUSTOM_REPORTED_GENE_CUSTOM']#.values#+[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ch6845/tools/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:29: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "novelty_check_list=[]\n",
    "\n",
    "for phenotype_name in result_all[result_all['P_significant']==1]['phenotype_name'].unique():   \n",
    "    #print('-----------------------')\n",
    "    if phenotype_name!='FEV_predicted':\n",
    "        pass#continue\n",
    "    \n",
    "    our_temp=result_all[(result_all['P_significant']==1)&(result_all['phenotype_name']==phenotype_name)]\n",
    "\n",
    "    catalog_temp=catalog_chr6_short.copy()\n",
    "    catalog_check=np.zeros(catalog_temp.shape[0]).astype(bool)\n",
    "    for include in phenotype_catalog_mapping[phenotype_name]['include_list']:\n",
    "        catalog_check=catalog_check|(catalog_temp['DISEASE/TRAIT'].str.contains(include)|catalog_temp['MAPPED_TRAIT'].str.contains(include))\n",
    "    #print(temp[['DISEASE/TRAIT','MAPPED_TRAIT']])\n",
    "    for exclude in phenotype_catalog_mapping[phenotype_name]['exclude_list']:\n",
    "        catalog_check=catalog_check&(~(catalog_temp['DISEASE/TRAIT'].str.contains(exclude)|catalog_temp['MAPPED_TRAIT'].str.contains(exclude)))\n",
    "\n",
    "    catalog_temp=catalog_temp[catalog_check][['PUBMEDID','DISEASE/TRAIT','MAPPED_TRAIT','SNPS','CHR_POS','REPORTED GENE(S)','GENE_CUSTOM_REPORTED_GENE_CUSTOM']]#['CHR_POS'])#['GENE_CUSTOM_REPORTED_GENE_CUSTOM'])\n",
    "    catalog_temp['genehow']='reported_gene+on_gene,nearest' \n",
    "    \n",
    "    #print(phenotype_name,catalog_temp.shape,catalog_temp)\n",
    "    is_novel=lambda gene: ('-' if len(set(gene.split(',')).intersection(set(np.unique(np.concatenate(catalog_temp['GENE_CUSTOM_REPORTED_GENE_CUSTOM'].values)))))>0 else 'novel') if catalog_temp.shape[0]>0 else 'novel'\n",
    "        \n",
    "    our_temp=our_temp[['marker_name','POS','gene','genehow']].rename(columns={'marker_name':'SNPS','POS':'CHR_POS','gene':'GENE_CUSTOM_REPORTED_GENE_CUSTOM'})\n",
    "    our_temp['PUBMEDID']='OUR_RESULT'\n",
    "    our_temp['novelty']=our_temp['GENE_CUSTOM_REPORTED_GENE_CUSTOM'].map(is_novel)\n",
    "\n",
    "    \n",
    "    our_catalog_concat=pd.concat([our_temp,catalog_temp])\n",
    "    our_catalog_concat['phenotype_name']=phenotype_name\n",
    "    our_catalog_concat=our_catalog_concat[['phenotype_name','PUBMEDID','DISEASE/TRAIT','MAPPED_TRAIT','SNPS','CHR_POS','REPORTED GENE(S)','GENE_CUSTOM_REPORTED_GENE_CUSTOM','genehow','novelty']]\n",
    "    \n",
    "    novelty_check_list.append(our_catalog_concat)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "novelty_check_df=pd.concat(novelty_check_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "#novelty_check_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "novelty_check_df['phenotype_category']=phenotype_info.loc[novelty_check_df['phenotype_name']]['phenotype_category'].values\n",
    "novelty_check_df['phenotype_type']=phenotype_info.loc[novelty_check_df['phenotype_name']]['phenotype_type'].values\n",
    "novelty_check_df['phenotype_name_publication']=phenotype_info.loc[novelty_check_df['phenotype_name']]['phenotype_name_publication'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "#novelty_check_df[(novelty_check_df['PUBMEDID']=='OUR_RESULT')&(novelty_check_df['novelty']=='novel')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "#novelty_check_df[(novelty_check_df['PUBMEDID']=='OUR_RESULT')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['phenotype_name', 'PUBMEDID', 'DISEASE/TRAIT', 'MAPPED_TRAIT', 'SNPS',\n",
       "       'CHR_POS', 'REPORTED GENE(S)', 'GENE_CUSTOM_REPORTED_GENE_CUSTOM',\n",
       "       'genehow', 'novelty', 'phenotype_category', 'phenotype_type',\n",
       "       'phenotype_name_publication'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "novelty_check_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1092,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ZKSCAN3', 'ZSCAN31'], dtype='<U7')"
      ]
     },
     "execution_count": 1092,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(novelty_check_list)['GENE_CUSTOM_REPORTED_GENE_CUSTOM'].iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "metadata": {},
   "outputs": [],
   "source": [
    "#novelty_check_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "novelty_check_df.sort_values(['phenotype_type','phenotype_category']).to_csv('data/result_concat_4_top_novelty_check.tsv',sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "novelty_check_df[['phenotype_type', 'phenotype_category', 'phenotype_name_publication',\n",
    "        'PUBMEDID', 'DISEASE/TRAIT', 'MAPPED_TRAIT', 'SNPS',\n",
    "       'CHR_POS', 'REPORTED GENE(S)', 'GENE_CUSTOM_REPORTED_GENE_CUSTOM',\n",
    "       'genehow', 'novelty', \n",
    "       ]].sort_values(['phenotype_type','phenotype_category']).to_csv('result_pub_significant_noveltycheck.tsv',sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>phenotype_type</th>\n",
       "      <th>phenotype_category</th>\n",
       "      <th>phenotype_name_publication</th>\n",
       "      <th>PUBMEDID</th>\n",
       "      <th>DISEASE/TRAIT</th>\n",
       "      <th>MAPPED_TRAIT</th>\n",
       "      <th>SNPS</th>\n",
       "      <th>CHR_POS</th>\n",
       "      <th>REPORTED GENE(S)</th>\n",
       "      <th>GENE_CUSTOM_REPORTED_GENE_CUSTOM</th>\n",
       "      <th>genehow</th>\n",
       "      <th>novelty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>binary</td>\n",
       "      <td>Cardiovascular disease</td>\n",
       "      <td>Hypertension</td>\n",
       "      <td>OUR_RESULT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SNPS_B_439_31324497_exon2_G</td>\n",
       "      <td>31324497</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HLA-B</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13052</th>\n",
       "      <td>binary</td>\n",
       "      <td>Cardiovascular disease</td>\n",
       "      <td>Hypertension</td>\n",
       "      <td>21909115</td>\n",
       "      <td>hypertension</td>\n",
       "      <td>hypertension</td>\n",
       "      <td>rs805303</td>\n",
       "      <td>31648589</td>\n",
       "      <td>BAT2, BAT5</td>\n",
       "      <td>[BAG6]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46605</th>\n",
       "      <td>binary</td>\n",
       "      <td>Cardiovascular disease</td>\n",
       "      <td>Hypertension</td>\n",
       "      <td>25249183</td>\n",
       "      <td>hypertension</td>\n",
       "      <td>hypertension</td>\n",
       "      <td>rs2021783</td>\n",
       "      <td>32077074</td>\n",
       "      <td>CYP21A2</td>\n",
       "      <td>[CYP21A2, TNXB]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104309</th>\n",
       "      <td>binary</td>\n",
       "      <td>Cardiovascular disease</td>\n",
       "      <td>Hypertension</td>\n",
       "      <td>30527956</td>\n",
       "      <td>pulmonary arterial hypertension</td>\n",
       "      <td>pulmonary arterial hypertension</td>\n",
       "      <td>rs2856830</td>\n",
       "      <td>33073957</td>\n",
       "      <td>HLA-DPA1, HLA-DPB1</td>\n",
       "      <td>[HLA-DPA1, HLA-DPB1]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116809</th>\n",
       "      <td>binary</td>\n",
       "      <td>Cardiovascular disease</td>\n",
       "      <td>Hypertension</td>\n",
       "      <td>31879980</td>\n",
       "      <td>hypertension</td>\n",
       "      <td>hypertension</td>\n",
       "      <td>rs2524095</td>\n",
       "      <td>31298340</td>\n",
       "      <td>LINC02571</td>\n",
       "      <td>[HLA-B]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116810</th>\n",
       "      <td>binary</td>\n",
       "      <td>Cardiovascular disease</td>\n",
       "      <td>Hypertension</td>\n",
       "      <td>31879980</td>\n",
       "      <td>hypertension</td>\n",
       "      <td>hypertension</td>\n",
       "      <td>rs2596473</td>\n",
       "      <td>31463022</td>\n",
       "      <td>HCP5</td>\n",
       "      <td>[MICA, MICB]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116832</th>\n",
       "      <td>binary</td>\n",
       "      <td>Cardiovascular disease</td>\n",
       "      <td>Hypertension</td>\n",
       "      <td>31879980</td>\n",
       "      <td>hypertension</td>\n",
       "      <td>hypertension</td>\n",
       "      <td>rs805293</td>\n",
       "      <td>31720741</td>\n",
       "      <td>MPIG6B</td>\n",
       "      <td>[C6orf25, LY6G6C]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116833</th>\n",
       "      <td>binary</td>\n",
       "      <td>Cardiovascular disease</td>\n",
       "      <td>Hypertension</td>\n",
       "      <td>31879980</td>\n",
       "      <td>hypertension</td>\n",
       "      <td>hypertension</td>\n",
       "      <td>rs3130482</td>\n",
       "      <td>31872005</td>\n",
       "      <td>SLC44A4</td>\n",
       "      <td>[SLC44A4]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>binary</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>OUR_RESULT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6:32632237_C/T</td>\n",
       "      <td>32632237</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HLA-DQB1</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2555</th>\n",
       "      <td>binary</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>22036096</td>\n",
       "      <td>ige grass sensitization</td>\n",
       "      <td>seasonal allergic rhinitis</td>\n",
       "      <td>rs7775228</td>\n",
       "      <td>32690302</td>\n",
       "      <td>HLA region</td>\n",
       "      <td>[HLA-DQA2, HLA-DQB1]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8602</th>\n",
       "      <td>binary</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>23042114</td>\n",
       "      <td>atopic dermatitis</td>\n",
       "      <td>atopic eczema</td>\n",
       "      <td>rs9368677</td>\n",
       "      <td>31304544</td>\n",
       "      <td>HLA-C</td>\n",
       "      <td>[HLA-B, HLA-C]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8607</th>\n",
       "      <td>binary</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>23042114</td>\n",
       "      <td>atopic dermatitis</td>\n",
       "      <td>atopic eczema</td>\n",
       "      <td>rs176095</td>\n",
       "      <td>32190542</td>\n",
       "      <td>GPSM3</td>\n",
       "      <td>[GPSM3, PBX2]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8608</th>\n",
       "      <td>binary</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>23042114</td>\n",
       "      <td>atopic dermatitis</td>\n",
       "      <td>atopic eczema</td>\n",
       "      <td>rs9469099</td>\n",
       "      <td>32341131</td>\n",
       "      <td>C6orf10</td>\n",
       "      <td>[C6orf10]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14538</th>\n",
       "      <td>binary</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>23817569</td>\n",
       "      <td>self-reported allergy</td>\n",
       "      <td>allergy</td>\n",
       "      <td>rs9266772</td>\n",
       "      <td>31384336</td>\n",
       "      <td>MICA, HLA-C</td>\n",
       "      <td>[HLA-B, HLA-C, MICA]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14539</th>\n",
       "      <td>binary</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>23817569</td>\n",
       "      <td>self-reported allergy</td>\n",
       "      <td>allergy</td>\n",
       "      <td>rs6906021</td>\n",
       "      <td>32658534</td>\n",
       "      <td>HLA-DQB1, HLA-DQA1</td>\n",
       "      <td>[HLA-DQA1, HLA-DQB1]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16875</th>\n",
       "      <td>binary</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>23886662</td>\n",
       "      <td>atopic dermatitis</td>\n",
       "      <td>atopic eczema</td>\n",
       "      <td>rs12153855</td>\n",
       "      <td>32107027</td>\n",
       "      <td>TNXB, CREBL1</td>\n",
       "      <td>[TNXB]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25072</th>\n",
       "      <td>binary</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>27182965</td>\n",
       "      <td>allergy</td>\n",
       "      <td>allergy</td>\n",
       "      <td>rs34004019</td>\n",
       "      <td>32658626</td>\n",
       "      <td>HLA-DQB1, HLA-DQA1</td>\n",
       "      <td>[HLA-DQA1, HLA-DQB1]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32479</th>\n",
       "      <td>binary</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>29083406</td>\n",
       "      <td>allergic disease (asthma, hay fever or eczema)</td>\n",
       "      <td>allergy</td>\n",
       "      <td>rs10947428</td>\n",
       "      <td>33679281</td>\n",
       "      <td>ITPR3</td>\n",
       "      <td>[ITPR3]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32480</th>\n",
       "      <td>binary</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>29083406</td>\n",
       "      <td>allergic disease (asthma, hay fever or eczema)</td>\n",
       "      <td>allergy</td>\n",
       "      <td>rs3097670</td>\n",
       "      <td>33078975</td>\n",
       "      <td>HLA-DPA1</td>\n",
       "      <td>[HLA-DPA1, HLA-DPB1]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35240</th>\n",
       "      <td>binary</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>29083406</td>\n",
       "      <td>allergic disease (asthma, hay fever or eczema)</td>\n",
       "      <td>allergy</td>\n",
       "      <td>rs34004019</td>\n",
       "      <td>32658626</td>\n",
       "      <td>HLA-DQA1, HLA-DQB1</td>\n",
       "      <td>[HLA-DQA1, HLA-DQB1]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35241</th>\n",
       "      <td>binary</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>29083406</td>\n",
       "      <td>allergic disease (asthma, hay fever or eczema)</td>\n",
       "      <td>allergy</td>\n",
       "      <td>rs2854001</td>\n",
       "      <td>31355235</td>\n",
       "      <td>HLA-B</td>\n",
       "      <td>[HLA-B]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35242</th>\n",
       "      <td>binary</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>29083406</td>\n",
       "      <td>allergic disease (asthma, hay fever or eczema)</td>\n",
       "      <td>allergy</td>\n",
       "      <td>rs9259819</td>\n",
       "      <td>29925798</td>\n",
       "      <td>HLA-J</td>\n",
       "      <td>[HLA-A, HLA-G]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35243</th>\n",
       "      <td>binary</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>29083406</td>\n",
       "      <td>allergic disease (asthma, hay fever or eczema)</td>\n",
       "      <td>allergy</td>\n",
       "      <td>rs28895016</td>\n",
       "      <td>31606748</td>\n",
       "      <td>NCR3, AIF1</td>\n",
       "      <td>[AIF1, NCR3]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35317</th>\n",
       "      <td>binary</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>29083406</td>\n",
       "      <td>allergic disease (asthma, hay fever or eczema)</td>\n",
       "      <td>allergy</td>\n",
       "      <td>rs2507978</td>\n",
       "      <td>31383887</td>\n",
       "      <td>HLA-B, MICA</td>\n",
       "      <td>[HLA-B, MICA]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46412</th>\n",
       "      <td>binary</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>25574825</td>\n",
       "      <td>atopic dermatitis</td>\n",
       "      <td>atopic eczema</td>\n",
       "      <td>rs41268896</td>\n",
       "      <td>32102292</td>\n",
       "      <td>c6orf10</td>\n",
       "      <td>[TNXB]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47939</th>\n",
       "      <td>binary</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>23817571</td>\n",
       "      <td>allergic sensitization</td>\n",
       "      <td>allergic sensitization measurement</td>\n",
       "      <td>rs6906021</td>\n",
       "      <td>32658534</td>\n",
       "      <td>HLA-DQB1, HLA-DQA1</td>\n",
       "      <td>[HLA-DQA1, HLA-DQB1]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47950</th>\n",
       "      <td>binary</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>23817571</td>\n",
       "      <td>allergic sensitization</td>\n",
       "      <td>allergic sensitization measurement</td>\n",
       "      <td>rs6932730</td>\n",
       "      <td>31386405</td>\n",
       "      <td>MICA, HLA-C, HLA-B, MICB</td>\n",
       "      <td>[HLA-B, HLA-C, MICA, MICB]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50618</th>\n",
       "      <td>binary</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>24388013</td>\n",
       "      <td>asthma and hay fever</td>\n",
       "      <td>seasonal allergic rhinitis, asthma</td>\n",
       "      <td>rs9273373</td>\n",
       "      <td>32658824</td>\n",
       "      <td>HLA-DQB1</td>\n",
       "      <td>[HLA-DQA1, HLA-DQB1]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51566</th>\n",
       "      <td>binary</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>25710614</td>\n",
       "      <td>peanut allergy</td>\n",
       "      <td>peanut allergy measurement</td>\n",
       "      <td>rs9275596</td>\n",
       "      <td>32713854</td>\n",
       "      <td>HLA-DRB1, HLA-DQA1, HLA-DQB1, HLA-DQA2</td>\n",
       "      <td>[HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DRB1]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77587</th>\n",
       "      <td>binary</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>26482879</td>\n",
       "      <td>atopic dermatitis</td>\n",
       "      <td>atopic eczema</td>\n",
       "      <td>rs4713555</td>\n",
       "      <td>32607747</td>\n",
       "      <td>HLA-DRB1, HLA-DQA1</td>\n",
       "      <td>[HLA-DQA1, HLA-DRB1]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150355</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30072576</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs2855812</td>\n",
       "      <td>31504943</td>\n",
       "      <td>MICB</td>\n",
       "      <td>[MICB]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150358</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30072576</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs2395400</td>\n",
       "      <td>33802299</td>\n",
       "      <td>MLN</td>\n",
       "      <td>[MLN]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150649</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30072576</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs114508013</td>\n",
       "      <td>31900988</td>\n",
       "      <td>C2</td>\n",
       "      <td>[C2, ZBTB12]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150650</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30072576</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs114508013</td>\n",
       "      <td>31900988</td>\n",
       "      <td>C2</td>\n",
       "      <td>[C2, ZBTB12]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150651</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30072576</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs114508013</td>\n",
       "      <td>31900988</td>\n",
       "      <td>C2</td>\n",
       "      <td>[C2, ZBTB12]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150652</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30072576</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs114508013</td>\n",
       "      <td>31900988</td>\n",
       "      <td>C2</td>\n",
       "      <td>[C2, ZBTB12]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150653</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30072576</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs114508013</td>\n",
       "      <td>31900988</td>\n",
       "      <td>C2</td>\n",
       "      <td>[C2, ZBTB12]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150654</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30072576</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs114508013</td>\n",
       "      <td>31900988</td>\n",
       "      <td>C2</td>\n",
       "      <td>[C2, ZBTB12]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151819</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30072576</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs4151671</td>\n",
       "      <td>31951126</td>\n",
       "      <td>CFB</td>\n",
       "      <td>[CFB]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152028</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30072576</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs1050673</td>\n",
       "      <td>33193884</td>\n",
       "      <td>COL11A2</td>\n",
       "      <td>[COL11A2, RXRB]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152329</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30072576</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs769178</td>\n",
       "      <td>31579737</td>\n",
       "      <td>NCR3</td>\n",
       "      <td>[LTB, NCR3, TNF]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156098</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30072576</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs116298992</td>\n",
       "      <td>32100718</td>\n",
       "      <td>TNXB</td>\n",
       "      <td>[TNXB]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156815</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30072576</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs114887538</td>\n",
       "      <td>32146203</td>\n",
       "      <td>ATF6B</td>\n",
       "      <td>[ATF6B, FKBPL, PRRT1]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157056</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30072576</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs114508013</td>\n",
       "      <td>31900988</td>\n",
       "      <td>C2</td>\n",
       "      <td>[C2, ZBTB12]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160209</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>27532455</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs9263871</td>\n",
       "      <td>31202751</td>\n",
       "      <td>NR</td>\n",
       "      <td>[HCG27]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160235</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>27532455</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs2256175</td>\n",
       "      <td>31412672</td>\n",
       "      <td>NR</td>\n",
       "      <td>[MICA]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160236</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>27532455</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs2239705</td>\n",
       "      <td>31545625</td>\n",
       "      <td>NR</td>\n",
       "      <td>[ATP6V1G2]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160654</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>31320639</td>\n",
       "      <td>neurological blood protein biomarker levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs62397979</td>\n",
       "      <td>31515429</td>\n",
       "      <td>XXbac-BPG16N22.5</td>\n",
       "      <td>[MCCD1, MICB]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162928</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30072576</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs146306986</td>\n",
       "      <td>33224636</td>\n",
       "      <td>TAPBP</td>\n",
       "      <td>[RING1, TAPBP, VPS52]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165513</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30072576</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs115878542</td>\n",
       "      <td>31707520</td>\n",
       "      <td>APOM</td>\n",
       "      <td>[APOM, LY6G6F, MEGT1, XXbac-BPG32J3.20]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165514</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30072576</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs115878542</td>\n",
       "      <td>31707520</td>\n",
       "      <td>APOM</td>\n",
       "      <td>[APOM, LY6G6F, MEGT1, XXbac-BPG32J3.20]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165518</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30072576</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs114887538</td>\n",
       "      <td>32146203</td>\n",
       "      <td>ATF6B</td>\n",
       "      <td>[ATF6B, FKBPL, PRRT1]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165519</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30072576</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs114887538</td>\n",
       "      <td>32146203</td>\n",
       "      <td>ATF6B</td>\n",
       "      <td>[ATF6B, FKBPL, PRRT1]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165520</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30072576</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs114887538</td>\n",
       "      <td>32146203</td>\n",
       "      <td>ATF6B</td>\n",
       "      <td>[ATF6B, FKBPL, PRRT1]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165521</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30072576</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs114887538</td>\n",
       "      <td>32146203</td>\n",
       "      <td>ATF6B</td>\n",
       "      <td>[ATF6B, FKBPL, PRRT1]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165522</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30072576</td>\n",
       "      <td>blood protein levels</td>\n",
       "      <td>blood protein measurement</td>\n",
       "      <td>rs114887538</td>\n",
       "      <td>32146203</td>\n",
       "      <td>ATF6B</td>\n",
       "      <td>[ATF6B, FKBPL, PRRT1]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30692682</td>\n",
       "      <td>total protein</td>\n",
       "      <td>NaN</td>\n",
       "      <td>rs13197513</td>\n",
       "      <td>32990121</td>\n",
       "      <td>HLA-DPB1</td>\n",
       "      <td>[HLA-DOA, HLA-DPA1, HLA-DPB1]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30692682</td>\n",
       "      <td>total protein</td>\n",
       "      <td>NaN</td>\n",
       "      <td>rs3132486</td>\n",
       "      <td>31243170</td>\n",
       "      <td>HLA-C</td>\n",
       "      <td>[HLA-B, HLA-C]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30692682</td>\n",
       "      <td>non-albumin protein</td>\n",
       "      <td>NaN</td>\n",
       "      <td>rs28752797</td>\n",
       "      <td>31291172</td>\n",
       "      <td>HLA-C</td>\n",
       "      <td>[HLA-B, HLA-C]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>continuous</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>30692682</td>\n",
       "      <td>non-albumin protein</td>\n",
       "      <td>NaN</td>\n",
       "      <td>rs562289</td>\n",
       "      <td>32577046</td>\n",
       "      <td>HLA-DRB1</td>\n",
       "      <td>[HLA-DQA1, HLA-DRB1]</td>\n",
       "      <td>reported_gene+on_gene,nearest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1149 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       phenotype_type       phenotype_category phenotype_name_publication  \\\n",
       "88             binary   Cardiovascular disease               Hypertension   \n",
       "13052          binary   Cardiovascular disease               Hypertension   \n",
       "46605          binary   Cardiovascular disease               Hypertension   \n",
       "104309         binary   Cardiovascular disease               Hypertension   \n",
       "116809         binary   Cardiovascular disease               Hypertension   \n",
       "116810         binary   Cardiovascular disease               Hypertension   \n",
       "116832         binary   Cardiovascular disease               Hypertension   \n",
       "116833         binary   Cardiovascular disease               Hypertension   \n",
       "10             binary  Immune-related diseases           Allergic disease   \n",
       "2555           binary  Immune-related diseases           Allergic disease   \n",
       "8602           binary  Immune-related diseases           Allergic disease   \n",
       "8607           binary  Immune-related diseases           Allergic disease   \n",
       "8608           binary  Immune-related diseases           Allergic disease   \n",
       "14538          binary  Immune-related diseases           Allergic disease   \n",
       "14539          binary  Immune-related diseases           Allergic disease   \n",
       "16875          binary  Immune-related diseases           Allergic disease   \n",
       "25072          binary  Immune-related diseases           Allergic disease   \n",
       "32479          binary  Immune-related diseases           Allergic disease   \n",
       "32480          binary  Immune-related diseases           Allergic disease   \n",
       "35240          binary  Immune-related diseases           Allergic disease   \n",
       "35241          binary  Immune-related diseases           Allergic disease   \n",
       "35242          binary  Immune-related diseases           Allergic disease   \n",
       "35243          binary  Immune-related diseases           Allergic disease   \n",
       "35317          binary  Immune-related diseases           Allergic disease   \n",
       "46412          binary  Immune-related diseases           Allergic disease   \n",
       "47939          binary  Immune-related diseases           Allergic disease   \n",
       "47950          binary  Immune-related diseases           Allergic disease   \n",
       "50618          binary  Immune-related diseases           Allergic disease   \n",
       "51566          binary  Immune-related diseases           Allergic disease   \n",
       "77587          binary  Immune-related diseases           Allergic disease   \n",
       "...               ...                      ...                        ...   \n",
       "150355     continuous              Protein QTL           Protein in blood   \n",
       "150358     continuous              Protein QTL           Protein in blood   \n",
       "150649     continuous              Protein QTL           Protein in blood   \n",
       "150650     continuous              Protein QTL           Protein in blood   \n",
       "150651     continuous              Protein QTL           Protein in blood   \n",
       "150652     continuous              Protein QTL           Protein in blood   \n",
       "150653     continuous              Protein QTL           Protein in blood   \n",
       "150654     continuous              Protein QTL           Protein in blood   \n",
       "151819     continuous              Protein QTL           Protein in blood   \n",
       "152028     continuous              Protein QTL           Protein in blood   \n",
       "152329     continuous              Protein QTL           Protein in blood   \n",
       "156098     continuous              Protein QTL           Protein in blood   \n",
       "156815     continuous              Protein QTL           Protein in blood   \n",
       "157056     continuous              Protein QTL           Protein in blood   \n",
       "160209     continuous              Protein QTL           Protein in blood   \n",
       "160235     continuous              Protein QTL           Protein in blood   \n",
       "160236     continuous              Protein QTL           Protein in blood   \n",
       "160654     continuous              Protein QTL           Protein in blood   \n",
       "162928     continuous              Protein QTL           Protein in blood   \n",
       "165513     continuous              Protein QTL           Protein in blood   \n",
       "165514     continuous              Protein QTL           Protein in blood   \n",
       "165518     continuous              Protein QTL           Protein in blood   \n",
       "165519     continuous              Protein QTL           Protein in blood   \n",
       "165520     continuous              Protein QTL           Protein in blood   \n",
       "165521     continuous              Protein QTL           Protein in blood   \n",
       "165522     continuous              Protein QTL           Protein in blood   \n",
       "41         continuous              Protein QTL           Protein in blood   \n",
       "42         continuous              Protein QTL           Protein in blood   \n",
       "44         continuous              Protein QTL           Protein in blood   \n",
       "45         continuous              Protein QTL           Protein in blood   \n",
       "\n",
       "          PUBMEDID                                   DISEASE/TRAIT  \\\n",
       "88      OUR_RESULT                                             NaN   \n",
       "13052     21909115                                    hypertension   \n",
       "46605     25249183                                    hypertension   \n",
       "104309    30527956                 pulmonary arterial hypertension   \n",
       "116809    31879980                                    hypertension   \n",
       "116810    31879980                                    hypertension   \n",
       "116832    31879980                                    hypertension   \n",
       "116833    31879980                                    hypertension   \n",
       "10      OUR_RESULT                                             NaN   \n",
       "2555      22036096                         ige grass sensitization   \n",
       "8602      23042114                               atopic dermatitis   \n",
       "8607      23042114                               atopic dermatitis   \n",
       "8608      23042114                               atopic dermatitis   \n",
       "14538     23817569                           self-reported allergy   \n",
       "14539     23817569                           self-reported allergy   \n",
       "16875     23886662                               atopic dermatitis   \n",
       "25072     27182965                                         allergy   \n",
       "32479     29083406  allergic disease (asthma, hay fever or eczema)   \n",
       "32480     29083406  allergic disease (asthma, hay fever or eczema)   \n",
       "35240     29083406  allergic disease (asthma, hay fever or eczema)   \n",
       "35241     29083406  allergic disease (asthma, hay fever or eczema)   \n",
       "35242     29083406  allergic disease (asthma, hay fever or eczema)   \n",
       "35243     29083406  allergic disease (asthma, hay fever or eczema)   \n",
       "35317     29083406  allergic disease (asthma, hay fever or eczema)   \n",
       "46412     25574825                               atopic dermatitis   \n",
       "47939     23817571                          allergic sensitization   \n",
       "47950     23817571                          allergic sensitization   \n",
       "50618     24388013                            asthma and hay fever   \n",
       "51566     25710614                                  peanut allergy   \n",
       "77587     26482879                               atopic dermatitis   \n",
       "...            ...                                             ...   \n",
       "150355    30072576                            blood protein levels   \n",
       "150358    30072576                            blood protein levels   \n",
       "150649    30072576                            blood protein levels   \n",
       "150650    30072576                            blood protein levels   \n",
       "150651    30072576                            blood protein levels   \n",
       "150652    30072576                            blood protein levels   \n",
       "150653    30072576                            blood protein levels   \n",
       "150654    30072576                            blood protein levels   \n",
       "151819    30072576                            blood protein levels   \n",
       "152028    30072576                            blood protein levels   \n",
       "152329    30072576                            blood protein levels   \n",
       "156098    30072576                            blood protein levels   \n",
       "156815    30072576                            blood protein levels   \n",
       "157056    30072576                            blood protein levels   \n",
       "160209    27532455                            blood protein levels   \n",
       "160235    27532455                            blood protein levels   \n",
       "160236    27532455                            blood protein levels   \n",
       "160654    31320639     neurological blood protein biomarker levels   \n",
       "162928    30072576                            blood protein levels   \n",
       "165513    30072576                            blood protein levels   \n",
       "165514    30072576                            blood protein levels   \n",
       "165518    30072576                            blood protein levels   \n",
       "165519    30072576                            blood protein levels   \n",
       "165520    30072576                            blood protein levels   \n",
       "165521    30072576                            blood protein levels   \n",
       "165522    30072576                            blood protein levels   \n",
       "41        30692682                                   total protein   \n",
       "42        30692682                                   total protein   \n",
       "44        30692682                             non-albumin protein   \n",
       "45        30692682                             non-albumin protein   \n",
       "\n",
       "                              MAPPED_TRAIT                         SNPS  \\\n",
       "88                                     NaN  SNPS_B_439_31324497_exon2_G   \n",
       "13052                         hypertension                     rs805303   \n",
       "46605                         hypertension                    rs2021783   \n",
       "104309     pulmonary arterial hypertension                    rs2856830   \n",
       "116809                        hypertension                    rs2524095   \n",
       "116810                        hypertension                    rs2596473   \n",
       "116832                        hypertension                     rs805293   \n",
       "116833                        hypertension                    rs3130482   \n",
       "10                                     NaN               6:32632237_C/T   \n",
       "2555            seasonal allergic rhinitis                    rs7775228   \n",
       "8602                         atopic eczema                    rs9368677   \n",
       "8607                         atopic eczema                     rs176095   \n",
       "8608                         atopic eczema                    rs9469099   \n",
       "14538                              allergy                    rs9266772   \n",
       "14539                              allergy                    rs6906021   \n",
       "16875                        atopic eczema                   rs12153855   \n",
       "25072                              allergy                   rs34004019   \n",
       "32479                              allergy                   rs10947428   \n",
       "32480                              allergy                    rs3097670   \n",
       "35240                              allergy                   rs34004019   \n",
       "35241                              allergy                    rs2854001   \n",
       "35242                              allergy                    rs9259819   \n",
       "35243                              allergy                   rs28895016   \n",
       "35317                              allergy                    rs2507978   \n",
       "46412                        atopic eczema                   rs41268896   \n",
       "47939   allergic sensitization measurement                    rs6906021   \n",
       "47950   allergic sensitization measurement                    rs6932730   \n",
       "50618   seasonal allergic rhinitis, asthma                    rs9273373   \n",
       "51566           peanut allergy measurement                    rs9275596   \n",
       "77587                        atopic eczema                    rs4713555   \n",
       "...                                    ...                          ...   \n",
       "150355           blood protein measurement                    rs2855812   \n",
       "150358           blood protein measurement                    rs2395400   \n",
       "150649           blood protein measurement                  rs114508013   \n",
       "150650           blood protein measurement                  rs114508013   \n",
       "150651           blood protein measurement                  rs114508013   \n",
       "150652           blood protein measurement                  rs114508013   \n",
       "150653           blood protein measurement                  rs114508013   \n",
       "150654           blood protein measurement                  rs114508013   \n",
       "151819           blood protein measurement                    rs4151671   \n",
       "152028           blood protein measurement                    rs1050673   \n",
       "152329           blood protein measurement                     rs769178   \n",
       "156098           blood protein measurement                  rs116298992   \n",
       "156815           blood protein measurement                  rs114887538   \n",
       "157056           blood protein measurement                  rs114508013   \n",
       "160209           blood protein measurement                    rs9263871   \n",
       "160235           blood protein measurement                    rs2256175   \n",
       "160236           blood protein measurement                    rs2239705   \n",
       "160654           blood protein measurement                   rs62397979   \n",
       "162928           blood protein measurement                  rs146306986   \n",
       "165513           blood protein measurement                  rs115878542   \n",
       "165514           blood protein measurement                  rs115878542   \n",
       "165518           blood protein measurement                  rs114887538   \n",
       "165519           blood protein measurement                  rs114887538   \n",
       "165520           blood protein measurement                  rs114887538   \n",
       "165521           blood protein measurement                  rs114887538   \n",
       "165522           blood protein measurement                  rs114887538   \n",
       "41                                     NaN                   rs13197513   \n",
       "42                                     NaN                    rs3132486   \n",
       "44                                     NaN                   rs28752797   \n",
       "45                                     NaN                     rs562289   \n",
       "\n",
       "         CHR_POS                        REPORTED GENE(S)  \\\n",
       "88      31324497                                     NaN   \n",
       "13052   31648589                              BAT2, BAT5   \n",
       "46605   32077074                                 CYP21A2   \n",
       "104309  33073957                      HLA-DPA1, HLA-DPB1   \n",
       "116809  31298340                               LINC02571   \n",
       "116810  31463022                                    HCP5   \n",
       "116832  31720741                                  MPIG6B   \n",
       "116833  31872005                                 SLC44A4   \n",
       "10      32632237                                     NaN   \n",
       "2555    32690302                              HLA region   \n",
       "8602    31304544                                   HLA-C   \n",
       "8607    32190542                                   GPSM3   \n",
       "8608    32341131                                 C6orf10   \n",
       "14538   31384336                             MICA, HLA-C   \n",
       "14539   32658534                      HLA-DQB1, HLA-DQA1   \n",
       "16875   32107027                            TNXB, CREBL1   \n",
       "25072   32658626                      HLA-DQB1, HLA-DQA1   \n",
       "32479   33679281                                   ITPR3   \n",
       "32480   33078975                                HLA-DPA1   \n",
       "35240   32658626                      HLA-DQA1, HLA-DQB1   \n",
       "35241   31355235                                   HLA-B   \n",
       "35242   29925798                                   HLA-J   \n",
       "35243   31606748                              NCR3, AIF1   \n",
       "35317   31383887                             HLA-B, MICA   \n",
       "46412   32102292                                 c6orf10   \n",
       "47939   32658534                      HLA-DQB1, HLA-DQA1   \n",
       "47950   31386405                MICA, HLA-C, HLA-B, MICB   \n",
       "50618   32658824                                HLA-DQB1   \n",
       "51566   32713854  HLA-DRB1, HLA-DQA1, HLA-DQB1, HLA-DQA2   \n",
       "77587   32607747                      HLA-DRB1, HLA-DQA1   \n",
       "...          ...                                     ...   \n",
       "150355  31504943                                    MICB   \n",
       "150358  33802299                                     MLN   \n",
       "150649  31900988                                      C2   \n",
       "150650  31900988                                      C2   \n",
       "150651  31900988                                      C2   \n",
       "150652  31900988                                      C2   \n",
       "150653  31900988                                      C2   \n",
       "150654  31900988                                      C2   \n",
       "151819  31951126                                     CFB   \n",
       "152028  33193884                                 COL11A2   \n",
       "152329  31579737                                    NCR3   \n",
       "156098  32100718                                    TNXB   \n",
       "156815  32146203                                   ATF6B   \n",
       "157056  31900988                                      C2   \n",
       "160209  31202751                                      NR   \n",
       "160235  31412672                                      NR   \n",
       "160236  31545625                                      NR   \n",
       "160654  31515429                        XXbac-BPG16N22.5   \n",
       "162928  33224636                                   TAPBP   \n",
       "165513  31707520                                    APOM   \n",
       "165514  31707520                                    APOM   \n",
       "165518  32146203                                   ATF6B   \n",
       "165519  32146203                                   ATF6B   \n",
       "165520  32146203                                   ATF6B   \n",
       "165521  32146203                                   ATF6B   \n",
       "165522  32146203                                   ATF6B   \n",
       "41      32990121                                HLA-DPB1   \n",
       "42      31243170                                   HLA-C   \n",
       "44      31291172                                   HLA-C   \n",
       "45      32577046                                HLA-DRB1   \n",
       "\n",
       "                GENE_CUSTOM_REPORTED_GENE_CUSTOM  \\\n",
       "88                                         HLA-B   \n",
       "13052                                     [BAG6]   \n",
       "46605                            [CYP21A2, TNXB]   \n",
       "104309                      [HLA-DPA1, HLA-DPB1]   \n",
       "116809                                   [HLA-B]   \n",
       "116810                              [MICA, MICB]   \n",
       "116832                         [C6orf25, LY6G6C]   \n",
       "116833                                 [SLC44A4]   \n",
       "10                                      HLA-DQB1   \n",
       "2555                        [HLA-DQA2, HLA-DQB1]   \n",
       "8602                              [HLA-B, HLA-C]   \n",
       "8607                               [GPSM3, PBX2]   \n",
       "8608                                   [C6orf10]   \n",
       "14538                       [HLA-B, HLA-C, MICA]   \n",
       "14539                       [HLA-DQA1, HLA-DQB1]   \n",
       "16875                                     [TNXB]   \n",
       "25072                       [HLA-DQA1, HLA-DQB1]   \n",
       "32479                                    [ITPR3]   \n",
       "32480                       [HLA-DPA1, HLA-DPB1]   \n",
       "35240                       [HLA-DQA1, HLA-DQB1]   \n",
       "35241                                    [HLA-B]   \n",
       "35242                             [HLA-A, HLA-G]   \n",
       "35243                               [AIF1, NCR3]   \n",
       "35317                              [HLA-B, MICA]   \n",
       "46412                                     [TNXB]   \n",
       "47939                       [HLA-DQA1, HLA-DQB1]   \n",
       "47950                 [HLA-B, HLA-C, MICA, MICB]   \n",
       "50618                       [HLA-DQA1, HLA-DQB1]   \n",
       "51566   [HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DRB1]   \n",
       "77587                       [HLA-DQA1, HLA-DRB1]   \n",
       "...                                          ...   \n",
       "150355                                    [MICB]   \n",
       "150358                                     [MLN]   \n",
       "150649                              [C2, ZBTB12]   \n",
       "150650                              [C2, ZBTB12]   \n",
       "150651                              [C2, ZBTB12]   \n",
       "150652                              [C2, ZBTB12]   \n",
       "150653                              [C2, ZBTB12]   \n",
       "150654                              [C2, ZBTB12]   \n",
       "151819                                     [CFB]   \n",
       "152028                           [COL11A2, RXRB]   \n",
       "152329                          [LTB, NCR3, TNF]   \n",
       "156098                                    [TNXB]   \n",
       "156815                     [ATF6B, FKBPL, PRRT1]   \n",
       "157056                              [C2, ZBTB12]   \n",
       "160209                                   [HCG27]   \n",
       "160235                                    [MICA]   \n",
       "160236                                [ATP6V1G2]   \n",
       "160654                             [MCCD1, MICB]   \n",
       "162928                     [RING1, TAPBP, VPS52]   \n",
       "165513   [APOM, LY6G6F, MEGT1, XXbac-BPG32J3.20]   \n",
       "165514   [APOM, LY6G6F, MEGT1, XXbac-BPG32J3.20]   \n",
       "165518                     [ATF6B, FKBPL, PRRT1]   \n",
       "165519                     [ATF6B, FKBPL, PRRT1]   \n",
       "165520                     [ATF6B, FKBPL, PRRT1]   \n",
       "165521                     [ATF6B, FKBPL, PRRT1]   \n",
       "165522                     [ATF6B, FKBPL, PRRT1]   \n",
       "41                 [HLA-DOA, HLA-DPA1, HLA-DPB1]   \n",
       "42                                [HLA-B, HLA-C]   \n",
       "44                                [HLA-B, HLA-C]   \n",
       "45                          [HLA-DQA1, HLA-DRB1]   \n",
       "\n",
       "                              genehow novelty  \n",
       "88                            on_gene       -  \n",
       "13052   reported_gene+on_gene,nearest     NaN  \n",
       "46605   reported_gene+on_gene,nearest     NaN  \n",
       "104309  reported_gene+on_gene,nearest     NaN  \n",
       "116809  reported_gene+on_gene,nearest     NaN  \n",
       "116810  reported_gene+on_gene,nearest     NaN  \n",
       "116832  reported_gene+on_gene,nearest     NaN  \n",
       "116833  reported_gene+on_gene,nearest     NaN  \n",
       "10                            on_gene       -  \n",
       "2555    reported_gene+on_gene,nearest     NaN  \n",
       "8602    reported_gene+on_gene,nearest     NaN  \n",
       "8607    reported_gene+on_gene,nearest     NaN  \n",
       "8608    reported_gene+on_gene,nearest     NaN  \n",
       "14538   reported_gene+on_gene,nearest     NaN  \n",
       "14539   reported_gene+on_gene,nearest     NaN  \n",
       "16875   reported_gene+on_gene,nearest     NaN  \n",
       "25072   reported_gene+on_gene,nearest     NaN  \n",
       "32479   reported_gene+on_gene,nearest     NaN  \n",
       "32480   reported_gene+on_gene,nearest     NaN  \n",
       "35240   reported_gene+on_gene,nearest     NaN  \n",
       "35241   reported_gene+on_gene,nearest     NaN  \n",
       "35242   reported_gene+on_gene,nearest     NaN  \n",
       "35243   reported_gene+on_gene,nearest     NaN  \n",
       "35317   reported_gene+on_gene,nearest     NaN  \n",
       "46412   reported_gene+on_gene,nearest     NaN  \n",
       "47939   reported_gene+on_gene,nearest     NaN  \n",
       "47950   reported_gene+on_gene,nearest     NaN  \n",
       "50618   reported_gene+on_gene,nearest     NaN  \n",
       "51566   reported_gene+on_gene,nearest     NaN  \n",
       "77587   reported_gene+on_gene,nearest     NaN  \n",
       "...                               ...     ...  \n",
       "150355  reported_gene+on_gene,nearest     NaN  \n",
       "150358  reported_gene+on_gene,nearest     NaN  \n",
       "150649  reported_gene+on_gene,nearest     NaN  \n",
       "150650  reported_gene+on_gene,nearest     NaN  \n",
       "150651  reported_gene+on_gene,nearest     NaN  \n",
       "150652  reported_gene+on_gene,nearest     NaN  \n",
       "150653  reported_gene+on_gene,nearest     NaN  \n",
       "150654  reported_gene+on_gene,nearest     NaN  \n",
       "151819  reported_gene+on_gene,nearest     NaN  \n",
       "152028  reported_gene+on_gene,nearest     NaN  \n",
       "152329  reported_gene+on_gene,nearest     NaN  \n",
       "156098  reported_gene+on_gene,nearest     NaN  \n",
       "156815  reported_gene+on_gene,nearest     NaN  \n",
       "157056  reported_gene+on_gene,nearest     NaN  \n",
       "160209  reported_gene+on_gene,nearest     NaN  \n",
       "160235  reported_gene+on_gene,nearest     NaN  \n",
       "160236  reported_gene+on_gene,nearest     NaN  \n",
       "160654  reported_gene+on_gene,nearest     NaN  \n",
       "162928  reported_gene+on_gene,nearest     NaN  \n",
       "165513  reported_gene+on_gene,nearest     NaN  \n",
       "165514  reported_gene+on_gene,nearest     NaN  \n",
       "165518  reported_gene+on_gene,nearest     NaN  \n",
       "165519  reported_gene+on_gene,nearest     NaN  \n",
       "165520  reported_gene+on_gene,nearest     NaN  \n",
       "165521  reported_gene+on_gene,nearest     NaN  \n",
       "165522  reported_gene+on_gene,nearest     NaN  \n",
       "41      reported_gene+on_gene,nearest     NaN  \n",
       "42      reported_gene+on_gene,nearest     NaN  \n",
       "44      reported_gene+on_gene,nearest     NaN  \n",
       "45      reported_gene+on_gene,nearest     NaN  \n",
       "\n",
       "[1149 rows x 12 columns]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "novelty_check_df[['phenotype_type', 'phenotype_category', 'phenotype_name_publication',\n",
    "        'PUBMEDID', 'DISEASE/TRAIT', 'MAPPED_TRAIT', 'SNPS',\n",
    "       'CHR_POS', 'REPORTED GENE(S)', 'GENE_CUSTOM_REPORTED_GENE_CUSTOM',\n",
    "       'genehow', 'novelty', \n",
    "       ]].sort_values(['phenotype_type','phenotype_category'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phewas plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(66, 3)"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phewas_data=result_all[result_all['P_significant']==1][['phenotype_name','gene','P']]\n",
    "phewas_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>phenotype_name</th>\n",
       "      <th>gene</th>\n",
       "      <th>P</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>hematocrit</td>\n",
       "      <td>RNF5,AGER</td>\n",
       "      <td>9.197860e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>hematocrit</td>\n",
       "      <td>HLA-B</td>\n",
       "      <td>1.049540e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>hematocrit</td>\n",
       "      <td>HLA-F</td>\n",
       "      <td>9.911020e-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   phenotype_name       gene             P\n",
       "74     hematocrit  RNF5,AGER  9.197860e-13\n",
       "75     hematocrit      HLA-B  1.049540e-09\n",
       "76     hematocrit      HLA-F  9.911020e-09"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phewas_data[phewas_data['phenotype_name']=='hematocrit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(67, 3)"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phewas_data.loc[phewas_data.index.max()+1]=['hematocrit','RNF5',9.197860e-13]\n",
    "phewas_data.loc[phewas_data.index.max()+1]=['hematocrit','AGER',9.197860e-13]\n",
    "phewas_data=phewas_data[~((phewas_data['phenotype_name']=='hematocrit') &(phewas_data['gene']=='RNF5,AGER'))]\n",
    "phewas_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "#result_all[result_all['phenotype_name']=='platelet']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>phenotype_name</th>\n",
       "      <th>gene</th>\n",
       "      <th>P</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>platelet</td>\n",
       "      <td>BAK1</td>\n",
       "      <td>1.579220e-69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>platelet</td>\n",
       "      <td>BAK1</td>\n",
       "      <td>1.105480e-33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    phenotype_name  gene             P\n",
       "109       platelet  BAK1  1.579220e-69\n",
       "111       platelet  BAK1  1.105480e-33"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phewas_data[phewas_data.duplicated(['phenotype_name','gene'],keep=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>phenotype_name</th>\n",
       "      <th>gene</th>\n",
       "      <th>P</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>platelet</td>\n",
       "      <td>BAK1</td>\n",
       "      <td>1.105480e-33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    phenotype_name  gene             P\n",
       "111       platelet  BAK1  1.105480e-33"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phewas_data[phewas_data.duplicated(['phenotype_name','gene'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(66, 3)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phewas_data=phewas_data[~phewas_data.duplicated(['phenotype_name','gene'])]\n",
    "phewas_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "phewas_data_2d=phewas_data.pivot(index='phenotype_name',columns='gene',values='P')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "phewas_data_2d=phewas_data_2d.applymap(lambda x: -np.log10(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "phewas_data_2d=phewas_data_2d[gene_bed.set_index('Gene name').loc[phewas_data_2d.columns].sort_values('Gene start (bp)').index]\n",
    "phewas_data_2d=phewas_data_2d.loc[phenotype_info.loc[phewas_data_2d.index].sort_values(['phenotype_type','phenotype_category']).index]\n",
    "#phewas_data_2d.index=phenotype_info.loc[phewas_data_2d.index]['phenotype_name_publication']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(phewas_data['phenotype_name'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(phewas_data['gene'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35, 26)"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phewas_data_2d.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(75.68871882374123, 7.336711897616068)"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phewas_data_2d.max().max(),phewas_data_2d.min().min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nboundaries = [0, 0.1,0.2,0.3,,0.4,1]  # custom boundaries\\ncustom_cmap=cm.get_cmap('rainbow')\\ncustom_cmap_array=custom_cmap(np.linspace(0, 1, len(boundaries)))\\n# here I generated twice as many colors, \\n# so that I could prune the boundaries more clearly\\n#hex_colors = sns.light_palette('navy', n_colors=len(boundaries) * 2 + 2, as_cmap=False).as_hex()\\n#hex_colors = [hex_colors[i] for i in range(0, len(hex_colors), 2)]\\n\\ncolors=list(zip(boundaries, custom_cmap_array))\\n\\ncustom_cmap = LinearSegmentedColormap.from_list(\\n    name='custom_navy',\\n    colors=colors,\\n)\\n\""
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap,LinearSegmentedColormap\n",
    "from matplotlib import cm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "custom_cmap=cm.get_cmap('rainbow')\n",
    "custom_cmap_array=custom_cmap(np.linspace(0, 1, 100))\n",
    "#custom_cmap_array=np.concatenate([np.array([[1,1,1,1]]*100),custom_cmap_array],axis=0)\n",
    "custom_cmap_array=np.array([[min(rgba[0]+0.1,1),min(rgba[1]+0.1,1),min(rgba[2]+0.1,1),rgba[3]] for rgba in custom_cmap_array])\n",
    "#custom_cmap_array[0][-1]=0\n",
    "custom_cmap_array[0]=[0.8,0.8,1,1]\n",
    "#custom_cmap_array[0]=custom_cmap_array[0]*2\n",
    "#custom_cmap_array=\n",
    "#custom_cmap_array=[custom_cmap_array[0],custom_cmap_array[20],custom_cmap_array[40],custom_cmap_array[60]]\n",
    "\n",
    "#custom_cmap_array=custom_cmap_array[30:]\n",
    "custom_cmap = ListedColormap(custom_cmap_array)\n",
    "\n",
    "\"\"\"\n",
    "#boundaries = [0, 10, 15, 20, 30, 50,100]  # custom boundaries\n",
    "#boundaries = [0, 0.1,0.2,0.3,,0.4,1]  # custom boundaries\n",
    "#custom_cmap=cm.get_cmap('rainbow')\n",
    "#custom_cmap_array=custom_cmap(np.linspace(0, 1, len(boundaries)))\n",
    "# here I generated twice as many colors, \n",
    "# so that I could prune the boundaries more clearly\n",
    "#hex_colors = sns.light_palette('navy', n_colors=len(boundaries) * 2 + 2, as_cmap=False).as_hex()\n",
    "#hex_colors = [hex_colors[i] for i in range(0, len(hex_colors), 2)]\n",
    "\n",
    "colors=list(zip(boundaries, custom_cmap_array))\n",
    "\n",
    "custom_cmap = LinearSegmentedColormap.from_list(\n",
    "    name='custom_navy',\n",
    "    colors=colors,\n",
    ")\n",
    "\"\"\"\n",
    "\n",
    "\"\"\"\n",
    "viridisBig = cm.get_cmap('rainbow')\n",
    "\n",
    "newcmp = ListedColormap(viridisBig(np.linspace(0, 1, 512)))\n",
    "\n",
    "def plot_examples(cms):\n",
    "    np.random.seed(19680801)\n",
    "    data = np.random.randn(30, 30)\n",
    "\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(6, 3), constrained_layout=True)\n",
    "    for [ax, cmap] in zip(axs, cms):\n",
    "        psm = ax.pcolormesh(data, cmap=cmap, rasterized=True, vmin=-4, vmax=4)\n",
    "        fig.colorbar(psm, ax=ax)\n",
    "    plt.show()\n",
    "\n",
    "plot_examples([newcmp])\n",
    "\"\"\"\n",
    "    \n",
    "    \n",
    "\"\"\"\n",
    "boundaries = [0, 0.1,0.2,0.3,,0.4,1]  # custom boundaries\n",
    "custom_cmap=cm.get_cmap('rainbow')\n",
    "custom_cmap_array=custom_cmap(np.linspace(0, 1, len(boundaries)))\n",
    "# here I generated twice as many colors, \n",
    "# so that I could prune the boundaries more clearly\n",
    "#hex_colors = sns.light_palette('navy', n_colors=len(boundaries) * 2 + 2, as_cmap=False).as_hex()\n",
    "#hex_colors = [hex_colors[i] for i in range(0, len(hex_colors), 2)]\n",
    "\n",
    "colors=list(zip(boundaries, custom_cmap_array))\n",
    "\n",
    "custom_cmap = LinearSegmentedColormap.from_list(\n",
    "    name='custom_navy',\n",
    "    colors=colors,\n",
    ")\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "phenotype_info=pd.read_csv('data/phenotype_pubname.tsv',sep='\\t',index_col=0)\n",
    "\n",
    "#data_out_assoc_phenotype_path=data_out_assoc_path+phenotype_name+'/'\n",
    "#pathlib.Path(data_out_assoc_phenotype_path).mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "#for i in binary_continuous_traits:\n",
    "#    if not os.path.exists('data/out_assoc/{}/step_01.plink.PHENO2.glm.linear'.format(i)) and not os.path.exists('data/out_assoc/{}/step_01.plink.PHENO2.glm.logistic'.format(i)):\n",
    "#        print(i)\n",
    "\n",
    "#pheno=pd.read_csv(data_out_pheno_path+phenotype_name+'.phe',sep='\\t',names=['FID','IID','pheno'])\n",
    "#phenotype_type='binary' if len(pheno['pheno'][pheno['pheno']!=-9].value_counts())<3 else 'continuous'\n",
    "#phenotype_type\n",
    "#for phenotype_name,row in phenotype_info.iterrows():\n",
    "#    pheno=pd.read_csv(data_out_pheno_path+phenotype_name+'.phe',sep='\\t',names=['FID','IID','pheno'])\n",
    "#    phenotype_type='binary' if len(pheno['pheno'][pheno['pheno']!=-9].value_counts())<3 else 'continuous'\n",
    "#    phenotype_info['phenotype_type'][phenotype_name]=phenotype_type    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ch6845/tools/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: DeprecationWarning: object of type <class 'numpy.float64'> cannot be safely interpreted as an integer.\n",
      "  if sys.path[0] == '':\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "phewas_data_2d_count=(phewas_data_2d>0).sum().to_frame().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "phewas_data_2d_count['color']=phewas_data_2d_count['gene'].map(gene_to_color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene</th>\n",
       "      <th>0</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SCAND3</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.29296875, 0.29296875, 0.234375, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TRIM27</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.29296875, 0.29296875, 0.234375, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C6orf100</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.29296875, 0.29296875, 0.234375, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UBD</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.29296875, 0.29296875, 0.234375, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HLA-F</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.15625, 0.78125, 0.15625, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HLA-A</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.9765625, 0.2265625, 0.078125, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HLA-E</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.15625, 0.78125, 0.15625, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>IER3</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.29296875, 0.29296875, 0.234375, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MUC22</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.29296875, 0.29296875, 0.234375, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>HLA-C</td>\n",
       "      <td>5</td>\n",
       "      <td>[0.9765625, 0.2265625, 0.078125, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HLA-B</td>\n",
       "      <td>8</td>\n",
       "      <td>[0.9765625, 0.2265625, 0.078125, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MICA</td>\n",
       "      <td>3</td>\n",
       "      <td>[0.15625, 0.78125, 0.15625, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MICB</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.15625, 0.78125, 0.15625, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ATF6B</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.29296875, 0.29296875, 0.234375, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>RNF5</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.29296875, 0.29296875, 0.234375, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>AGER</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.29296875, 0.29296875, 0.234375, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NOTCH4</td>\n",
       "      <td>3</td>\n",
       "      <td>[0.29296875, 0.29296875, 0.234375, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>C6orf10</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.29296875, 0.29296875, 0.234375, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>HLA-DRB1</td>\n",
       "      <td>7</td>\n",
       "      <td>[0.15625, 0.3125, 0.78125, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>HLA-DQA1</td>\n",
       "      <td>3</td>\n",
       "      <td>[0.15625, 0.3125, 0.78125, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>HLA-DQB1</td>\n",
       "      <td>4</td>\n",
       "      <td>[0.15625, 0.3125, 0.78125, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>HLA-DPB1</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.15625, 0.3125, 0.78125, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>BAK1</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.29296875, 0.29296875, 0.234375, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>HMGA1</td>\n",
       "      <td>12</td>\n",
       "      <td>[0.29296875, 0.29296875, 0.234375, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>UHRF1BP1</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.29296875, 0.29296875, 0.234375, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>TAF11</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.29296875, 0.29296875, 0.234375, 1.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        gene   0                                    color\n",
       "0     SCAND3   1  [0.29296875, 0.29296875, 0.234375, 1.0]\n",
       "1     TRIM27   2  [0.29296875, 0.29296875, 0.234375, 1.0]\n",
       "2   C6orf100   1  [0.29296875, 0.29296875, 0.234375, 1.0]\n",
       "3        UBD   1  [0.29296875, 0.29296875, 0.234375, 1.0]\n",
       "4      HLA-F   1         [0.15625, 0.78125, 0.15625, 1.0]\n",
       "5      HLA-A   2    [0.9765625, 0.2265625, 0.078125, 1.0]\n",
       "6      HLA-E   1         [0.15625, 0.78125, 0.15625, 1.0]\n",
       "7       IER3   1  [0.29296875, 0.29296875, 0.234375, 1.0]\n",
       "8      MUC22   2  [0.29296875, 0.29296875, 0.234375, 1.0]\n",
       "9      HLA-C   5    [0.9765625, 0.2265625, 0.078125, 1.0]\n",
       "10     HLA-B   8    [0.9765625, 0.2265625, 0.078125, 1.0]\n",
       "11      MICA   3         [0.15625, 0.78125, 0.15625, 1.0]\n",
       "12      MICB   1         [0.15625, 0.78125, 0.15625, 1.0]\n",
       "13     ATF6B   1  [0.29296875, 0.29296875, 0.234375, 1.0]\n",
       "14      RNF5   1  [0.29296875, 0.29296875, 0.234375, 1.0]\n",
       "15      AGER   1  [0.29296875, 0.29296875, 0.234375, 1.0]\n",
       "16    NOTCH4   3  [0.29296875, 0.29296875, 0.234375, 1.0]\n",
       "17   C6orf10   1  [0.29296875, 0.29296875, 0.234375, 1.0]\n",
       "18  HLA-DRB1   7          [0.15625, 0.3125, 0.78125, 1.0]\n",
       "19  HLA-DQA1   3          [0.15625, 0.3125, 0.78125, 1.0]\n",
       "20  HLA-DQB1   4          [0.15625, 0.3125, 0.78125, 1.0]\n",
       "21  HLA-DPB1   1          [0.15625, 0.3125, 0.78125, 1.0]\n",
       "22      BAK1   1  [0.29296875, 0.29296875, 0.234375, 1.0]\n",
       "23     HMGA1  12  [0.29296875, 0.29296875, 0.234375, 1.0]\n",
       "24  UHRF1BP1   1  [0.29296875, 0.29296875, 0.234375, 1.0]\n",
       "25     TAF11   1  [0.29296875, 0.29296875, 0.234375, 1.0]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phewas_data_2d_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gene stable ID                                                                  ENSG00000204308\n",
       "Gene stable ID version                                                        ENSG00000204308.6\n",
       "Transcript stable ID                                                            ENST00000375094\n",
       "Transcript stable ID version                                                  ENST00000375094.3\n",
       "Gene start (bp)                                                                        32146131\n",
       "Gene end (bp)                                                                          32151930\n",
       "Transcript start (bp)                                                                  32146131\n",
       "Transcript end (bp)                                                                    32148570\n",
       "Protein stable ID                                                               ENSP00000364235\n",
       "Protein stable ID version                                                     ENSP00000364235.3\n",
       "Gene description                              ring finger protein 5, E3 ubiquitin protein li...\n",
       "Chromosome/scaffold name                                                                      6\n",
       "Strand                                                                                        1\n",
       "Karyotype band                                                                           p21.32\n",
       "Transcription start site (TSS)                                                         32146131\n",
       "Version (protein)                                                                             3\n",
       "Version (transcript)                                                                          3\n",
       "Version (gene)                                                                                6\n",
       "Source (transcript)                                                              ensembl_havana\n",
       "Source (gene)                                                                    ensembl_havana\n",
       "Transcript type                                                                  protein_coding\n",
       "Gene type                                                                        protein_coding\n",
       "Gene % GC content                                                                         54.05\n",
       "Transcript count                                                                              3\n",
       "Source of transcript name                                                  HGNC transcript name\n",
       "Transcript name                                                                        RNF5-001\n",
       "Source of gene name                                                                 HGNC Symbol\n",
       "GENCODE basic annotation                                                                      1\n",
       "Transcript length (including UTRs and CDS)                                                 1168\n",
       "Gene stable ID38                                                                ENSG00000204308\n",
       "Gene start (bp)38                                                                   3.21784e+07\n",
       "Gene end (bp)38                                                                     3.21808e+07\n",
       "Name: RNF5, dtype: object"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_bed.set_index('Gene name').loc['RNF5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'HMGA1', 'HLA-C', 'HLA-DQB1', 'MICA', 'HLA-DRB1', 'HLA-DPB1',\n",
       "       'HLA-A', 'HLA-DQA1', 'TRIM27', 'HLA-E', 'NOTCH4', 'UHRF1BP1',\n",
       "       'MUC22', 'SCAND3', 'TAF11', 'IER3', 'RNF5,AGER', 'HLA-B', 'HLA-F',\n",
       "       'C6orf100', 'MICB', 'BAK1', 'C6orf10', 'UBD', 'ATF6B'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_all['gene'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SCAND3', 'TRIM27', 'C6orf100', 'UBD', 'HLA-F', 'HLA-A', 'HLA-E',\n",
       "       'IER3', 'MUC22', 'HLA-C', 'HLA-B', 'MICA', 'MICB', 'ATF6B', 'RNF5',\n",
       "       'AGER', 'NOTCH4', 'C6orf10', 'HLA-DRB1', 'HLA-DQA1', 'HLA-DQB1',\n",
       "       'HLA-DPB1', 'BAK1', 'HMGA1', 'UHRF1BP1', 'TAF11'],\n",
       "      dtype='object', name='gene')"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phewas_data_2d.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SCAND3\n",
      "(66, 25)\n",
      "(27, 25)\n",
      "(27, 25)\n",
      "---------------------------------\n",
      "TRIM27\n",
      "(78, 25)\n",
      "(33, 25)\n",
      "(32, 25)\n",
      "---------------------------------\n",
      "C6orf100\n",
      "(12, 25)\n",
      "(11, 25)\n",
      "(11, 25)\n",
      "---------------------------------\n",
      "UBD\n",
      "(18, 25)\n",
      "(11, 25)\n",
      "(10, 25)\n",
      "---------------------------------\n",
      "HLA-F\n",
      "(38, 25)\n",
      "(22, 25)\n",
      "(22, 25)\n",
      "---------------------------------\n",
      "HLA-A\n",
      "(113, 25)\n",
      "(68, 25)\n",
      "(67, 25)\n",
      "---------------------------------\n",
      "HLA-E\n",
      "(53, 25)\n",
      "(26, 25)\n",
      "(25, 25)\n",
      "---------------------------------\n",
      "IER3\n",
      "(45, 25)\n",
      "(21, 25)\n",
      "(21, 25)\n",
      "---------------------------------\n",
      "MUC22\n",
      "(57, 25)\n",
      "(34, 25)\n",
      "(32, 25)\n",
      "---------------------------------\n",
      "HLA-C\n",
      "(155, 25)\n",
      "(86, 25)\n",
      "(81, 25)\n",
      "---------------------------------\n",
      "HLA-B\n",
      "(305, 25)\n",
      "(146, 25)\n",
      "(139, 25)\n",
      "---------------------------------\n",
      "MICA\n",
      "(233, 25)\n",
      "(119, 25)\n",
      "(115, 25)\n",
      "---------------------------------\n",
      "MICB\n",
      "(159, 25)\n",
      "(76, 25)\n",
      "(71, 25)\n",
      "---------------------------------\n",
      "ATF6B\n",
      "(37, 25)\n",
      "(16, 25)\n",
      "(14, 25)\n",
      "---------------------------------\n",
      "RNF5\n",
      "(24, 25)\n",
      "(8, 25)\n",
      "(8, 25)\n",
      "---------------------------------\n",
      "AGER\n",
      "(39, 25)\n",
      "(20, 25)\n",
      "(19, 25)\n",
      "---------------------------------\n",
      "NOTCH4\n",
      "(83, 25)\n",
      "(49, 25)\n",
      "(48, 25)\n",
      "---------------------------------\n",
      "C6orf10\n",
      "(108, 25)\n",
      "(57, 25)\n",
      "(54, 25)\n",
      "---------------------------------\n",
      "HLA-DRB1\n",
      "(315, 25)\n",
      "(109, 25)\n",
      "(107, 25)\n",
      "---------------------------------\n",
      "HLA-DQA1\n",
      "(356, 25)\n",
      "(150, 25)\n",
      "(149, 25)\n",
      "---------------------------------\n",
      "HLA-DQB1\n",
      "(283, 25)\n",
      "(134, 25)\n",
      "(131, 25)\n",
      "---------------------------------\n",
      "HLA-DPB1\n",
      "(62, 25)\n",
      "(40, 25)\n",
      "(40, 25)\n",
      "---------------------------------\n",
      "BAK1\n",
      "(35, 25)\n",
      "(17, 25)\n",
      "(16, 25)\n",
      "---------------------------------\n",
      "HMGA1\n",
      "(91, 25)\n",
      "(31, 25)\n",
      "(31, 25)\n",
      "---------------------------------\n",
      "UHRF1BP1\n",
      "(38, 25)\n",
      "(19, 25)\n",
      "(19, 25)\n",
      "---------------------------------\n",
      "TAF11\n",
      "(3, 25)\n",
      "(3, 25)\n",
      "(3, 25)\n",
      "---------------------------------\n"
     ]
    }
   ],
   "source": [
    "phewas_data_2d_columns_catalog=[]\n",
    "for gene_name in phewas_data_2d.columns:\n",
    "    \n",
    "    if gene_name!='HMGA1':\n",
    "        pass#continue\n",
    "    print(gene_name)\n",
    "    catalog_chr6_short_select=catalog_chr6_short[catalog_chr6_short['GENE_CUSTOM_REPORTED_GENE_CUSTOM'].map(lambda x: gene_name in x)]\n",
    "    print(catalog_chr6_short_select.shape)\n",
    "\n",
    "    catalog_chr6_short_select=catalog_chr6_short_select[~catalog_chr6_short_select.duplicated('MAPPED_TRAIT')]\n",
    "    print(catalog_chr6_short_select.shape)\n",
    "    \n",
    "    catalog_chr6_short_select=catalog_chr6_short_select[~catalog_chr6_short_select.duplicated('DISEASE/TRAIT')]\n",
    "    print(catalog_chr6_short_select.shape)    \n",
    "    #catalog_chr6_short_select=catalog_chr6_short_select[~catalog_chr6_short_select.duplicated('PUBMEDID')]\n",
    "    #catalog_chr6_short_select=catalog_chr6_short_select[~catalog_chr6_short_select.duplicated('PUBMEDID')]\n",
    "    catalog_chr6_short_select['gene_name']=gene_name\n",
    "    phewas_data_2d_columns_catalog.append(catalog_chr6_short_select)\n",
    "    print('---------------------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26, 3)"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phewas_data_2d_columns_catalog_all=pd.concat(phewas_data_2d_columns_catalog)\n",
    "\n",
    "phewas_data_2d_columns_catalog_all=phewas_data_2d_columns_catalog_all[[phewas_data_2d_columns_catalog_all.columns[-1]]+phewas_data_2d_columns_catalog_all.columns[:-1].tolist()]\n",
    "\n",
    "#catalog_chr6_short_select#['MAPPED_TRAIT']\n",
    "\n",
    "phewas_data_2d.columns\n",
    "\n",
    "phewas_data_2d_columns_catalog_all_count=phewas_data_2d_columns_catalog_all['gene_name'].value_counts().to_frame().reset_index()\n",
    "\n",
    "phewas_data_2d_columns_catalog_all_count=phewas_data_2d_columns_catalog_all_count.loc[phewas_data_2d_columns_catalog_all_count.reset_index().set_index('index').loc[phewas_data_2d_count['gene']]['level_0'].values]\n",
    "\n",
    "phewas_data_2d_columns_catalog_all_count['color']=phewas_data_2d_columns_catalog_all_count['index'].map(gene_to_color)\n",
    "\n",
    "phewas_data_2d_count.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "phewas_data_2d_columns_catalog_all.to_csv('data/temp_catalog_match.tsv',sep='\\t',index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26, 3)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phewas_data_2d_columns_catalog_all_count.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "#phewas_data_2d_columns_catalog_all_count\n",
    "#phewas_data_2d_columns_catalog_all_count\n",
    "#phewas_data_2d_columns_catalog_all_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "#phewas_data_2d_columns_catalog_all[phewas_data_2d_columns_catalog_all['gene_name']=='HMGA1'][['DISEASE/TRAIT','MAPPED_TRAIT']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "phewas_data_2d_columns_catalog_all_count['color']=phewas_data_2d_columns_catalog_all_count['color'].map(lambda x: [min(x[0]+0.3,1),min(x[1]+0.3,1),min(x[2]+0.6,1),x[3]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14    [0.59296875, 0.59296875, 0.834375, 1.0]\n",
       "12    [0.59296875, 0.59296875, 0.834375, 1.0]\n",
       "22    [0.59296875, 0.59296875, 0.834375, 1.0]\n",
       "23    [0.59296875, 0.59296875, 0.834375, 1.0]\n",
       "16                 [0.45625, 1, 0.75625, 1.0]\n",
       "7               [1, 0.5265625, 0.678125, 1.0]\n",
       "15                 [0.45625, 1, 0.75625, 1.0]\n",
       "17    [0.59296875, 0.59296875, 0.834375, 1.0]\n",
       "11    [0.59296875, 0.59296875, 0.834375, 1.0]\n",
       "5               [1, 0.5265625, 0.678125, 1.0]\n",
       "1               [1, 0.5265625, 0.678125, 1.0]\n",
       "3                  [0.45625, 1, 0.75625, 1.0]\n",
       "6                  [0.45625, 1, 0.75625, 1.0]\n",
       "21    [0.59296875, 0.59296875, 0.834375, 1.0]\n",
       "24    [0.59296875, 0.59296875, 0.834375, 1.0]\n",
       "18    [0.59296875, 0.59296875, 0.834375, 1.0]\n",
       "9     [0.59296875, 0.59296875, 0.834375, 1.0]\n",
       "8     [0.59296875, 0.59296875, 0.834375, 1.0]\n",
       "4                   [0.45625, 0.6125, 1, 1.0]\n",
       "0                   [0.45625, 0.6125, 1, 1.0]\n",
       "2                   [0.45625, 0.6125, 1, 1.0]\n",
       "10                  [0.45625, 0.6125, 1, 1.0]\n",
       "20    [0.59296875, 0.59296875, 0.834375, 1.0]\n",
       "13    [0.59296875, 0.59296875, 0.834375, 1.0]\n",
       "19    [0.59296875, 0.59296875, 0.834375, 1.0]\n",
       "25    [0.59296875, 0.59296875, 0.834375, 1.0]\n",
       "Name: color, dtype: object"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phewas_data_2d_columns_catalog_all_count['color']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (8,2)\n",
    "plt.rcParams[\"font.size\"] = 15\n",
    "plt.rcParams['font.family']='Arial'\n",
    "\n",
    "ax=sns.barplot(x='index',y='gene_name',data=phewas_data_2d_columns_catalog_all_count,palette=list(phewas_data_2d_columns_catalog_all_count['color'].values),alpha=0.5)\n",
    "#sns.barplot(x='index',y='gene_name',data=phewas_data_2d_columns_catalog_all_count,palette=list(phewas_data_2d_columns_catalog_all_count['color'].values),alpha=0.5)\n",
    "\n",
    "\n",
    "\n",
    "#ax.spines['right'].set_visible(False)\n",
    "#ax.spines['top'].set_visible(False)\n",
    "ax.xaxis.set_visible(False)\n",
    "plt.xticks(fontsize=5)\n",
    "plt.gca().invert_yaxis()\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "plt.ylabel('GWAS catalog')\n",
    "#plt.xlabel('ss')\n",
    "\n",
    "ax2 = ax.twinx() \n",
    "ax2=sns.barplot(x='gene',y=0,data=phewas_data_2d_count,palette=list(phewas_data_2d_count['color'].values),alpha=0.7)\n",
    "plt.gca().invert_yaxis()\n",
    "ax2.spines['bottom'].set_visible(False)\n",
    "plt.ylabel('our result')\n",
    "\n",
    "plt.savefig('figure_phewas2.svg', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "phewas_data_2d_noveltycheck=phewas_data_2d.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>phenotype_name</th>\n",
       "      <th>PUBMEDID</th>\n",
       "      <th>DISEASE/TRAIT</th>\n",
       "      <th>MAPPED_TRAIT</th>\n",
       "      <th>SNPS</th>\n",
       "      <th>CHR_POS</th>\n",
       "      <th>REPORTED GENE(S)</th>\n",
       "      <th>GENE_CUSTOM_REPORTED_GENE_CUSTOM</th>\n",
       "      <th>genehow</th>\n",
       "      <th>novelty</th>\n",
       "      <th>phenotype_category</th>\n",
       "      <th>phenotype_type</th>\n",
       "      <th>phenotype_name_publication</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>hematocrit</td>\n",
       "      <td>OUR_RESULT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6:32149801_C/T</td>\n",
       "      <td>32149801</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RNF5,AGER</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>novel</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Hematocrit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>hematocrit</td>\n",
       "      <td>OUR_RESULT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SNPS_B_836_31324100_exon3</td>\n",
       "      <td>31324100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HLA-B</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>-</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Hematocrit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>hematocrit</td>\n",
       "      <td>OUR_RESULT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6:29698050_A/G</td>\n",
       "      <td>29698050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HLA-F</td>\n",
       "      <td>on_gene</td>\n",
       "      <td>novel</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "      <td>Hematocrit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   phenotype_name    PUBMEDID DISEASE/TRAIT MAPPED_TRAIT  \\\n",
       "74     hematocrit  OUR_RESULT           NaN          NaN   \n",
       "75     hematocrit  OUR_RESULT           NaN          NaN   \n",
       "76     hematocrit  OUR_RESULT           NaN          NaN   \n",
       "\n",
       "                         SNPS   CHR_POS REPORTED GENE(S)  \\\n",
       "74             6:32149801_C/T  32149801              NaN   \n",
       "75  SNPS_B_836_31324100_exon3  31324100              NaN   \n",
       "76             6:29698050_A/G  29698050              NaN   \n",
       "\n",
       "   GENE_CUSTOM_REPORTED_GENE_CUSTOM  genehow novelty phenotype_category  \\\n",
       "74                        RNF5,AGER  on_gene   novel  Hematological QTL   \n",
       "75                            HLA-B  on_gene       -  Hematological QTL   \n",
       "76                            HLA-F  on_gene   novel  Hematological QTL   \n",
       "\n",
       "   phenotype_type phenotype_name_publication  \n",
       "74     continuous                 Hematocrit  \n",
       "75     continuous                 Hematocrit  \n",
       "76     continuous                 Hematocrit  "
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "novelty_check_df[(novelty_check_df['PUBMEDID']=='OUR_RESULT')&(novelty_check_df['phenotype_name']=='hematocrit')]#['GENE_CUSTOM_REPORTED_GENE_CUSTOM'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "blood_in_urine HLA-A\n",
      "thyroid_disease HLA-DRB1\n",
      "grip_strength MICA\n",
      "height SCAND3\n",
      "height IER3\n",
      "hip_circumference C6orf100\n",
      "hip_circumference HLA-DRB1\n",
      "weight TRIM27\n",
      "weight MUC22\n",
      "weight HLA-DQA1\n",
      "diastolic_blood_pressure NOTCH4\n",
      "systolic_blood_pressure NOTCH4\n",
      "hematocrit HLA-F\n",
      "hematocrit RNF5\n",
      "hematocrit AGER\n",
      "platelet HLA-DRB1\n",
      "platelet HMGA1\n",
      "rbc HLA-A\n",
      "rbc ATF6B\n",
      "bun HLA-E\n",
      "creatinine HLA-C\n",
      "creatinine NOTCH4\n",
      "alt MICA\n",
      "alt HMGA1\n",
      "ast HLA-DRB1\n",
      "ast HLA-DPB1\n",
      "hdl_cholesterol MUC22\n",
      "hdl_cholesterol UHRF1BP1\n",
      "triglyceride HMGA1\n",
      "menopause_age HLA-DRB1\n",
      "pulse UBD\n",
      "albumin HMGA1\n",
      "hscrp MICB\n"
     ]
    }
   ],
   "source": [
    "phewas_data_2d_noveltycheck=phewas_data_2d.copy()\n",
    "\n",
    "for row, row_data in phewas_data_2d_noveltycheck.iterrows():\n",
    "    for col,element in row_data.items():\n",
    "        #print(row,co)\n",
    "        if ~np.isnan(element):\n",
    "            if ((row=='hematocrit') and (col=='RNF5' or col=='AGER')) or (col in novelty_check_df[(novelty_check_df['PUBMEDID']=='OUR_RESULT')&(novelty_check_df['phenotype_name']==row)&(novelty_check_df['novelty']=='novel')]['GENE_CUSTOM_REPORTED_GENE_CUSTOM'].values):\n",
    "                phewas_data_2d_noveltycheck.loc[row,col]=1\n",
    "                print(row,col)\n",
    "            else:\n",
    "                phewas_data_2d_noveltycheck.loc[row,col]=np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 620,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 4)\n"
     ]
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap,LinearSegmentedColormap\n",
    "from matplotlib import cm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#custom_cmap=cm.get_cmap('rainbow')\n",
    "#custom_cmap_array=custom_cmap(np.linspace(0.3, 1, 100))\n",
    "\n",
    "rgba_array=[(190,235,256,256),\n",
    "            (110,150,220,256),\n",
    "            (26,42,180,256),\n",
    "            (30,128,30,256),\n",
    "            (249,206,20,256),\n",
    "            (231,51,132,256)]\n",
    "cutidx_array=np.array([160,330,500,667,830,1000])\n",
    "\n",
    "custom_cmap_array=[]\n",
    "\n",
    "for idx in range(1000):\n",
    "    custom_cmap_array.append(\n",
    "        rgba_array[np.argmax((cutidx_array-idx)>0)]\n",
    "    )\n",
    "    \n",
    "custom_cmap_array=np.array(custom_cmap_array)/256\n",
    "print(custom_cmap_array.shape)\n",
    "\"\"\"        \n",
    "for idx,_ in enumerate(idx_thres[:-1:]):\n",
    "    \n",
    "    custom_cmap_array[idx_thres[idx]:idx_thres[idx+1],:]=custom_cmap_array[idx_thres[idx+1]-1]\n",
    "    if idx==0:\n",
    "        custom_cmap_array[idx_thres[idx]:idx_thres[idx+1],:-1]=2*custom_cmap_array[idx_thres[idx]:idx_thres[idx+1],:-1]\n",
    "\"\"\"\n",
    "#custom_cmap_array[:30,:3]=custom_cmap_array[:30,:3]*np.linspace(3, 1, 30).reshape((-1,1))\n",
    "#custom_cmap_array=np.array([[min(rgba[0]+0.1,1),min(rgba[1]+0.1,1),min(rgba[2]+0.1,1),rgba[3]] for rgba in custom_cmap_array])\n",
    "#custom_cmap_array=np.concatenate([np.array([[1,1,1,1]]*100),custom_cmap_array],axis=0)\n",
    "#custom_cmap_array=np.array([[min(rgba[0]+0.1,1),min(rgba[1]+0.1,1),min(rgba[2]+0.1,1),rgba[3]] for rgba in custom_cmap_array])\n",
    "#custom_cmap_array[0][-1]=0\n",
    "#custom_cmap_array[0]=[0.8,0.8,1,1]\n",
    "#custom_cmap_array[0]=custom_cmap_array[0]*2\n",
    "#custom_cmap_array=\n",
    "#custom_cmap_array=[custom_cmap_array[0],custom_cmap_array[20],custom_cmap_array[40],custom_cmap_array[60]]\n",
    "\n",
    "#custom_cmap_array=custom_cmap_array[30:]\n",
    "custom_cmap = ListedColormap(custom_cmap_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 621,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ch6845/tools/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: DeprecationWarning: object of type <class 'numpy.float64'> cannot be safely interpreted as an integer.\n",
      "  if sys.path[0] == '':\n"
     ]
    }
   ],
   "source": [
    "phenotype_category_color=pd.Series(index=phenotype_info.loc[phewas_data_2d.index]['phenotype_category'].unique())\n",
    "cnt=1\n",
    "for phenotype_category,row in phenotype_category_color.items():\n",
    "    if 'Other' in phenotype_category:\n",
    "        phenotype_category_color[phenotype_category]=0\n",
    "        \n",
    "    elif (phenotype_info.loc[phewas_data_2d.index]['phenotype_category']==phenotype_category).sum()==1:\n",
    "        phenotype_category_color[phenotype_category]=0\n",
    "    else:\n",
    "        phenotype_category_color[phenotype_category]=cnt;cnt+=1\n",
    "\n",
    "cmap_temp=plt.cm.brg(np.linspace(0,1,phenotype_category_color.max()))\n",
    "cmap_temp=[[color[0]*0.6,color[1]*0.6,color[2]*0.6,color[3]] for color in cmap_temp]\n",
    "np.random.shuffle(cmap_temp)\n",
    "cmap_temp=np.concatenate(([[0,0,0,1]],cmap_temp))   \n",
    "\n",
    "phenotype_category_color=phenotype_category_color.map(lambda x: cmap_temp[int(x)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 721,
   "metadata": {},
   "outputs": [],
   "source": [
    "def category_to_color(category):\n",
    "    color=(0,0,0,256)\n",
    "    if 'Hemato' in category:##\n",
    "        color=(180,5,5,256)\n",
    "    elif 'Anthropomet' in category:##\n",
    "        color=(5,5,180,256)\n",
    "    elif 'Protein' in category:##\n",
    "        color=(5,180,5,256)        \n",
    "        \n",
    "    elif 'Metabolic' in category:##\n",
    "        color=(160,120,20,256)\n",
    "    elif 'Liver' in category:\n",
    "        color=(120,20,160,256)\n",
    "    elif 'Kidney' in category:##\n",
    "        color=(20,120,160,256)\n",
    "    elif 'Blood-pressure' in category:\n",
    "        color=(256,50,5,256)\n",
    "    return np.array(color)/256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 636,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1.])"
      ]
     },
     "execution_count": 636,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "26,42,180"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 631,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cardiovascular disease                                   [0.0, 0.0, 0.0, 1.0]\n",
       "Immune-related diseases                                  [0.0, 0.0, 0.0, 1.0]\n",
       "Kidney-related disease                                   [0.0, 0.0, 0.0, 1.0]\n",
       "Metabolic diseases          [0.16941176470588235, 0.43058823529411766, 0.0...\n",
       "Thyroid-related diseases                                 [0.0, 0.0, 0.0, 1.0]\n",
       "Anthropometric QTL          [0.34352941176470586, 0.0, 0.2564705882352941,...\n",
       "Blood-pressure QTL                                       [0.0, 0.6, 0.0, 1.0]\n",
       "Hematological QTL           [0.5129411764705882, 0.08705882352941177, 0.0,...\n",
       "Kidney-related QTL                                       [0.0, 0.0, 0.6, 1.0]\n",
       "Liver-related QTL           [0.16941176470588235, 0.0, 0.43058823529411766...\n",
       "Metabolic QTL               [0.34352941176470586, 0.2564705882352941, 0.0,...\n",
       "Other QTL                                                [0.0, 0.0, 0.0, 1.0]\n",
       "Protein QTL                 [0.5129411764705882, 0.0, 0.08705882352941179,...\n",
       "dtype: object"
      ]
     },
     "execution_count": 631,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phenotype_category_color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 678,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gene_to_color(gene_name):\n",
    "\n",
    "    if gene_name in ['HLA-A','HLA-B','HLA-C']:\n",
    "        #color='#fa3c14'\n",
    "        #250, 58, 20\n",
    "        color=1/256*np.array((250,58,20,256))\n",
    "    elif gene_name in ['HLA-DPA1', 'HLA-DPB1','HLA-DQA1', 'HLA-DQB1','HLA-DRB1','HLA-DRA']:\n",
    "        #color='#2850c8'\n",
    "        color=1/256*np.array((40, 80, 200,256))\n",
    "    elif gene_name in ['HLA-F', 'HLA-G', 'HLA-E', 'HLA-DRB5', 'HLA-DQA2', 'HLA-DQB2', 'HLA-DOB', 'HLA-DMB', 'HLA-DMA', 'HLA-DOA', 'MICA','MICB','TAP2','TAP1']:\n",
    "        #color='#28c828'\n",
    "        color=1/256*np.array((40, 200, 40,256))\n",
    "    else:\n",
    "        #color='#fafac8'\n",
    "        color=1/256*np.array((250, 250, 200,256))\n",
    "        color[:3]=color[:3]*0.1\n",
    "        color=1/256*np.array((0, 0, 0,256))\n",
    "    return color\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 723,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x1872 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10,15)\n",
    "plt.rcParams[\"font.size\"] = 25\n",
    "plt.rcParams['font.family']='Arial'\n",
    "\n",
    "import matplotlib.gridspec as gridspec\n",
    "import seaborn as sns\n",
    "\n",
    "#fig = plt.figure(figsize=(10, 15+3)) \n",
    "fig = plt.figure(figsize=(13+1+3, 20+2+4)) \n",
    "gs=fig.add_gridspec(20+3+4,13+1+3)\n",
    "\n",
    "\n",
    "\n",
    "#fig_barplot=fig.add_subplot(gs[20:24,0:13])\n",
    "\n",
    "#gs = gridspec.GridSpec(3, 2, height_ratios=[5, 0.05, 1], width_ratios=[10,1]) \n",
    "\n",
    "\n",
    "fig_heatmap=fig.add_subplot(gs[0:20,0:13])\n",
    "fig_heatmap_cmap=fig.add_subplot(gs[0+7:20-7, 13+1:13+1+1])\n",
    "\n",
    "\n",
    "sns.heatmap(phewas_data_2d,\n",
    "            ax=fig_heatmap,\n",
    "            cmap=custom_cmap,\n",
    "            vmin=0,\n",
    "            vmax=60,\n",
    "            linewidths=1,\n",
    "            linecolor=np.array([220,220,220,256])/256,\n",
    "            cbar_ax=fig_heatmap_cmap,\n",
    "            cbar_kws={'fraction':0.1, \"ticks\":[10, 20, 30, 40, 50]},\n",
    "            cbar=True,\n",
    "            alpha=1,edgecolor='black')#,legend=None)\n",
    "#fig.delaxes(fig_heatmap)\n",
    "\n",
    "fig_heatmap.set_ylabel('')\n",
    "fig_heatmap.set_xlabel('')\n",
    "\n",
    "for ticklabel in fig_heatmap.yaxis.get_ticklabels():\n",
    "    phenotype_name=ticklabel.get_text()\n",
    "    phenotype_category=phenotype_info.loc[phenotype_name]['phenotype_category']\n",
    "    ticklabel.set_color(category_to_color(phenotype_category))\n",
    "fig_heatmap.set_yticklabels([phenotype_info.loc[item.get_text()]['phenotype_name_publication'] for item in fig_heatmap.get_yticklabels()])\n",
    "\n",
    "for ticklabel in fig_heatmap.xaxis.get_ticklabels():\n",
    "    gene_name=ticklabel.get_text()\n",
    "    ticklabel.set_color(gene_to_color(gene_name))\n",
    "\n",
    "\n",
    "custom_cmap_white = ListedColormap([[1,1,1,0],\n",
    "                                    [1,1,1,0],\n",
    "                                    [1,1,1,0],\n",
    "                                   ])    \n",
    "#ax=sns.heatmap(phewas_data_2d_noveltycheck, ax=ax, cmap=custom_cmap_white, vmin=0, vmax=1, linecolor=None, cbar=False, alpha=0.7)#,legend=None)    \n",
    "fig_heatmap.pcolor(phewas_data_2d_noveltycheck.values, cmap=custom_cmap_white, facecolor=None, edgecolors='black', linewidths=6)#, ax=ax)    \n",
    "  \n",
    "    \n",
    "    \n",
    "fig_barplot=fig.add_subplot(gs[20+3:20+3+4,0:13])    \n",
    "sns.barplot(x='index',y='gene_name',ax=fig_barplot,data=phewas_data_2d_columns_catalog_all_count,\n",
    "            edgecolor='black',\n",
    "            palette=list(phewas_data_2d_columns_catalog_all_count['color'].values),alpha=0.3)    \n",
    "fig_barplot.xaxis.set_visible(False)\n",
    "fig_barplot.spines['right'].set_visible(False)\n",
    "fig_barplot.spines['bottom'].set_visible(False)\n",
    "fig_barplot.set_ylim(fig_barplot.get_ylim()[::-1])\n",
    "fig_barplot.set_ylabel('GWAS Catalog')\n",
    "\n",
    "fig_barplot2 = fig_barplot.twinx() \n",
    "sns.barplot(x='gene',y=0,data=phewas_data_2d_count,\n",
    "            edgecolor='black',\n",
    "            palette=list(phewas_data_2d_count['color'].values),alpha=0.7,ax=fig_barplot2)\n",
    "fig_barplot2.set_ylim(fig_barplot2.get_ylim()[::-1])\n",
    "fig_barplot2.set_ylabel('Our Result (thin)')\n",
    "\n",
    "\n",
    "for bar in fig_barplot2.patches:\n",
    "    newwidth=0.3\n",
    "    \n",
    "    x = bar.get_x()\n",
    "    width = bar.get_width()\n",
    "    centre = x + width/2.\n",
    "    \n",
    "    bar.set_x(centre - newwidth/2.)\n",
    "    bar.set_width(newwidth)\n",
    "\n",
    "\n",
    "plt.savefig('figure_phewas.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10,15)\n",
    "plt.rcParams[\"font.size\"] = 15\n",
    "plt.rcParams['font.family']='Arial'\n",
    "\n",
    "import seaborn as sns\n",
    "#ax=sns.heatmap(phewas_data_2d_noveltycheck,cmap=custom_cmap,vmin=0,linewidths=1, linecolor='grey',cbar_kws={\"shrink\": 0.3,'aspect':15},alpha=0.8)#,legend=None)\n",
    "\n",
    "#ax=sns.heatmap(phewas_data_2d,cmap=custom_cmap,vmin=-np.log10(5e-8),linewidths=1, linecolor='grey',cbar_kws={\"shrink\": 0.3,'aspect':15},alpha=0.8)#,legend=None)\n",
    "ax=sns.heatmap(phewas_data_2d,cmap=custom_cmap,vmin=-np.log10(5e-8),linewidths=1, linecolor='grey',cbar=False, alpha=0.8)#,legend=None)\n",
    "#ax.legend().set_visible(False)\n",
    "#ax.legend.\n",
    "#ax.xaxis.set_ticks_position('top')\n",
    "#plt.xticks(rotation=90)\n",
    "\n",
    "plt.ylabel('')\n",
    "plt.xlabel('')\n",
    "\n",
    "for ticklabel in ax.yaxis.get_ticklabels():\n",
    "    phenotype_name=ticklabel.get_text()\n",
    "    phenotype_category=phenotype_info.loc[phenotype_name]['phenotype_category']\n",
    "    ticklabel.set_color(phenotype_category_color.loc[phenotype_category])\n",
    "ax.set_yticklabels([phenotype_info.loc[item.get_text()]['phenotype_name_publication'] for item in ax.get_yticklabels()])\n",
    "\n",
    "for ticklabel in ax.xaxis.get_ticklabels():\n",
    "    gene_name=ticklabel.get_text()\n",
    "    ticklabel.set_color(gene_to_color(gene_name))\n",
    "    \"\"\"\n",
    "    if gene_name in ['HLA-A','HLA-B','HLA-C']:\n",
    "        ticklabel.set_color('#fa3c14')\n",
    "    elif gene_name in ['HLA-DPA1', 'HLA-DPB1','HLA-DQA1', 'HLA-DQB1','HLA-DRB1','HLA-DRA']:\n",
    "        ticklabel.set_color(color='#2850c8')\n",
    "    elif gene_name in ['HLA-F', 'HLA-G', 'HLA-E', 'HLA-DRB5', 'HLA-DQA2', 'HLA-DQB2', 'HLA-DOB', 'HLA-DMB', 'HLA-DMA', 'HLA-DOA', 'MICA','MICB','TAP2','TAP1']:\n",
    "        ticklabel.set_color('#28c828')\n",
    "    \"\"\"\n",
    "#plt.show()\n",
    "\n",
    "plt.savefig('figure_phewas.svg', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 989,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>phenotype_name_publication</th>\n",
       "      <th>phenotype_category</th>\n",
       "      <th>phenotype_type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phenotype_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bladder_cancer</th>\n",
       "      <td>Bladder cancer</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breast_cancer</th>\n",
       "      <td>Breast cancer</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cervical_cancer</th>\n",
       "      <td>Cervical cancer</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colorectal_cancer</th>\n",
       "      <td>Colorectal cancer</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>liver_cancer</th>\n",
       "      <td>Liver cancer</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lung_cancer</th>\n",
       "      <td>Lung cancer</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prostate_cancer</th>\n",
       "      <td>Prostate cancer</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stomach_cancer</th>\n",
       "      <td>Stomach cancer</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thyroid_cancer</th>\n",
       "      <td>Thryroid cancer</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>angina_pectoris_myocardial_infarction</th>\n",
       "      <td>Angina pectoris/Myocardial infarction</td>\n",
       "      <td>Cardiovascular disease</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heart_failure</th>\n",
       "      <td>Heart failure</td>\n",
       "      <td>Cardiovascular disease</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypertension</th>\n",
       "      <td>Hypertension</td>\n",
       "      <td>Cardiovascular disease</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peripheral_vascular_diseases</th>\n",
       "      <td>Peripheral vascular diseases</td>\n",
       "      <td>Cardiovascular disease</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <td>Stroke</td>\n",
       "      <td>Cardiovascular disease</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transient_ischemic_attacks</th>\n",
       "      <td>Transient ischemic attack</td>\n",
       "      <td>Cardiovascular disease</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cataract</th>\n",
       "      <td>Cataract</td>\n",
       "      <td>Eye-related disease</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glaucoma</th>\n",
       "      <td>Glaucoma</td>\n",
       "      <td>Eye-related diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>allergic_disease</th>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asthma</th>\n",
       "      <td>Asthma</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rheumatoid_arthritis</th>\n",
       "      <td>Rheumatoid arthritis</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blood_in_urine</th>\n",
       "      <td>Blood in urine</td>\n",
       "      <td>Kidney-related disease</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>renal_failure</th>\n",
       "      <td>Renal failure</td>\n",
       "      <td>Kidney-related diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acute_liver_disease</th>\n",
       "      <td>Acute liver disease</td>\n",
       "      <td>Liver-related diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fatty_liver</th>\n",
       "      <td>Fatty liver</td>\n",
       "      <td>Liver-related diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetes</th>\n",
       "      <td>Diabetes</td>\n",
       "      <td>Metabolic diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hyperlipidemia</th>\n",
       "      <td>Hyperlipidemia</td>\n",
       "      <td>Metabolic diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>insomnia</th>\n",
       "      <td>Insomnia</td>\n",
       "      <td>Nervous system diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neurosis</th>\n",
       "      <td>Neurosis</td>\n",
       "      <td>Nervous system diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parkinson</th>\n",
       "      <td>Parkinson</td>\n",
       "      <td>Nervous system diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arthritis</th>\n",
       "      <td>Arthritis</td>\n",
       "      <td>Orthopedic disorders</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcv</th>\n",
       "      <td>Mean corpuscular volume</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>platelet</th>\n",
       "      <td>Platelet</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rbc</th>\n",
       "      <td>Red blood cell count</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wbc</th>\n",
       "      <td>White blood cell count</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>creatinine</th>\n",
       "      <td>Creatinine</td>\n",
       "      <td>Kidney-related QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bun</th>\n",
       "      <td>Blood urea nitrogen</td>\n",
       "      <td>Kidney-related QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alt</th>\n",
       "      <td>Alanine aminotransferase</td>\n",
       "      <td>Liver-related QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ast</th>\n",
       "      <td>Aspartate transaminase</td>\n",
       "      <td>Liver-related QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gtp</th>\n",
       "      <td>γ-glutamyl transpeptidase</td>\n",
       "      <td>Liver-related QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hdl_cholesterol</th>\n",
       "      <td>HDL cholesterol</td>\n",
       "      <td>Metabolic QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_cholesterol</th>\n",
       "      <td>Total cholesterol</td>\n",
       "      <td>Metabolic QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>triglyceride</th>\n",
       "      <td>Triglyceride</td>\n",
       "      <td>Metabolic QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glucose_in_blood</th>\n",
       "      <td>Glucose in blood</td>\n",
       "      <td>Metabolic QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hba1c</th>\n",
       "      <td>Hemoglobin A1c</td>\n",
       "      <td>Metabolic QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>child_dead</th>\n",
       "      <td>Child dead</td>\n",
       "      <td>Other QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>child_delivery_number</th>\n",
       "      <td>Child delivery count</td>\n",
       "      <td>Other QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>child_present</th>\n",
       "      <td>Child present</td>\n",
       "      <td>Other QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>menarche_onset_age</th>\n",
       "      <td>Menarche onset age</td>\n",
       "      <td>Other QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>menopause_age</th>\n",
       "      <td>Menopause age</td>\n",
       "      <td>Other QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>menopause_cycle</th>\n",
       "      <td>Menopause cycle</td>\n",
       "      <td>Other QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pulse</th>\n",
       "      <td>Pulse rate</td>\n",
       "      <td>Other QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pwi_total</th>\n",
       "      <td>Personal Well-being Index</td>\n",
       "      <td>Other QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sleep_duration</th>\n",
       "      <td>Sleep duration</td>\n",
       "      <td>Other QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>albumin</th>\n",
       "      <td>Albumin</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hscrp</th>\n",
       "      <td>High-sensitivity C-reactive protein</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>protein_in_blood</th>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bilirubin</th>\n",
       "      <td>Total bilirubin</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>renin</th>\n",
       "      <td>Renin</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freet4</th>\n",
       "      <td>Free T4</td>\n",
       "      <td>Thyroid-related QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsh</th>\n",
       "      <td>Thyroid stimulating hormone</td>\n",
       "      <td>Thyroid-related QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  phenotype_name_publication  \\\n",
       "phenotype_name                                                                 \n",
       "bladder_cancer                                                Bladder cancer   \n",
       "breast_cancer                                                  Breast cancer   \n",
       "cervical_cancer                                              Cervical cancer   \n",
       "colorectal_cancer                                          Colorectal cancer   \n",
       "liver_cancer                                                    Liver cancer   \n",
       "lung_cancer                                                      Lung cancer   \n",
       "prostate_cancer                                              Prostate cancer   \n",
       "stomach_cancer                                                Stomach cancer   \n",
       "thyroid_cancer                                               Thryroid cancer   \n",
       "angina_pectoris_myocardial_infarction  Angina pectoris/Myocardial infarction   \n",
       "heart_failure                                                  Heart failure   \n",
       "hypertension                                                    Hypertension   \n",
       "peripheral_vascular_diseases                    Peripheral vascular diseases   \n",
       "stroke                                                                Stroke   \n",
       "transient_ischemic_attacks                         Transient ischemic attack   \n",
       "cataract                                                            Cataract   \n",
       "glaucoma                                                            Glaucoma   \n",
       "allergic_disease                                            Allergic disease   \n",
       "asthma                                                                Asthma   \n",
       "rheumatoid_arthritis                                    Rheumatoid arthritis   \n",
       "blood_in_urine                                                Blood in urine   \n",
       "renal_failure                                                  Renal failure   \n",
       "acute_liver_disease                                      Acute liver disease   \n",
       "fatty_liver                                                      Fatty liver   \n",
       "diabetes                                                            Diabetes   \n",
       "hyperlipidemia                                                Hyperlipidemia   \n",
       "insomnia                                                            Insomnia   \n",
       "neurosis                                                            Neurosis   \n",
       "parkinson                                                          Parkinson   \n",
       "arthritis                                                          Arthritis   \n",
       "...                                                                      ...   \n",
       "mcv                                                  Mean corpuscular volume   \n",
       "platelet                                                            Platelet   \n",
       "rbc                                                     Red blood cell count   \n",
       "wbc                                                   White blood cell count   \n",
       "creatinine                                                        Creatinine   \n",
       "bun                                                      Blood urea nitrogen   \n",
       "alt                                                 Alanine aminotransferase   \n",
       "ast                                                   Aspartate transaminase   \n",
       "gtp                                                γ-glutamyl transpeptidase   \n",
       "hdl_cholesterol                                              HDL cholesterol   \n",
       "total_cholesterol                                          Total cholesterol   \n",
       "triglyceride                                                    Triglyceride   \n",
       "glucose_in_blood                                            Glucose in blood   \n",
       "hba1c                                                         Hemoglobin A1c   \n",
       "child_dead                                                        Child dead   \n",
       "child_delivery_number                                   Child delivery count   \n",
       "child_present                                                  Child present   \n",
       "menarche_onset_age                                        Menarche onset age   \n",
       "menopause_age                                                  Menopause age   \n",
       "menopause_cycle                                              Menopause cycle   \n",
       "pulse                                                             Pulse rate   \n",
       "pwi_total                                          Personal Well-being Index   \n",
       "sleep_duration                                                Sleep duration   \n",
       "albumin                                                              Albumin   \n",
       "hscrp                                    High-sensitivity C-reactive protein   \n",
       "protein_in_blood                                            Protein in blood   \n",
       "total_bilirubin                                              Total bilirubin   \n",
       "renin                                                                  Renin   \n",
       "freet4                                                               Free T4   \n",
       "tsh                                              Thyroid stimulating hormone   \n",
       "\n",
       "                                            phenotype_category phenotype_type  \n",
       "phenotype_name                                                                 \n",
       "bladder_cancer                                          Cancer         binary  \n",
       "breast_cancer                                           Cancer         binary  \n",
       "cervical_cancer                                         Cancer         binary  \n",
       "colorectal_cancer                                       Cancer         binary  \n",
       "liver_cancer                                            Cancer         binary  \n",
       "lung_cancer                                             Cancer         binary  \n",
       "prostate_cancer                                         Cancer         binary  \n",
       "stomach_cancer                                          Cancer         binary  \n",
       "thyroid_cancer                                          Cancer         binary  \n",
       "angina_pectoris_myocardial_infarction   Cardiovascular disease         binary  \n",
       "heart_failure                           Cardiovascular disease         binary  \n",
       "hypertension                            Cardiovascular disease         binary  \n",
       "peripheral_vascular_diseases            Cardiovascular disease         binary  \n",
       "stroke                                  Cardiovascular disease         binary  \n",
       "transient_ischemic_attacks              Cardiovascular disease         binary  \n",
       "cataract                                   Eye-related disease         binary  \n",
       "glaucoma                                  Eye-related diseases         binary  \n",
       "allergic_disease                       Immune-related diseases         binary  \n",
       "asthma                                 Immune-related diseases         binary  \n",
       "rheumatoid_arthritis                   Immune-related diseases         binary  \n",
       "blood_in_urine                          Kidney-related disease         binary  \n",
       "renal_failure                          Kidney-related diseases         binary  \n",
       "acute_liver_disease                     Liver-related diseases         binary  \n",
       "fatty_liver                             Liver-related diseases         binary  \n",
       "diabetes                                    Metabolic diseases         binary  \n",
       "hyperlipidemia                              Metabolic diseases         binary  \n",
       "insomnia                               Nervous system diseases         binary  \n",
       "neurosis                               Nervous system diseases         binary  \n",
       "parkinson                              Nervous system diseases         binary  \n",
       "arthritis                                 Orthopedic disorders         binary  \n",
       "...                                                        ...            ...  \n",
       "mcv                                          Hematological QTL     continuous  \n",
       "platelet                                     Hematological QTL     continuous  \n",
       "rbc                                          Hematological QTL     continuous  \n",
       "wbc                                          Hematological QTL     continuous  \n",
       "creatinine                                  Kidney-related QTL     continuous  \n",
       "bun                                         Kidney-related QTL     continuous  \n",
       "alt                                          Liver-related QTL     continuous  \n",
       "ast                                          Liver-related QTL     continuous  \n",
       "gtp                                          Liver-related QTL     continuous  \n",
       "hdl_cholesterol                                  Metabolic QTL     continuous  \n",
       "total_cholesterol                                Metabolic QTL     continuous  \n",
       "triglyceride                                     Metabolic QTL     continuous  \n",
       "glucose_in_blood                                 Metabolic QTL     continuous  \n",
       "hba1c                                            Metabolic QTL     continuous  \n",
       "child_dead                                           Other QTL     continuous  \n",
       "child_delivery_number                                Other QTL     continuous  \n",
       "child_present                                        Other QTL     continuous  \n",
       "menarche_onset_age                                   Other QTL     continuous  \n",
       "menopause_age                                        Other QTL     continuous  \n",
       "menopause_cycle                                      Other QTL     continuous  \n",
       "pulse                                                Other QTL     continuous  \n",
       "pwi_total                                            Other QTL     continuous  \n",
       "sleep_duration                                       Other QTL     continuous  \n",
       "albumin                                            Protein QTL     continuous  \n",
       "hscrp                                              Protein QTL     continuous  \n",
       "protein_in_blood                                   Protein QTL     continuous  \n",
       "total_bilirubin                                    Protein QTL     continuous  \n",
       "renin                                              Protein QTL     continuous  \n",
       "freet4                                     Thyroid-related QTL     continuous  \n",
       "tsh                                        Thyroid-related QTL     continuous  \n",
       "\n",
       "[98 rows x 3 columns]"
      ]
     },
     "execution_count": 989,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10,15)\n",
    "plt.rcParams[\"font.size\"] = 15\n",
    "plt.rcParams['font.family']='Arial'\n",
    "\n",
    "import matplotlib.gridspec as gridspec\n",
    "import seaborn as sns\n",
    "\n",
    "fig = plt.figure(figsize=(10, 15)) \n",
    "gs = gridspec.GridSpec(2, 1, height_ratios=[5, 1]) \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax=plt.subplot(gs[0])\n",
    "\n",
    "ax=sns.heatmap(phewas_data_2d,cmap=custom_cmap,vmin=-np.log10(5e-8),linewidths=1, linecolor='grey',cbar_kws={\"shrink\": 0.3,'aspect':15},alpha=0.8)#,legend=None)\n",
    "#ax.legend().set_visible(False)\n",
    "#ax.legend.\n",
    "#ax.xaxis.set_ticks_position('top')\n",
    "#plt.xticks(rotation=90)\n",
    "\n",
    "plt.ylabel('')\n",
    "plt.xlabel('')\n",
    "\n",
    "for ticklabel in ax.yaxis.get_ticklabels():\n",
    "    phenotype_name=ticklabel.get_text()\n",
    "    phenotype_category=phenotype_info.loc[phenotype_name]['phenotype_category']\n",
    "    ticklabel.set_color(phenotype_category_color.loc[phenotype_category])\n",
    "ax.set_yticklabels([phenotype_info.loc[item.get_text()]['phenotype_name_publication'] for item in ax.get_yticklabels()])\n",
    "\n",
    "for ticklabel in ax.xaxis.get_ticklabels():\n",
    "    gene_name=ticklabel.get_text()\n",
    "    ticklabel.set_color(gene_to_color(gene_name))\n",
    "    \n",
    "  \n",
    "\n",
    "\n",
    "\n",
    "ax=plt.subplot(gs[1])\n",
    "    \n",
    "    \n",
    "#ax=\n",
    "sns.barplot(x='index',y='gene_name',ax=ax,data=phewas_data_2d_columns_catalog_all_count,palette=list(phewas_data_2d_columns_catalog_all_count['color'].values),alpha=0.5)\n",
    "#sns.barplot(x='index',y='gene_name',data=phewas_data_2d_columns_catalog_all_count,palette=list(phewas_data_2d_columns_catalog_all_count['color'].values),alpha=0.5)\n",
    "\n",
    "#ax.spines['right'].set_visible(False)\n",
    "#ax.spines['top'].set_visible(False)\n",
    "ax.xaxis.set_visible(False)\n",
    "plt.xticks(fontsize=5)\n",
    "plt.gca().invert_yaxis()\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "plt.ylabel('GWAS catalog')\n",
    "#plt.xlabel('ss')\n",
    "\n",
    "ax2 = ax.twinx() \n",
    "ax2=sns.barplot(x='gene',y=0,data=phewas_data_2d_count,palette=list(phewas_data_2d_count['color'].values),alpha=0.7)\n",
    "plt.gca().invert_yaxis()\n",
    "ax2.spines['bottom'].set_visible(False)\n",
    "plt.ylabel('our result')    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['hypertension', 'allergic_disease', 'blood_in_urine', 'diabetes',\n",
       "       'hyperlipidemia', 'thyroid_disease', 'FEV_predicted', 'FVC_predicted',\n",
       "       'bmi', 'grip_strength', 'height', 'hip_circumference', 'weight',\n",
       "       'diastolic_blood_pressure', 'systolic_blood_pressure', 'hematocrit',\n",
       "       'hemoglobin', 'mch', 'mcv', 'platelet', 'rbc', 'wbc', 'bun',\n",
       "       'creatinine', 'alt', 'ast', 'hba1c', 'hdl_cholesterol',\n",
       "       'total_cholesterol', 'triglyceride', 'menopause_age', 'pulse',\n",
       "       'albumin', 'hscrp', 'protein_in_blood'],\n",
       "      dtype='object', name='phenotype_name')"
      ]
     },
     "execution_count": 457,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>phenotype_name_publication</th>\n",
       "      <th>phenotype_category</th>\n",
       "      <th>phenotype_type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phenotype_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bladder_cancer</th>\n",
       "      <td>Bladder cancer</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breast_cancer</th>\n",
       "      <td>Breast cancer</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cervical_cancer</th>\n",
       "      <td>Cervical cancer</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colorectal_cancer</th>\n",
       "      <td>Colorectal cancer</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>liver_cancer</th>\n",
       "      <td>Liver cancer</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lung_cancer</th>\n",
       "      <td>Lung cancer</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prostate_cancer</th>\n",
       "      <td>Prostate cancer</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stomach_cancer</th>\n",
       "      <td>Stomach cancer</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thyroid_cancer</th>\n",
       "      <td>Thryroid cancer</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>angina_pectoris_myocardial_infarction</th>\n",
       "      <td>Angina pectoris/Myocardial infarction</td>\n",
       "      <td>Cardiovascular disease</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heart_failure</th>\n",
       "      <td>Heart failure</td>\n",
       "      <td>Cardiovascular disease</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypertension</th>\n",
       "      <td>Hypertension</td>\n",
       "      <td>Cardiovascular disease</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peripheral_vascular_diseases</th>\n",
       "      <td>Peripheral vascular diseases</td>\n",
       "      <td>Cardiovascular disease</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <td>Stroke</td>\n",
       "      <td>Cardiovascular disease</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transient_ischemic_attacks</th>\n",
       "      <td>Transient ischemic attack</td>\n",
       "      <td>Cardiovascular disease</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cataract</th>\n",
       "      <td>Cataract</td>\n",
       "      <td>Eye-related disease</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glaucoma</th>\n",
       "      <td>Glaucoma</td>\n",
       "      <td>Eye-related diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>allergic_disease</th>\n",
       "      <td>Allergic disease</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asthma</th>\n",
       "      <td>Asthma</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rheumatoid_arthritis</th>\n",
       "      <td>Rheumatoid arthritis</td>\n",
       "      <td>Immune-related diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blood_in_urine</th>\n",
       "      <td>Blood in urine</td>\n",
       "      <td>Kidney-related disease</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>renal_failure</th>\n",
       "      <td>Renal failure</td>\n",
       "      <td>Kidney-related diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acute_liver_disease</th>\n",
       "      <td>Acute liver disease</td>\n",
       "      <td>Liver-related diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fatty_liver</th>\n",
       "      <td>Fatty liver</td>\n",
       "      <td>Liver-related diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetes</th>\n",
       "      <td>Diabetes</td>\n",
       "      <td>Metabolic diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hyperlipidemia</th>\n",
       "      <td>Hyperlipidemia</td>\n",
       "      <td>Metabolic diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>insomnia</th>\n",
       "      <td>Insomnia</td>\n",
       "      <td>Nervous system diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neurosis</th>\n",
       "      <td>Neurosis</td>\n",
       "      <td>Nervous system diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parkinson</th>\n",
       "      <td>Parkinson</td>\n",
       "      <td>Nervous system diseases</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arthritis</th>\n",
       "      <td>Arthritis</td>\n",
       "      <td>Orthopedic disorders</td>\n",
       "      <td>binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcv</th>\n",
       "      <td>Mean corpuscular volume</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>platelet</th>\n",
       "      <td>Platelet</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rbc</th>\n",
       "      <td>Red blood cell count</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wbc</th>\n",
       "      <td>White blood cell count</td>\n",
       "      <td>Hematological QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>creatinine</th>\n",
       "      <td>Creatinine</td>\n",
       "      <td>Kidney-related QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bun</th>\n",
       "      <td>Blood urea nitrogen</td>\n",
       "      <td>Kidney-related QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alt</th>\n",
       "      <td>Alanine aminotransferase</td>\n",
       "      <td>Liver-related QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ast</th>\n",
       "      <td>Aspartate transaminase</td>\n",
       "      <td>Liver-related QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gtp</th>\n",
       "      <td>γ-glutamyl transpeptidase</td>\n",
       "      <td>Liver-related QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hdl_cholesterol</th>\n",
       "      <td>HDL cholesterol</td>\n",
       "      <td>Metabolic QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_cholesterol</th>\n",
       "      <td>Total cholesterol</td>\n",
       "      <td>Metabolic QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>triglyceride</th>\n",
       "      <td>Triglyceride</td>\n",
       "      <td>Metabolic QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glucose_in_blood</th>\n",
       "      <td>Glucose in blood</td>\n",
       "      <td>Metabolic QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hba1c</th>\n",
       "      <td>HbA1C</td>\n",
       "      <td>Metabolic QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>child_dead</th>\n",
       "      <td>Child dead</td>\n",
       "      <td>Other QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>child_delivery_number</th>\n",
       "      <td>Child delivery count</td>\n",
       "      <td>Other QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>child_present</th>\n",
       "      <td>Child present</td>\n",
       "      <td>Other QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>menarche_onset_age</th>\n",
       "      <td>Menarche onset age</td>\n",
       "      <td>Other QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>menopause_age</th>\n",
       "      <td>Menopause age</td>\n",
       "      <td>Other QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>menopause_cycle</th>\n",
       "      <td>Menopause cycle</td>\n",
       "      <td>Other QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pulse</th>\n",
       "      <td>Pulse rate</td>\n",
       "      <td>Other QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pwi_total</th>\n",
       "      <td>Personal Well-being Index</td>\n",
       "      <td>Other QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sleep_duration</th>\n",
       "      <td>Sleep duration</td>\n",
       "      <td>Other QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>albumin</th>\n",
       "      <td>Albumin</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hscrp</th>\n",
       "      <td>hsCRP</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>protein_in_blood</th>\n",
       "      <td>Protein in blood</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bilirubin</th>\n",
       "      <td>Total bilirubin</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>renin</th>\n",
       "      <td>Renin</td>\n",
       "      <td>Protein QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freet4</th>\n",
       "      <td>Free T4</td>\n",
       "      <td>Thyroid-related QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsh</th>\n",
       "      <td>Thyroid stimulating hormone</td>\n",
       "      <td>Thyroid-related QTL</td>\n",
       "      <td>continuous</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  phenotype_name_publication  \\\n",
       "phenotype_name                                                                 \n",
       "bladder_cancer                                                Bladder cancer   \n",
       "breast_cancer                                                  Breast cancer   \n",
       "cervical_cancer                                              Cervical cancer   \n",
       "colorectal_cancer                                          Colorectal cancer   \n",
       "liver_cancer                                                    Liver cancer   \n",
       "lung_cancer                                                      Lung cancer   \n",
       "prostate_cancer                                              Prostate cancer   \n",
       "stomach_cancer                                                Stomach cancer   \n",
       "thyroid_cancer                                               Thryroid cancer   \n",
       "angina_pectoris_myocardial_infarction  Angina pectoris/Myocardial infarction   \n",
       "heart_failure                                                  Heart failure   \n",
       "hypertension                                                    Hypertension   \n",
       "peripheral_vascular_diseases                    Peripheral vascular diseases   \n",
       "stroke                                                                Stroke   \n",
       "transient_ischemic_attacks                         Transient ischemic attack   \n",
       "cataract                                                            Cataract   \n",
       "glaucoma                                                            Glaucoma   \n",
       "allergic_disease                                            Allergic disease   \n",
       "asthma                                                                Asthma   \n",
       "rheumatoid_arthritis                                    Rheumatoid arthritis   \n",
       "blood_in_urine                                                Blood in urine   \n",
       "renal_failure                                                  Renal failure   \n",
       "acute_liver_disease                                      Acute liver disease   \n",
       "fatty_liver                                                      Fatty liver   \n",
       "diabetes                                                            Diabetes   \n",
       "hyperlipidemia                                                Hyperlipidemia   \n",
       "insomnia                                                            Insomnia   \n",
       "neurosis                                                            Neurosis   \n",
       "parkinson                                                          Parkinson   \n",
       "arthritis                                                          Arthritis   \n",
       "...                                                                      ...   \n",
       "mcv                                                  Mean corpuscular volume   \n",
       "platelet                                                            Platelet   \n",
       "rbc                                                     Red blood cell count   \n",
       "wbc                                                   White blood cell count   \n",
       "creatinine                                                        Creatinine   \n",
       "bun                                                      Blood urea nitrogen   \n",
       "alt                                                 Alanine aminotransferase   \n",
       "ast                                                   Aspartate transaminase   \n",
       "gtp                                                γ-glutamyl transpeptidase   \n",
       "hdl_cholesterol                                              HDL cholesterol   \n",
       "total_cholesterol                                          Total cholesterol   \n",
       "triglyceride                                                    Triglyceride   \n",
       "glucose_in_blood                                            Glucose in blood   \n",
       "hba1c                                                                  HbA1C   \n",
       "child_dead                                                        Child dead   \n",
       "child_delivery_number                                   Child delivery count   \n",
       "child_present                                                  Child present   \n",
       "menarche_onset_age                                        Menarche onset age   \n",
       "menopause_age                                                  Menopause age   \n",
       "menopause_cycle                                              Menopause cycle   \n",
       "pulse                                                             Pulse rate   \n",
       "pwi_total                                          Personal Well-being Index   \n",
       "sleep_duration                                                Sleep duration   \n",
       "albumin                                                              Albumin   \n",
       "hscrp                                                                  hsCRP   \n",
       "protein_in_blood                                            Protein in blood   \n",
       "total_bilirubin                                              Total bilirubin   \n",
       "renin                                                                  Renin   \n",
       "freet4                                                               Free T4   \n",
       "tsh                                              Thyroid stimulating hormone   \n",
       "\n",
       "                                            phenotype_category phenotype_type  \n",
       "phenotype_name                                                                 \n",
       "bladder_cancer                                          Cancer         binary  \n",
       "breast_cancer                                           Cancer         binary  \n",
       "cervical_cancer                                         Cancer         binary  \n",
       "colorectal_cancer                                       Cancer         binary  \n",
       "liver_cancer                                            Cancer         binary  \n",
       "lung_cancer                                             Cancer         binary  \n",
       "prostate_cancer                                         Cancer         binary  \n",
       "stomach_cancer                                          Cancer         binary  \n",
       "thyroid_cancer                                          Cancer         binary  \n",
       "angina_pectoris_myocardial_infarction   Cardiovascular disease         binary  \n",
       "heart_failure                           Cardiovascular disease         binary  \n",
       "hypertension                            Cardiovascular disease         binary  \n",
       "peripheral_vascular_diseases            Cardiovascular disease         binary  \n",
       "stroke                                  Cardiovascular disease         binary  \n",
       "transient_ischemic_attacks              Cardiovascular disease         binary  \n",
       "cataract                                   Eye-related disease         binary  \n",
       "glaucoma                                  Eye-related diseases         binary  \n",
       "allergic_disease                       Immune-related diseases         binary  \n",
       "asthma                                 Immune-related diseases         binary  \n",
       "rheumatoid_arthritis                   Immune-related diseases         binary  \n",
       "blood_in_urine                          Kidney-related disease         binary  \n",
       "renal_failure                          Kidney-related diseases         binary  \n",
       "acute_liver_disease                     Liver-related diseases         binary  \n",
       "fatty_liver                             Liver-related diseases         binary  \n",
       "diabetes                                    Metabolic diseases         binary  \n",
       "hyperlipidemia                              Metabolic diseases         binary  \n",
       "insomnia                               Nervous system diseases         binary  \n",
       "neurosis                               Nervous system diseases         binary  \n",
       "parkinson                              Nervous system diseases         binary  \n",
       "arthritis                                 Orthopedic disorders         binary  \n",
       "...                                                        ...            ...  \n",
       "mcv                                          Hematological QTL     continuous  \n",
       "platelet                                     Hematological QTL     continuous  \n",
       "rbc                                          Hematological QTL     continuous  \n",
       "wbc                                          Hematological QTL     continuous  \n",
       "creatinine                                  Kidney-related QTL     continuous  \n",
       "bun                                         Kidney-related QTL     continuous  \n",
       "alt                                          Liver-related QTL     continuous  \n",
       "ast                                          Liver-related QTL     continuous  \n",
       "gtp                                          Liver-related QTL     continuous  \n",
       "hdl_cholesterol                                  Metabolic QTL     continuous  \n",
       "total_cholesterol                                Metabolic QTL     continuous  \n",
       "triglyceride                                     Metabolic QTL     continuous  \n",
       "glucose_in_blood                                 Metabolic QTL     continuous  \n",
       "hba1c                                            Metabolic QTL     continuous  \n",
       "child_dead                                           Other QTL     continuous  \n",
       "child_delivery_number                                Other QTL     continuous  \n",
       "child_present                                        Other QTL     continuous  \n",
       "menarche_onset_age                                   Other QTL     continuous  \n",
       "menopause_age                                        Other QTL     continuous  \n",
       "menopause_cycle                                      Other QTL     continuous  \n",
       "pulse                                                Other QTL     continuous  \n",
       "pwi_total                                            Other QTL     continuous  \n",
       "sleep_duration                                       Other QTL     continuous  \n",
       "albumin                                            Protein QTL     continuous  \n",
       "hscrp                                              Protein QTL     continuous  \n",
       "protein_in_blood                                   Protein QTL     continuous  \n",
       "total_bilirubin                                    Protein QTL     continuous  \n",
       "renin                                              Protein QTL     continuous  \n",
       "freet4                                     Thyroid-related QTL     continuous  \n",
       "tsh                                        Thyroid-related QTL     continuous  \n",
       "\n",
       "[98 rows x 3 columns]"
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phenotype_info.sort_values(['phenotype_type','phenotype_category'])#.sort_values('phenotype_category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ch6845/tools/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['SCAND3', 'TRIM27', 'C6orf100', 'UBD', 'HLA-F', 'HLA-A', 'HLA-E',\n",
       "       'IER3', 'MUC22', 'HLA-C', 'HLA-B', 'MICA', 'MICB', 'ATF6B', 'NOTCH4',\n",
       "       'C6orf10', 'HLA-DRB5', 'HLA-DRB1', 'HLA-DQA1', 'HLA-DQB1', 'HLA-DPB1',\n",
       "       'BAK1', 'HMGA1', 'UHRF1BP1', 'TAF11', 'RNF5,AGER'],\n",
       "      dtype='object', name='gene')"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_bed.set_index('Gene name').loc[phewas_data_2d.columns].sort_values('Gene start (bp)').index#.index#.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ATF6B', 'BAK1', 'C6orf10', 'C6orf100', 'HLA-A', 'HLA-B', 'HLA-C',\n",
       "       'HLA-DPB1', 'HLA-DQA1', 'HLA-DQB1', 'HLA-DRB1', 'HLA-DRB5', 'HLA-E',\n",
       "       'HLA-F', 'HMGA1', 'IER3', 'MICA', 'MICB', 'MUC22', 'NOTCH4',\n",
       "       'RNF5,AGER', 'SCAND3', 'TAF11', 'TRIM27', 'UBD', 'UHRF1BP1'],\n",
       "      dtype='object', name='gene')"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [],
   "source": [
    "phewas_data['count']=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "None",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/tools/miniconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2656\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2657\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2658\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: None",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-410-506f0d32c093>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mphewas_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'count'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/tools/miniconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mpivot\u001b[0;34m(self, index, columns, values)\u001b[0m\n\u001b[1;32m   5626\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpivot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5627\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivot\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpivot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5628\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mpivot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5629\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5630\u001b[0m     _shared_docs['pivot_table'] = \"\"\"\n",
      "\u001b[0;32m~/tools/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/pivot.py\u001b[0m in \u001b[0;36mpivot\u001b[0;34m(data, index, columns, values)\u001b[0m\n\u001b[1;32m    371\u001b[0m         \u001b[0mcols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    372\u001b[0m         \u001b[0mappend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 373\u001b[0;31m         \u001b[0mindexed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mappend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    374\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/tools/miniconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mset_index\u001b[0;34m(self, keys, drop, append, inplace, verify_integrity)\u001b[0m\n\u001b[1;32m   4176\u001b[0m                 \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4177\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4178\u001b[0;31m                 \u001b[0mlevel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4179\u001b[0m                 \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4180\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/tools/miniconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2925\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2926\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2927\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2928\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2929\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/tools/miniconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2657\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2658\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2659\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2660\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2661\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: None"
     ]
    }
   ],
   "source": [
    "phewas_data.pivot('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "#catalog_chr6_short[catalog_chr6_short['DISEASE/TRAIT'].str.contains('hemoglobin')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Allele', 'CHR_POS', 'CNV', 'CONTEXT', 'Category', 'DISEASE/TRAIT',\n",
       "       'DOWNSTREAM_GENE_DISTANCE', 'DOWNSTREAM_GENE_ID', 'INITIAL SAMPLE SIZE',\n",
       "       'INTERGENIC', 'MAPPED_GENE', 'MAPPED_TRAIT', 'P-VALUE',\n",
       "       'P-VALUE (TEXT)', 'PUBMEDID', 'REPORTED GENE(S)', 'SNPS',\n",
       "       'SNP_GENE_IDS', 'SNP_ID_CURRENT', 'STUDY', 'UPSTREAM_GENE_DISTANCE',\n",
       "       'UPSTREAM_GENE_ID', 'GENE_CUSTOM', 'REPORTED_GENE_CUSTOM',\n",
       "       'GENE_CUSTOM_REPORTED_GENE_CUSTOM'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catalog_temp.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>phenotype_name</th>\n",
       "      <th>samples(case/control)</th>\n",
       "      <th>step</th>\n",
       "      <th>condition</th>\n",
       "      <th>marker_name</th>\n",
       "      <th>note</th>\n",
       "      <th>term</th>\n",
       "      <th>POS</th>\n",
       "      <th>...</th>\n",
       "      <th>Z</th>\n",
       "      <th>coef</th>\n",
       "      <th>std</th>\n",
       "      <th>chisq</th>\n",
       "      <th>df</th>\n",
       "      <th>P</th>\n",
       "      <th>P_significant</th>\n",
       "      <th>gene_genehow</th>\n",
       "      <th>gene</th>\n",
       "      <th>genehow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>33113</td>\n",
       "      <td>264088</td>\n",
       "      <td>hscrp</td>\n",
       "      <td>99904</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6:31538497_T/A</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>31538497</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.82507</td>\n",
       "      <td>-0.004091</td>\n",
       "      <td>0.000702</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.726200e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>('LTA', 'nearest')</td>\n",
       "      <td>LTA</td>\n",
       "      <td>nearest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>32121</td>\n",
       "      <td>288171</td>\n",
       "      <td>hscrp</td>\n",
       "      <td>99904</td>\n",
       "      <td>2</td>\n",
       "      <td>6:31538497_T/A</td>\n",
       "      <td>6:31442731_T/A</td>\n",
       "      <td>unphased bialleic</td>\n",
       "      <td>ADD</td>\n",
       "      <td>31442731</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.79197</td>\n",
       "      <td>-0.003449</td>\n",
       "      <td>0.000720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.653840e-06</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  Unnamed: 0.1 phenotype_name samples(case/control)  step  \\\n",
       "87       33113        264088          hscrp                 99904     1   \n",
       "88       32121        288171          hscrp                 99904     2   \n",
       "\n",
       "         condition     marker_name               note term       POS  ...  \\\n",
       "87             NaN  6:31538497_T/A  unphased bialleic  ADD  31538497  ...   \n",
       "88  6:31538497_T/A  6:31442731_T/A  unphased bialleic  ADD  31442731  ...   \n",
       "\n",
       "          Z      coef       std  chisq  df             P  P_significant  \\\n",
       "87 -5.82507 -0.004091  0.000702    NaN NaN  5.726200e-09              1   \n",
       "88 -4.79197 -0.003449  0.000720    NaN NaN  1.653840e-06              0   \n",
       "\n",
       "          gene_genehow  gene  genehow  \n",
       "87  ('LTA', 'nearest')   LTA  nearest  \n",
       "88                 NaN   NaN      NaN  \n",
       "\n",
       "[2 rows x 26 columns]"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_all[result_all['phenotype_name']=='hscrp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24082, 25625)"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "31371356-31347274,31347274-31321649"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "31538497_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gene stable ID                                                                  ENSG00000168394\n",
       "Gene stable ID version                                                        ENSG00000168394.9\n",
       "Transcript stable ID                                                            ENST00000354258\n",
       "Transcript stable ID version                                                  ENST00000354258.4\n",
       "Gene start (bp)                                                                        32812986\n",
       "Gene end (bp)                                                                          32821755\n",
       "Transcript start (bp)                                                                  32812986\n",
       "Transcript end (bp)                                                                    32821755\n",
       "Protein stable ID                                                               ENSP00000346206\n",
       "Protein stable ID version                                                     ENSP00000346206.4\n",
       "Gene description                              transporter 1, ATP-binding cassette, sub-famil...\n",
       "Chromosome/scaffold name                                                                      6\n",
       "Strand                                                                                       -1\n",
       "Karyotype band                                                                           p21.32\n",
       "Transcription start site (TSS)                                                         32821755\n",
       "Version (protein)                                                                             4\n",
       "Version (transcript)                                                                          4\n",
       "Version (gene)                                                                                9\n",
       "Source (transcript)                                                              ensembl_havana\n",
       "Source (gene)                                                                    ensembl_havana\n",
       "Transcript type                                                                  protein_coding\n",
       "Gene type                                                                        protein_coding\n",
       "Gene % GC content                                                                         50.46\n",
       "Transcript count                                                                              4\n",
       "Source of transcript name                                                  HGNC transcript name\n",
       "Transcript name                                                                        TAP1-001\n",
       "Source of gene name                                                                 HGNC Symbol\n",
       "Gene name                                                                                  TAP1\n",
       "GENCODE basic annotation                                                                      1\n",
       "Transcript length (including UTRs and CDS)                                                 2959\n",
       "Gene stable ID38                                                                ENSG00000168394\n",
       "Gene start (bp)38                                                                   3.28452e+07\n",
       "Gene end (bp)38                                                                      3.2854e+07\n",
       "Name: 4087116, dtype: object"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_bed[gene_bed['Gene name']=='TAP1'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene stable ID</th>\n",
       "      <th>Gene stable ID version</th>\n",
       "      <th>Transcript stable ID</th>\n",
       "      <th>Transcript stable ID version</th>\n",
       "      <th>Gene start (bp)</th>\n",
       "      <th>Gene end (bp)</th>\n",
       "      <th>Transcript start (bp)</th>\n",
       "      <th>Transcript end (bp)</th>\n",
       "      <th>Protein stable ID</th>\n",
       "      <th>Protein stable ID version</th>\n",
       "      <th>...</th>\n",
       "      <th>Transcript count</th>\n",
       "      <th>Source of transcript name</th>\n",
       "      <th>Transcript name</th>\n",
       "      <th>Source of gene name</th>\n",
       "      <th>Gene name</th>\n",
       "      <th>GENCODE basic annotation</th>\n",
       "      <th>Transcript length (including UTRs and CDS)</th>\n",
       "      <th>Gene stable ID38</th>\n",
       "      <th>Gene start (bp)38</th>\n",
       "      <th>Gene end (bp)38</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2123096</th>\n",
       "      <td>ENSG00000204520</td>\n",
       "      <td>ENSG00000204520.8</td>\n",
       "      <td>ENST00000449934</td>\n",
       "      <td>ENST00000449934.2</td>\n",
       "      <td>31371356</td>\n",
       "      <td>31383092</td>\n",
       "      <td>31371356</td>\n",
       "      <td>31383092</td>\n",
       "      <td>ENSP00000413079</td>\n",
       "      <td>ENSP00000413079.1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>MICA-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>MICA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1386</td>\n",
       "      <td>ENSG00000204520</td>\n",
       "      <td>31399784.0</td>\n",
       "      <td>31415315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2123486</th>\n",
       "      <td>ENSG00000204516</td>\n",
       "      <td>ENSG00000204516.5</td>\n",
       "      <td>ENST00000399150</td>\n",
       "      <td>ENST00000399150.3</td>\n",
       "      <td>31462658</td>\n",
       "      <td>31478901</td>\n",
       "      <td>31465863</td>\n",
       "      <td>31478901</td>\n",
       "      <td>ENSP00000382103</td>\n",
       "      <td>ENSP00000382103.3</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>MICB-002</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>MICB</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2346</td>\n",
       "      <td>ENSG00000204516</td>\n",
       "      <td>31494881.0</td>\n",
       "      <td>31511124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4062867</th>\n",
       "      <td>ENSG00000204290</td>\n",
       "      <td>ENSG00000204290.6</td>\n",
       "      <td>ENST00000454136</td>\n",
       "      <td>ENST00000454136.3</td>\n",
       "      <td>32361740</td>\n",
       "      <td>32374905</td>\n",
       "      <td>32361740</td>\n",
       "      <td>32374905</td>\n",
       "      <td>ENSP00000390613</td>\n",
       "      <td>ENSP00000390613.3</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>BTNL2-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>BTNL2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1460</td>\n",
       "      <td>ENSG00000204290</td>\n",
       "      <td>32393963.0</td>\n",
       "      <td>32407128.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Gene stable ID Gene stable ID version Transcript stable ID  \\\n",
       "2123096  ENSG00000204520      ENSG00000204520.8      ENST00000449934   \n",
       "2123486  ENSG00000204516      ENSG00000204516.5      ENST00000399150   \n",
       "4062867  ENSG00000204290      ENSG00000204290.6      ENST00000454136   \n",
       "\n",
       "        Transcript stable ID version  Gene start (bp)  Gene end (bp)  \\\n",
       "2123096            ENST00000449934.2         31371356       31383092   \n",
       "2123486            ENST00000399150.3         31462658       31478901   \n",
       "4062867            ENST00000454136.3         32361740       32374905   \n",
       "\n",
       "         Transcript start (bp)  Transcript end (bp) Protein stable ID  \\\n",
       "2123096               31371356             31383092   ENSP00000413079   \n",
       "2123486               31465863             31478901   ENSP00000382103   \n",
       "4062867               32361740             32374905   ENSP00000390613   \n",
       "\n",
       "        Protein stable ID version  ... Transcript count  \\\n",
       "2123096         ENSP00000413079.1  ...                2   \n",
       "2123486         ENSP00000382103.3  ...                5   \n",
       "4062867         ENSP00000390613.3  ...                9   \n",
       "\n",
       "         Source of transcript name  Transcript name Source of gene name  \\\n",
       "2123096       HGNC transcript name         MICA-001         HGNC Symbol   \n",
       "2123486       HGNC transcript name         MICB-002         HGNC Symbol   \n",
       "4062867       HGNC transcript name        BTNL2-001         HGNC Symbol   \n",
       "\n",
       "         Gene name  GENCODE basic annotation  \\\n",
       "2123096       MICA                       1.0   \n",
       "2123486       MICB                       1.0   \n",
       "4062867      BTNL2                       1.0   \n",
       "\n",
       "         Transcript length (including UTRs and CDS)  Gene stable ID38  \\\n",
       "2123096                                        1386   ENSG00000204520   \n",
       "2123486                                        2346   ENSG00000204516   \n",
       "4062867                                        1460   ENSG00000204290   \n",
       "\n",
       "        Gene start (bp)38 Gene end (bp)38  \n",
       "2123096        31399784.0      31415315.0  \n",
       "2123486        31494881.0      31511124.0  \n",
       "4062867        32393963.0      32407128.0  \n",
       "\n",
       "[3 rows x 33 columns]"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_bed[gene_bed['Gene description'].str.contains('MHC')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Gene stable ID</th>\n",
       "      <th>Gene stable ID version</th>\n",
       "      <th>Transcript stable ID</th>\n",
       "      <th>Transcript stable ID version</th>\n",
       "      <th>Gene start (bp)</th>\n",
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       "      <th>Transcript count</th>\n",
       "      <th>Source of transcript name</th>\n",
       "      <th>Transcript name</th>\n",
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       "      <th>Gene name</th>\n",
       "      <th>GENCODE basic annotation</th>\n",
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       "      <th>Gene start (bp)38</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2187189</th>\n",
       "      <td>ENSG00000226979</td>\n",
       "      <td>ENSG00000226979.4</td>\n",
       "      <td>ENST00000454783</td>\n",
       "      <td>ENST00000454783.1</td>\n",
       "      <td>31539831</td>\n",
       "      <td>31542101</td>\n",
       "      <td>31539831</td>\n",
       "      <td>31542101</td>\n",
       "      <td>ENSP00000403495</td>\n",
       "      <td>ENSP00000403495.1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>LTA-002</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>LTA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1507</td>\n",
       "      <td>ENSG00000226979</td>\n",
       "      <td>31572054.0</td>\n",
       "      <td>31574324.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2187777</th>\n",
       "      <td>ENSG00000232810</td>\n",
       "      <td>ENSG00000232810.3</td>\n",
       "      <td>ENST00000449264</td>\n",
       "      <td>ENST00000449264.2</td>\n",
       "      <td>31543344</td>\n",
       "      <td>31546113</td>\n",
       "      <td>31543344</td>\n",
       "      <td>31546113</td>\n",
       "      <td>ENSP00000398698</td>\n",
       "      <td>ENSP00000398698.2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>TNF-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>TNF</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1676</td>\n",
       "      <td>ENSG00000232810</td>\n",
       "      <td>31575565.0</td>\n",
       "      <td>31578336.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2187949</th>\n",
       "      <td>ENSG00000227507</td>\n",
       "      <td>ENSG00000227507.2</td>\n",
       "      <td>ENST00000429299</td>\n",
       "      <td>ENST00000429299.2</td>\n",
       "      <td>31548302</td>\n",
       "      <td>31550299</td>\n",
       "      <td>31548332</td>\n",
       "      <td>31550202</td>\n",
       "      <td>ENSP00000410481</td>\n",
       "      <td>ENSP00000410481.2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>LTB-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>LTB</td>\n",
       "      <td>1.0</td>\n",
       "      <td>897</td>\n",
       "      <td>ENSG00000227507</td>\n",
       "      <td>31580525.0</td>\n",
       "      <td>31582522.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2188273</th>\n",
       "      <td>ENSG00000204482</td>\n",
       "      <td>ENSG00000204482.6</td>\n",
       "      <td>ENST00000418507</td>\n",
       "      <td>ENST00000418507.2</td>\n",
       "      <td>31553901</td>\n",
       "      <td>31556686</td>\n",
       "      <td>31553901</td>\n",
       "      <td>31556686</td>\n",
       "      <td>ENSP00000405900</td>\n",
       "      <td>ENSP00000405900.2</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>LST1-011</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>LST1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>726</td>\n",
       "      <td>ENSG00000204482</td>\n",
       "      <td>31586124.0</td>\n",
       "      <td>31588909.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2205633</th>\n",
       "      <td>ENSG00000204475</td>\n",
       "      <td>ENSG00000204475.5</td>\n",
       "      <td>ENST00000340027</td>\n",
       "      <td>ENST00000340027.5</td>\n",
       "      <td>31556672</td>\n",
       "      <td>31560762</td>\n",
       "      <td>31556672</td>\n",
       "      <td>31560762</td>\n",
       "      <td>ENSP00000342156</td>\n",
       "      <td>ENSP00000342156.5</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>NCR3-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>NCR3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1042</td>\n",
       "      <td>ENSG00000204475</td>\n",
       "      <td>31588895.0</td>\n",
       "      <td>31593006.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2186467</th>\n",
       "      <td>ENSG00000204498</td>\n",
       "      <td>ENSG00000204498.6</td>\n",
       "      <td>ENST00000376146</td>\n",
       "      <td>ENST00000376146.4</td>\n",
       "      <td>31514647</td>\n",
       "      <td>31526606</td>\n",
       "      <td>31514647</td>\n",
       "      <td>31526606</td>\n",
       "      <td>ENSP00000365316</td>\n",
       "      <td>ENSP00000365316.4</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>NFKBIL1-003</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>NFKBIL1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1411</td>\n",
       "      <td>ENSG00000204498</td>\n",
       "      <td>31546870.0</td>\n",
       "      <td>31558829.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2185022</th>\n",
       "      <td>ENSG00000213760</td>\n",
       "      <td>ENSG00000213760.6</td>\n",
       "      <td>ENST00000376151</td>\n",
       "      <td>ENST00000376151.4</td>\n",
       "      <td>31512239</td>\n",
       "      <td>31516204</td>\n",
       "      <td>31512239</td>\n",
       "      <td>31514414</td>\n",
       "      <td>ENSP00000365321</td>\n",
       "      <td>ENSP00000365321.4</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>ATP6V1G2-002</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>ATP6V1G2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1261</td>\n",
       "      <td>ENSG00000213760</td>\n",
       "      <td>31544462.0</td>\n",
       "      <td>31548427.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127794</th>\n",
       "      <td>ENSG00000198563</td>\n",
       "      <td>ENSG00000198563.9</td>\n",
       "      <td>ENST00000376177</td>\n",
       "      <td>ENST00000376177.2</td>\n",
       "      <td>31497996</td>\n",
       "      <td>31510225</td>\n",
       "      <td>31497996</td>\n",
       "      <td>31508313</td>\n",
       "      <td>ENSP00000365347</td>\n",
       "      <td>ENSP00000365347.2</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>DDX39B-006</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>DDX39B</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1529</td>\n",
       "      <td>ENSG00000198563</td>\n",
       "      <td>31530219.0</td>\n",
       "      <td>31542448.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2123980</th>\n",
       "      <td>ENSG00000204511</td>\n",
       "      <td>ENSG00000204511.2</td>\n",
       "      <td>ENST00000376191</td>\n",
       "      <td>ENST00000376191.2</td>\n",
       "      <td>31496494</td>\n",
       "      <td>31498009</td>\n",
       "      <td>31496494</td>\n",
       "      <td>31498009</td>\n",
       "      <td>ENSP00000365362</td>\n",
       "      <td>ENSP00000365362.2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>MCCD1-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>MCCD1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>955</td>\n",
       "      <td>ENSG00000204511</td>\n",
       "      <td>31528962.0</td>\n",
       "      <td>31530232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2207335</th>\n",
       "      <td>ENSG00000204472</td>\n",
       "      <td>ENSG00000204472.8</td>\n",
       "      <td>ENST00000376059</td>\n",
       "      <td>ENST00000376059.3</td>\n",
       "      <td>31582961</td>\n",
       "      <td>31584798</td>\n",
       "      <td>31582961</td>\n",
       "      <td>31584798</td>\n",
       "      <td>ENSP00000365227</td>\n",
       "      <td>ENSP00000365227.3</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>AIF1-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>AIF1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>711</td>\n",
       "      <td>ENSG00000204472</td>\n",
       "      <td>31615217.0</td>\n",
       "      <td>31617021.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2209271</th>\n",
       "      <td>ENSG00000204469</td>\n",
       "      <td>ENSG00000204469.8</td>\n",
       "      <td>ENST00000376007</td>\n",
       "      <td>ENST00000376007.4</td>\n",
       "      <td>31588497</td>\n",
       "      <td>31605548</td>\n",
       "      <td>31588497</td>\n",
       "      <td>31605548</td>\n",
       "      <td>ENSP00000365175</td>\n",
       "      <td>ENSP00000365175.4</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>PRRC2A-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>PRRC2A</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6861</td>\n",
       "      <td>ENSG00000204469</td>\n",
       "      <td>31620715.0</td>\n",
       "      <td>31637771.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2245560</th>\n",
       "      <td>ENSG00000204463</td>\n",
       "      <td>ENSG00000204463.8</td>\n",
       "      <td>ENST00000422948</td>\n",
       "      <td>ENST00000422948.1</td>\n",
       "      <td>31606805</td>\n",
       "      <td>31620482</td>\n",
       "      <td>31606805</td>\n",
       "      <td>31608714</td>\n",
       "      <td>ENSP00000413225</td>\n",
       "      <td>ENSP00000413225.1</td>\n",
       "      <td>...</td>\n",
       "      <td>30</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>BAG6-030</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>BAG6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>513</td>\n",
       "      <td>ENSG00000204463</td>\n",
       "      <td>31639028.0</td>\n",
       "      <td>31652705.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2123486</th>\n",
       "      <td>ENSG00000204516</td>\n",
       "      <td>ENSG00000204516.5</td>\n",
       "      <td>ENST00000399150</td>\n",
       "      <td>ENST00000399150.3</td>\n",
       "      <td>31462658</td>\n",
       "      <td>31478901</td>\n",
       "      <td>31465863</td>\n",
       "      <td>31478901</td>\n",
       "      <td>ENSP00000382103</td>\n",
       "      <td>ENSP00000382103.3</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>MICB-002</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>MICB</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2346</td>\n",
       "      <td>ENSG00000204516</td>\n",
       "      <td>31494881.0</td>\n",
       "      <td>31511124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2541096</th>\n",
       "      <td>ENSG00000204444</td>\n",
       "      <td>ENSG00000204444.6</td>\n",
       "      <td>ENST00000375918</td>\n",
       "      <td>ENST00000375918.2</td>\n",
       "      <td>31620193</td>\n",
       "      <td>31625987</td>\n",
       "      <td>31620193</td>\n",
       "      <td>31625859</td>\n",
       "      <td>ENSP00000365083</td>\n",
       "      <td>ENSP00000365083.2</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>APOM-003</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>APOM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>922</td>\n",
       "      <td>ENSG00000204444</td>\n",
       "      <td>31652416.0</td>\n",
       "      <td>31658210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2541963</th>\n",
       "      <td>ENSG00000204439</td>\n",
       "      <td>ENSG00000204439.3</td>\n",
       "      <td>ENST00000375911</td>\n",
       "      <td>ENST00000375911.1</td>\n",
       "      <td>31626075</td>\n",
       "      <td>31628549</td>\n",
       "      <td>31626075</td>\n",
       "      <td>31628549</td>\n",
       "      <td>ENSP00000365076</td>\n",
       "      <td>ENSP00000365076.1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>C6orf47-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>C6orf47</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2475</td>\n",
       "      <td>ENSG00000204439</td>\n",
       "      <td>31658298.0</td>\n",
       "      <td>31660778.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2541969</th>\n",
       "      <td>ENSG00000204438</td>\n",
       "      <td>ENSG00000204438.6</td>\n",
       "      <td>ENST00000375906</td>\n",
       "      <td>ENST00000375906.1</td>\n",
       "      <td>31629006</td>\n",
       "      <td>31634060</td>\n",
       "      <td>31629006</td>\n",
       "      <td>31634060</td>\n",
       "      <td>ENSP00000365071</td>\n",
       "      <td>ENSP00000365071.1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>GPANK1-003</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>GPANK1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2793</td>\n",
       "      <td>ENSG00000204438</td>\n",
       "      <td>31661228.0</td>\n",
       "      <td>31666283.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2545890</th>\n",
       "      <td>ENSG00000204435</td>\n",
       "      <td>ENSG00000204435.9</td>\n",
       "      <td>ENST00000375885</td>\n",
       "      <td>ENST00000375885.4</td>\n",
       "      <td>31633013</td>\n",
       "      <td>31638120</td>\n",
       "      <td>31633013</td>\n",
       "      <td>31637847</td>\n",
       "      <td>ENSP00000365046</td>\n",
       "      <td>ENSP00000365046.4</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>CSNK2B-004</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>CSNK2B</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1061</td>\n",
       "      <td>ENSG00000204435</td>\n",
       "      <td>31665236.0</td>\n",
       "      <td>31670343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2549588</th>\n",
       "      <td>ENSG00000263020</td>\n",
       "      <td>ENSG00000263020.1</td>\n",
       "      <td>ENST00000375880</td>\n",
       "      <td>ENST00000375880.2</td>\n",
       "      <td>31633879</td>\n",
       "      <td>31641323</td>\n",
       "      <td>31633879</td>\n",
       "      <td>31641323</td>\n",
       "      <td>ENSP00000365040</td>\n",
       "      <td>ENSP00000365040.2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>UniProt transcript name</td>\n",
       "      <td>CSNK2B-LY6G5B-1181-001</td>\n",
       "      <td>UniProtKB Gene Name</td>\n",
       "      <td>CSNK2B-LY6G5B-1181</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2487</td>\n",
       "      <td>ENSG00000263020</td>\n",
       "      <td>31666102.0</td>\n",
       "      <td>31673546.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2549804</th>\n",
       "      <td>ENSG00000240053</td>\n",
       "      <td>ENSG00000240053.8</td>\n",
       "      <td>ENST00000375864</td>\n",
       "      <td>ENST00000375864.4</td>\n",
       "      <td>31637944</td>\n",
       "      <td>31641553</td>\n",
       "      <td>31637944</td>\n",
       "      <td>31641323</td>\n",
       "      <td>ENSP00000365024</td>\n",
       "      <td>ENSP00000365024.4</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>LY6G5B-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>LY6G5B</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2654</td>\n",
       "      <td>ENSG00000240053</td>\n",
       "      <td>31670167.0</td>\n",
       "      <td>31673776.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2549902</th>\n",
       "      <td>ENSG00000204428</td>\n",
       "      <td>ENSG00000204428.8</td>\n",
       "      <td>ENST00000375863</td>\n",
       "      <td>ENST00000375863.3</td>\n",
       "      <td>31644461</td>\n",
       "      <td>31651817</td>\n",
       "      <td>31644461</td>\n",
       "      <td>31651817</td>\n",
       "      <td>ENSP00000365023</td>\n",
       "      <td>ENSP00000365023.3</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>LY6G5C-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>LY6G5C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>950</td>\n",
       "      <td>ENSG00000204428</td>\n",
       "      <td>31676684.0</td>\n",
       "      <td>31684040.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2551666</th>\n",
       "      <td>ENSG00000204427</td>\n",
       "      <td>ENSG00000204427.7</td>\n",
       "      <td>ENST00000395952</td>\n",
       "      <td>ENST00000395952.3</td>\n",
       "      <td>31654726</td>\n",
       "      <td>31671221</td>\n",
       "      <td>31654726</td>\n",
       "      <td>31671221</td>\n",
       "      <td>ENSP00000379282</td>\n",
       "      <td>ENSP00000379282.3</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>ABHD16A-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>ABHD16A</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2103</td>\n",
       "      <td>ENSG00000204427</td>\n",
       "      <td>31686955.0</td>\n",
       "      <td>31703356.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2630866</th>\n",
       "      <td>ENSG00000204422</td>\n",
       "      <td>ENSG00000204422.7</td>\n",
       "      <td>ENST00000461287</td>\n",
       "      <td>ENST00000461287.1</td>\n",
       "      <td>31654739</td>\n",
       "      <td>31681849</td>\n",
       "      <td>31654739</td>\n",
       "      <td>31681849</td>\n",
       "      <td>ENSP00000475702</td>\n",
       "      <td>ENSP00000475702.1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>Clone-based (Vega) transcript</td>\n",
       "      <td>XXbac-BPG32J3.20-001</td>\n",
       "      <td>Clone-based (Vega) gene</td>\n",
       "      <td>XXbac-BPG32J3.20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2332</td>\n",
       "      <td>ENSG00000204422</td>\n",
       "      <td>31686962.0</td>\n",
       "      <td>31714072.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2631351</th>\n",
       "      <td>ENSG00000204424</td>\n",
       "      <td>ENSG00000204424.8</td>\n",
       "      <td>ENST00000375832</td>\n",
       "      <td>ENST00000375832.4</td>\n",
       "      <td>31674640</td>\n",
       "      <td>31685581</td>\n",
       "      <td>31674662</td>\n",
       "      <td>31678372</td>\n",
       "      <td>ENSP00000364992</td>\n",
       "      <td>ENSP00000364992.4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>LY6G6F-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>LY6G6F</td>\n",
       "      <td>1.0</td>\n",
       "      <td>920</td>\n",
       "      <td>ENSG00000204424</td>\n",
       "      <td>31706885.0</td>\n",
       "      <td>31710595.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2631639</th>\n",
       "      <td>ENSG00000250641</td>\n",
       "      <td>ENSG00000250641.1</td>\n",
       "      <td>ENST00000503322</td>\n",
       "      <td>ENST00000503322.1</td>\n",
       "      <td>31674681</td>\n",
       "      <td>31685695</td>\n",
       "      <td>31674681</td>\n",
       "      <td>31685695</td>\n",
       "      <td>ENSP00000421232</td>\n",
       "      <td>ENSP00000421232.1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>UniProt transcript name</td>\n",
       "      <td>MEGT1-001</td>\n",
       "      <td>UniProtKB Gene Name</td>\n",
       "      <td>MEGT1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1266</td>\n",
       "      <td>ENSG00000250641</td>\n",
       "      <td>31706904.0</td>\n",
       "      <td>31717918.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2631675</th>\n",
       "      <td>ENSG00000255552</td>\n",
       "      <td>ENSG00000255552.3</td>\n",
       "      <td>ENST00000409239</td>\n",
       "      <td>ENST00000409239.1</td>\n",
       "      <td>31679548</td>\n",
       "      <td>31681842</td>\n",
       "      <td>31679548</td>\n",
       "      <td>31681842</td>\n",
       "      <td>ENSP00000386842</td>\n",
       "      <td>ENSP00000386842.1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>LY6G6E-005</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>LY6G6E</td>\n",
       "      <td>1.0</td>\n",
       "      <td>776</td>\n",
       "      <td>ENSG00000255552</td>\n",
       "      <td>31711771.0</td>\n",
       "      <td>31714065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2631917</th>\n",
       "      <td>ENSG00000244355</td>\n",
       "      <td>ENSG00000244355.3</td>\n",
       "      <td>ENST00000375825</td>\n",
       "      <td>ENST00000375825.3</td>\n",
       "      <td>31683133</td>\n",
       "      <td>31685581</td>\n",
       "      <td>31683133</td>\n",
       "      <td>31685581</td>\n",
       "      <td>ENSP00000364985</td>\n",
       "      <td>ENSP00000364985.3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>LY6G6D-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>LY6G6D</td>\n",
       "      <td>1.0</td>\n",
       "      <td>402</td>\n",
       "      <td>ENSG00000244355</td>\n",
       "      <td>31715356.0</td>\n",
       "      <td>31717804.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2633805</th>\n",
       "      <td>ENSG00000204420</td>\n",
       "      <td>ENSG00000204420.4</td>\n",
       "      <td>ENST00000480039</td>\n",
       "      <td>ENST00000480039.1</td>\n",
       "      <td>31686371</td>\n",
       "      <td>31694491</td>\n",
       "      <td>31691121</td>\n",
       "      <td>31692750</td>\n",
       "      <td>ENSP00000419306</td>\n",
       "      <td>ENSP00000419306.1</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>C6orf25-009</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>C6orf25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>790</td>\n",
       "      <td>ENSG00000204420</td>\n",
       "      <td>31718594.0</td>\n",
       "      <td>31726714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2644781</th>\n",
       "      <td>ENSG00000204421</td>\n",
       "      <td>ENSG00000204421.2</td>\n",
       "      <td>ENST00000495859</td>\n",
       "      <td>ENST00000495859.1</td>\n",
       "      <td>31686425</td>\n",
       "      <td>31689622</td>\n",
       "      <td>31686425</td>\n",
       "      <td>31689456</td>\n",
       "      <td>ENSP00000433207</td>\n",
       "      <td>ENSP00000433207.1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>LY6G6C-002</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>LY6G6C</td>\n",
       "      <td>1.0</td>\n",
       "      <td>937</td>\n",
       "      <td>ENSG00000204421</td>\n",
       "      <td>31718648.0</td>\n",
       "      <td>31721746.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2644928</th>\n",
       "      <td>ENSG00000213722</td>\n",
       "      <td>ENSG00000213722.4</td>\n",
       "      <td>ENST00000437288</td>\n",
       "      <td>ENST00000437288.1</td>\n",
       "      <td>31694815</td>\n",
       "      <td>31698394</td>\n",
       "      <td>31694815</td>\n",
       "      <td>31696654</td>\n",
       "      <td>ENSP00000413164</td>\n",
       "      <td>ENSP00000413164.1</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>DDAH2-010</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>DDAH2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>724</td>\n",
       "      <td>ENSG00000213722</td>\n",
       "      <td>31727038.0</td>\n",
       "      <td>31730617.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2648064</th>\n",
       "      <td>ENSG00000213719</td>\n",
       "      <td>ENSG00000213719.4</td>\n",
       "      <td>ENST00000375784</td>\n",
       "      <td>ENST00000375784.3</td>\n",
       "      <td>31698358</td>\n",
       "      <td>31707540</td>\n",
       "      <td>31698358</td>\n",
       "      <td>31704344</td>\n",
       "      <td>ENSP00000364940</td>\n",
       "      <td>ENSP00000364940.3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>CLIC1-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>CLIC1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1254</td>\n",
       "      <td>ENSG00000213719</td>\n",
       "      <td>31730581.0</td>\n",
       "      <td>31739763.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26779</th>\n",
       "      <td>ENSG00000204703</td>\n",
       "      <td>ENSG00000204703.3</td>\n",
       "      <td>ENST00000377173</td>\n",
       "      <td>ENST00000377173.2</td>\n",
       "      <td>29053985</td>\n",
       "      <td>29055090</td>\n",
       "      <td>29053985</td>\n",
       "      <td>29055090</td>\n",
       "      <td>ENSP00000366378</td>\n",
       "      <td>ENSP00000366378.2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>OR2B3-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>OR2B3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1106</td>\n",
       "      <td>ENSG00000204703</td>\n",
       "      <td>29086208.0</td>\n",
       "      <td>29087313.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26772</th>\n",
       "      <td>ENSG00000204704</td>\n",
       "      <td>ENSG00000204704.2</td>\n",
       "      <td>ENST00000377175</td>\n",
       "      <td>ENST00000377175.1</td>\n",
       "      <td>29011990</td>\n",
       "      <td>29013017</td>\n",
       "      <td>29011990</td>\n",
       "      <td>29013017</td>\n",
       "      <td>ENSP00000366380</td>\n",
       "      <td>ENSP00000366380.1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>OR2W1-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>OR2W1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1028</td>\n",
       "      <td>ENSG00000204704</td>\n",
       "      <td>29044213.0</td>\n",
       "      <td>29045240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26601</th>\n",
       "      <td>ENSG00000197935</td>\n",
       "      <td>ENSG00000197935.6</td>\n",
       "      <td>ENST00000377179</td>\n",
       "      <td>ENST00000377179.3</td>\n",
       "      <td>28962562</td>\n",
       "      <td>28973093</td>\n",
       "      <td>28962562</td>\n",
       "      <td>28973037</td>\n",
       "      <td>ENSP00000366384</td>\n",
       "      <td>ENSP00000366384.3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>ZNF311-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>ZNF311</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2730</td>\n",
       "      <td>ENSG00000197935</td>\n",
       "      <td>28994785.0</td>\n",
       "      <td>29005316.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26560</th>\n",
       "      <td>ENSG00000204709</td>\n",
       "      <td>ENSG00000204709.4</td>\n",
       "      <td>ENST00000377186</td>\n",
       "      <td>ENST00000377186.3</td>\n",
       "      <td>28911654</td>\n",
       "      <td>28912314</td>\n",
       "      <td>28911654</td>\n",
       "      <td>28912314</td>\n",
       "      <td>ENSP00000366391</td>\n",
       "      <td>ENSP00000366391.3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>C6orf100-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>C6orf100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>466</td>\n",
       "      <td>ENSG00000204709</td>\n",
       "      <td>28943877.0</td>\n",
       "      <td>28944537.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4842751</th>\n",
       "      <td>ENSG00000137309</td>\n",
       "      <td>ENSG00000137309.15</td>\n",
       "      <td>ENST00000347617</td>\n",
       "      <td>ENST00000347617.6</td>\n",
       "      <td>34204650</td>\n",
       "      <td>34214008</td>\n",
       "      <td>34204650</td>\n",
       "      <td>34214008</td>\n",
       "      <td>ENSP00000288245</td>\n",
       "      <td>ENSP00000288245.9</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>HMGA1-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>HMGA1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1773</td>\n",
       "      <td>ENSG00000137309</td>\n",
       "      <td>34236873.0</td>\n",
       "      <td>34246231.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24109</th>\n",
       "      <td>ENSG00000204713</td>\n",
       "      <td>ENSG00000204713.6</td>\n",
       "      <td>ENST00000414543</td>\n",
       "      <td>ENST00000414543.1</td>\n",
       "      <td>28870779</td>\n",
       "      <td>28891766</td>\n",
       "      <td>28870779</td>\n",
       "      <td>28876766</td>\n",
       "      <td>ENSP00000400058</td>\n",
       "      <td>ENSP00000400058.1</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>TRIM27-003</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>TRIM27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1813</td>\n",
       "      <td>ENSG00000204713</td>\n",
       "      <td>28903002.0</td>\n",
       "      <td>28923988.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4844009</th>\n",
       "      <td>ENSG00000186577</td>\n",
       "      <td>ENSG00000186577.7</td>\n",
       "      <td>ENST00000476320</td>\n",
       "      <td>ENST00000476320.1</td>\n",
       "      <td>34214157</td>\n",
       "      <td>34217247</td>\n",
       "      <td>34214157</td>\n",
       "      <td>34217247</td>\n",
       "      <td>ENSP00000417604</td>\n",
       "      <td>ENSP00000417604.1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>C6orf1-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>C6orf1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1297</td>\n",
       "      <td>ENSG00000186577</td>\n",
       "      <td>34246381.0</td>\n",
       "      <td>34249108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4847832</th>\n",
       "      <td>ENSG00000272325</td>\n",
       "      <td>ENSG00000272325.1</td>\n",
       "      <td>ENST00000607016</td>\n",
       "      <td>ENST00000607016.1</td>\n",
       "      <td>34247456</td>\n",
       "      <td>34360451</td>\n",
       "      <td>34247456</td>\n",
       "      <td>34360451</td>\n",
       "      <td>ENSP00000476119</td>\n",
       "      <td>ENSP00000476119.1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>NUDT3-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>NUDT3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9905</td>\n",
       "      <td>ENSG00000272325</td>\n",
       "      <td>34279679.0</td>\n",
       "      <td>34392669.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4847857</th>\n",
       "      <td>ENSG00000270800</td>\n",
       "      <td>ENSG00000270800.1</td>\n",
       "      <td>ENST00000605528</td>\n",
       "      <td>ENST00000605528.1</td>\n",
       "      <td>34256547</td>\n",
       "      <td>34393825</td>\n",
       "      <td>34256547</td>\n",
       "      <td>34393825</td>\n",
       "      <td>ENSP00000475027</td>\n",
       "      <td>ENSP00000475027.1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>RPS10-NUDT3-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>RPS10-NUDT3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>876</td>\n",
       "      <td>ENSG00000270800</td>\n",
       "      <td>34284887.0</td>\n",
       "      <td>34426071.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4848118</th>\n",
       "      <td>ENSG00000124614</td>\n",
       "      <td>ENSG00000124614.9</td>\n",
       "      <td>ENST00000344700</td>\n",
       "      <td>ENST00000344700.3</td>\n",
       "      <td>34385231</td>\n",
       "      <td>34393902</td>\n",
       "      <td>34385231</td>\n",
       "      <td>34392998</td>\n",
       "      <td>ENSP00000363169</td>\n",
       "      <td>ENSP00000363169.1</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>RPS10-005</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>RPS10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>650</td>\n",
       "      <td>ENSG00000124614</td>\n",
       "      <td>34417454.0</td>\n",
       "      <td>34426069.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4851088</th>\n",
       "      <td>ENSG00000124507</td>\n",
       "      <td>ENSG00000124507.6</td>\n",
       "      <td>ENST00000244458</td>\n",
       "      <td>ENST00000244458.2</td>\n",
       "      <td>34433916</td>\n",
       "      <td>34503006</td>\n",
       "      <td>34433916</td>\n",
       "      <td>34503006</td>\n",
       "      <td>ENSP00000244458</td>\n",
       "      <td>ENSP00000244458.2</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>PACSIN1-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>PACSIN1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4229</td>\n",
       "      <td>ENSG00000124507</td>\n",
       "      <td>34466061.0</td>\n",
       "      <td>34535229.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4853180</th>\n",
       "      <td>ENSG00000124664</td>\n",
       "      <td>ENSG00000124664.6</td>\n",
       "      <td>ENST00000374037</td>\n",
       "      <td>ENST00000374037.3</td>\n",
       "      <td>34505579</td>\n",
       "      <td>34524110</td>\n",
       "      <td>34505579</td>\n",
       "      <td>34524091</td>\n",
       "      <td>ENSP00000363149</td>\n",
       "      <td>ENSP00000363149.3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>SPDEF-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>SPDEF</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1895</td>\n",
       "      <td>ENSG00000124664</td>\n",
       "      <td>34537802.0</td>\n",
       "      <td>34556333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22215</th>\n",
       "      <td>ENSG00000232040</td>\n",
       "      <td>ENSG00000232040.2</td>\n",
       "      <td>ENST00000452236</td>\n",
       "      <td>ENST00000452236.2</td>\n",
       "      <td>28539407</td>\n",
       "      <td>28583989</td>\n",
       "      <td>28539407</td>\n",
       "      <td>28555112</td>\n",
       "      <td>ENSP00000395259</td>\n",
       "      <td>ENSP00000395259.2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>SCAND3-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>SCAND3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4877</td>\n",
       "      <td>ENSG00000232040</td>\n",
       "      <td>28570535.0</td>\n",
       "      <td>28616212.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4853423</th>\n",
       "      <td>ENSG00000196821</td>\n",
       "      <td>ENSG00000196821.5</td>\n",
       "      <td>ENST00000374023</td>\n",
       "      <td>ENST00000374023.3</td>\n",
       "      <td>34555065</td>\n",
       "      <td>34664636</td>\n",
       "      <td>34555065</td>\n",
       "      <td>34664624</td>\n",
       "      <td>ENSP00000363135</td>\n",
       "      <td>ENSP00000363135.3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>C6orf106-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>C6orf106</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4418</td>\n",
       "      <td>ENSG00000196821</td>\n",
       "      <td>34587288.0</td>\n",
       "      <td>34696859.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20771</th>\n",
       "      <td>ENSG00000224586</td>\n",
       "      <td>ENSG00000224586.2</td>\n",
       "      <td>ENST00000412168</td>\n",
       "      <td>ENST00000412168.2</td>\n",
       "      <td>28493702</td>\n",
       "      <td>28502729</td>\n",
       "      <td>28493702</td>\n",
       "      <td>28502729</td>\n",
       "      <td>ENSP00000392398</td>\n",
       "      <td>ENSP00000392398.2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>GPX5-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>GPX5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1540</td>\n",
       "      <td>ENSG00000224586</td>\n",
       "      <td>28525881.0</td>\n",
       "      <td>28534955.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19091</th>\n",
       "      <td>ENSG00000198704</td>\n",
       "      <td>ENSG00000198704.5</td>\n",
       "      <td>ENST00000361902</td>\n",
       "      <td>ENST00000361902.1</td>\n",
       "      <td>28471073</td>\n",
       "      <td>28495992</td>\n",
       "      <td>28471073</td>\n",
       "      <td>28483570</td>\n",
       "      <td>ENSP00000354581</td>\n",
       "      <td>ENSP00000354581.1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>GPX6-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>GPX6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1712</td>\n",
       "      <td>ENSG00000198704</td>\n",
       "      <td>28503296.0</td>\n",
       "      <td>28528215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18852</th>\n",
       "      <td>ENSG00000187987</td>\n",
       "      <td>ENSG00000187987.5</td>\n",
       "      <td>ENST00000289788</td>\n",
       "      <td>ENST00000289788.4</td>\n",
       "      <td>28399707</td>\n",
       "      <td>28411279</td>\n",
       "      <td>28400432</td>\n",
       "      <td>28411244</td>\n",
       "      <td>ENSP00000289788</td>\n",
       "      <td>ENSP00000289788.4</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>ZSCAN23-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>ZSCAN23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3126</td>\n",
       "      <td>ENSG00000187987</td>\n",
       "      <td>28431930.0</td>\n",
       "      <td>28443502.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4854045</th>\n",
       "      <td>ENSG00000124562</td>\n",
       "      <td>ENSG00000124562.5</td>\n",
       "      <td>ENST00000244520</td>\n",
       "      <td>ENST00000244520.5</td>\n",
       "      <td>34725183</td>\n",
       "      <td>34741571</td>\n",
       "      <td>34725183</td>\n",
       "      <td>34741571</td>\n",
       "      <td>ENSP00000244520</td>\n",
       "      <td>ENSP00000244520.5</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>SNRPC-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>SNRPC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>842</td>\n",
       "      <td>ENSG00000124562</td>\n",
       "      <td>34757505.0</td>\n",
       "      <td>34773857.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17993</th>\n",
       "      <td>ENSG00000158691</td>\n",
       "      <td>ENSG00000158691.10</td>\n",
       "      <td>ENST00000361028</td>\n",
       "      <td>ENST00000361028.1</td>\n",
       "      <td>28346732</td>\n",
       "      <td>28367511</td>\n",
       "      <td>28346732</td>\n",
       "      <td>28367511</td>\n",
       "      <td>ENSP00000354305</td>\n",
       "      <td>ENSP00000354305.1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>ZSCAN12-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>ZSCAN12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5495</td>\n",
       "      <td>ENSG00000158691</td>\n",
       "      <td>28378955.0</td>\n",
       "      <td>28399734.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16373</th>\n",
       "      <td>ENSG00000189298</td>\n",
       "      <td>ENSG00000189298.9</td>\n",
       "      <td>ENST00000341464</td>\n",
       "      <td>ENST00000341464.5</td>\n",
       "      <td>28317691</td>\n",
       "      <td>28336947</td>\n",
       "      <td>28317727</td>\n",
       "      <td>28334523</td>\n",
       "      <td>ENSP00000341883</td>\n",
       "      <td>ENSP00000341883.5</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>ZKSCAN3-002</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>ZKSCAN3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1794</td>\n",
       "      <td>ENSG00000189298</td>\n",
       "      <td>28349947.0</td>\n",
       "      <td>28369172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4855013</th>\n",
       "      <td>ENSG00000065060</td>\n",
       "      <td>ENSG00000065060.12</td>\n",
       "      <td>ENST00000192788</td>\n",
       "      <td>ENST00000192788.5</td>\n",
       "      <td>34759857</td>\n",
       "      <td>34850915</td>\n",
       "      <td>34759857</td>\n",
       "      <td>34845291</td>\n",
       "      <td>ENSP00000192788</td>\n",
       "      <td>ENSP00000192788.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>UHRF1BP1-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>UHRF1BP1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9570</td>\n",
       "      <td>ENSG00000065060</td>\n",
       "      <td>34792015.0</td>\n",
       "      <td>34883138.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6309</th>\n",
       "      <td>ENSG00000235109</td>\n",
       "      <td>ENSG00000235109.3</td>\n",
       "      <td>ENST00000439158</td>\n",
       "      <td>ENST00000439158.1</td>\n",
       "      <td>28292470</td>\n",
       "      <td>28324048</td>\n",
       "      <td>28292514</td>\n",
       "      <td>28303911</td>\n",
       "      <td>ENSP00000413705</td>\n",
       "      <td>ENSP00000413705.1</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>ZSCAN31-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>ZSCAN31</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3050</td>\n",
       "      <td>ENSG00000235109</td>\n",
       "      <td>28324693.0</td>\n",
       "      <td>28356271.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5718</th>\n",
       "      <td>ENSG00000137338</td>\n",
       "      <td>ENSG00000137338.4</td>\n",
       "      <td>ENST00000259883</td>\n",
       "      <td>ENST00000259883.3</td>\n",
       "      <td>28249314</td>\n",
       "      <td>28270326</td>\n",
       "      <td>28249349</td>\n",
       "      <td>28270326</td>\n",
       "      <td>ENSP00000259883</td>\n",
       "      <td>ENSP00000259883.3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>PGBD1-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>PGBD1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3100</td>\n",
       "      <td>ENSG00000137338</td>\n",
       "      <td>28281572.0</td>\n",
       "      <td>28302549.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4862410</th>\n",
       "      <td>ENSG00000064995</td>\n",
       "      <td>ENSG00000064995.12</td>\n",
       "      <td>ENST00000361288</td>\n",
       "      <td>ENST00000361288.4</td>\n",
       "      <td>34845555</td>\n",
       "      <td>34855866</td>\n",
       "      <td>34845555</td>\n",
       "      <td>34855866</td>\n",
       "      <td>ENSP00000354633</td>\n",
       "      <td>ENSP00000354633.4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>TAF11-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>TAF11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1580</td>\n",
       "      <td>ENSG00000064995</td>\n",
       "      <td>34877462.0</td>\n",
       "      <td>34888071.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5116</th>\n",
       "      <td>ENSG00000189134</td>\n",
       "      <td>ENSG00000189134.3</td>\n",
       "      <td>ENST00000343684</td>\n",
       "      <td>ENST00000343684.3</td>\n",
       "      <td>28227098</td>\n",
       "      <td>28228736</td>\n",
       "      <td>28227098</td>\n",
       "      <td>28228736</td>\n",
       "      <td>ENSP00000345716</td>\n",
       "      <td>ENSP00000345716.3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>NKAPL-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>NKAPL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1639</td>\n",
       "      <td>ENSG00000189134</td>\n",
       "      <td>28259297.0</td>\n",
       "      <td>28260958.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4816</th>\n",
       "      <td>ENSG00000187626</td>\n",
       "      <td>ENSG00000187626.7</td>\n",
       "      <td>ENST00000377294</td>\n",
       "      <td>ENST00000377294.2</td>\n",
       "      <td>28212401</td>\n",
       "      <td>28227011</td>\n",
       "      <td>28212401</td>\n",
       "      <td>28220002</td>\n",
       "      <td>ENSP00000366509</td>\n",
       "      <td>ENSP00000366509.2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>ZKSCAN4-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>ZKSCAN4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2375</td>\n",
       "      <td>ENSG00000187626</td>\n",
       "      <td>28241697.0</td>\n",
       "      <td>28252269.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3024</th>\n",
       "      <td>ENSG00000137185</td>\n",
       "      <td>ENSG00000137185.7</td>\n",
       "      <td>ENST00000531941</td>\n",
       "      <td>ENST00000531941.1</td>\n",
       "      <td>28192664</td>\n",
       "      <td>28201260</td>\n",
       "      <td>28192664</td>\n",
       "      <td>28194985</td>\n",
       "      <td>ENSP00000433307</td>\n",
       "      <td>ENSP00000433307.1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>ZSCAN9-005</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>ZSCAN9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>579</td>\n",
       "      <td>ENSG00000137185</td>\n",
       "      <td>28224886.0</td>\n",
       "      <td>28233482.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>ENSG00000198315</td>\n",
       "      <td>ENSG00000198315.6</td>\n",
       "      <td>ENST00000457389</td>\n",
       "      <td>ENST00000457389.2</td>\n",
       "      <td>28109688</td>\n",
       "      <td>28127250</td>\n",
       "      <td>28109716</td>\n",
       "      <td>28121851</td>\n",
       "      <td>ENSP00000402948</td>\n",
       "      <td>ENSP00000402948.2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>ZKSCAN8-003</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>ZKSCAN8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2071</td>\n",
       "      <td>ENSG00000198315</td>\n",
       "      <td>28141883.0</td>\n",
       "      <td>28159460.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>ENSG00000196812</td>\n",
       "      <td>ENSG00000196812.4</td>\n",
       "      <td>ENST00000340487</td>\n",
       "      <td>ENST00000340487.4</td>\n",
       "      <td>28092338</td>\n",
       "      <td>28097860</td>\n",
       "      <td>28092338</td>\n",
       "      <td>28097860</td>\n",
       "      <td>ENSP00000366527</td>\n",
       "      <td>ENSP00000366527.3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>ZSCAN16-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>ZSCAN16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1328</td>\n",
       "      <td>ENSG00000196812</td>\n",
       "      <td>28124609.0</td>\n",
       "      <td>28130082.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ENSG00000197279</td>\n",
       "      <td>ENSG00000197279.3</td>\n",
       "      <td>ENST00000377325</td>\n",
       "      <td>ENST00000377325.1</td>\n",
       "      <td>28048753</td>\n",
       "      <td>28057341</td>\n",
       "      <td>28048753</td>\n",
       "      <td>28057341</td>\n",
       "      <td>ENSP00000366542</td>\n",
       "      <td>ENSP00000366542.1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>HGNC transcript name</td>\n",
       "      <td>ZNF165-001</td>\n",
       "      <td>HGNC Symbol</td>\n",
       "      <td>ZNF165</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2107</td>\n",
       "      <td>ENSG00000197279</td>\n",
       "      <td>28080568.0</td>\n",
       "      <td>28089563.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>207 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Gene stable ID Gene stable ID version Transcript stable ID  \\\n",
       "2187189  ENSG00000226979      ENSG00000226979.4      ENST00000454783   \n",
       "2187777  ENSG00000232810      ENSG00000232810.3      ENST00000449264   \n",
       "2187949  ENSG00000227507      ENSG00000227507.2      ENST00000429299   \n",
       "2188273  ENSG00000204482      ENSG00000204482.6      ENST00000418507   \n",
       "2205633  ENSG00000204475      ENSG00000204475.5      ENST00000340027   \n",
       "2186467  ENSG00000204498      ENSG00000204498.6      ENST00000376146   \n",
       "2185022  ENSG00000213760      ENSG00000213760.6      ENST00000376151   \n",
       "2127794  ENSG00000198563      ENSG00000198563.9      ENST00000376177   \n",
       "2123980  ENSG00000204511      ENSG00000204511.2      ENST00000376191   \n",
       "2207335  ENSG00000204472      ENSG00000204472.8      ENST00000376059   \n",
       "2209271  ENSG00000204469      ENSG00000204469.8      ENST00000376007   \n",
       "2245560  ENSG00000204463      ENSG00000204463.8      ENST00000422948   \n",
       "2123486  ENSG00000204516      ENSG00000204516.5      ENST00000399150   \n",
       "2541096  ENSG00000204444      ENSG00000204444.6      ENST00000375918   \n",
       "2541963  ENSG00000204439      ENSG00000204439.3      ENST00000375911   \n",
       "2541969  ENSG00000204438      ENSG00000204438.6      ENST00000375906   \n",
       "2545890  ENSG00000204435      ENSG00000204435.9      ENST00000375885   \n",
       "2549588  ENSG00000263020      ENSG00000263020.1      ENST00000375880   \n",
       "2549804  ENSG00000240053      ENSG00000240053.8      ENST00000375864   \n",
       "2549902  ENSG00000204428      ENSG00000204428.8      ENST00000375863   \n",
       "2551666  ENSG00000204427      ENSG00000204427.7      ENST00000395952   \n",
       "2630866  ENSG00000204422      ENSG00000204422.7      ENST00000461287   \n",
       "2631351  ENSG00000204424      ENSG00000204424.8      ENST00000375832   \n",
       "2631639  ENSG00000250641      ENSG00000250641.1      ENST00000503322   \n",
       "2631675  ENSG00000255552      ENSG00000255552.3      ENST00000409239   \n",
       "2631917  ENSG00000244355      ENSG00000244355.3      ENST00000375825   \n",
       "2633805  ENSG00000204420      ENSG00000204420.4      ENST00000480039   \n",
       "2644781  ENSG00000204421      ENSG00000204421.2      ENST00000495859   \n",
       "2644928  ENSG00000213722      ENSG00000213722.4      ENST00000437288   \n",
       "2648064  ENSG00000213719      ENSG00000213719.4      ENST00000375784   \n",
       "...                  ...                    ...                  ...   \n",
       "26779    ENSG00000204703      ENSG00000204703.3      ENST00000377173   \n",
       "26772    ENSG00000204704      ENSG00000204704.2      ENST00000377175   \n",
       "26601    ENSG00000197935      ENSG00000197935.6      ENST00000377179   \n",
       "26560    ENSG00000204709      ENSG00000204709.4      ENST00000377186   \n",
       "4842751  ENSG00000137309     ENSG00000137309.15      ENST00000347617   \n",
       "24109    ENSG00000204713      ENSG00000204713.6      ENST00000414543   \n",
       "4844009  ENSG00000186577      ENSG00000186577.7      ENST00000476320   \n",
       "4847832  ENSG00000272325      ENSG00000272325.1      ENST00000607016   \n",
       "4847857  ENSG00000270800      ENSG00000270800.1      ENST00000605528   \n",
       "4848118  ENSG00000124614      ENSG00000124614.9      ENST00000344700   \n",
       "4851088  ENSG00000124507      ENSG00000124507.6      ENST00000244458   \n",
       "4853180  ENSG00000124664      ENSG00000124664.6      ENST00000374037   \n",
       "22215    ENSG00000232040      ENSG00000232040.2      ENST00000452236   \n",
       "4853423  ENSG00000196821      ENSG00000196821.5      ENST00000374023   \n",
       "20771    ENSG00000224586      ENSG00000224586.2      ENST00000412168   \n",
       "19091    ENSG00000198704      ENSG00000198704.5      ENST00000361902   \n",
       "18852    ENSG00000187987      ENSG00000187987.5      ENST00000289788   \n",
       "4854045  ENSG00000124562      ENSG00000124562.5      ENST00000244520   \n",
       "17993    ENSG00000158691     ENSG00000158691.10      ENST00000361028   \n",
       "16373    ENSG00000189298      ENSG00000189298.9      ENST00000341464   \n",
       "4855013  ENSG00000065060     ENSG00000065060.12      ENST00000192788   \n",
       "6309     ENSG00000235109      ENSG00000235109.3      ENST00000439158   \n",
       "5718     ENSG00000137338      ENSG00000137338.4      ENST00000259883   \n",
       "4862410  ENSG00000064995     ENSG00000064995.12      ENST00000361288   \n",
       "5116     ENSG00000189134      ENSG00000189134.3      ENST00000343684   \n",
       "4816     ENSG00000187626      ENSG00000187626.7      ENST00000377294   \n",
       "3024     ENSG00000137185      ENSG00000137185.7      ENST00000531941   \n",
       "691      ENSG00000198315      ENSG00000198315.6      ENST00000457389   \n",
       "158      ENSG00000196812      ENSG00000196812.4      ENST00000340487   \n",
       "11       ENSG00000197279      ENSG00000197279.3      ENST00000377325   \n",
       "\n",
       "        Transcript stable ID version  Gene start (bp)  Gene end (bp)  \\\n",
       "2187189            ENST00000454783.1         31539831       31542101   \n",
       "2187777            ENST00000449264.2         31543344       31546113   \n",
       "2187949            ENST00000429299.2         31548302       31550299   \n",
       "2188273            ENST00000418507.2         31553901       31556686   \n",
       "2205633            ENST00000340027.5         31556672       31560762   \n",
       "2186467            ENST00000376146.4         31514647       31526606   \n",
       "2185022            ENST00000376151.4         31512239       31516204   \n",
       "2127794            ENST00000376177.2         31497996       31510225   \n",
       "2123980            ENST00000376191.2         31496494       31498009   \n",
       "2207335            ENST00000376059.3         31582961       31584798   \n",
       "2209271            ENST00000376007.4         31588497       31605548   \n",
       "2245560            ENST00000422948.1         31606805       31620482   \n",
       "2123486            ENST00000399150.3         31462658       31478901   \n",
       "2541096            ENST00000375918.2         31620193       31625987   \n",
       "2541963            ENST00000375911.1         31626075       31628549   \n",
       "2541969            ENST00000375906.1         31629006       31634060   \n",
       "2545890            ENST00000375885.4         31633013       31638120   \n",
       "2549588            ENST00000375880.2         31633879       31641323   \n",
       "2549804            ENST00000375864.4         31637944       31641553   \n",
       "2549902            ENST00000375863.3         31644461       31651817   \n",
       "2551666            ENST00000395952.3         31654726       31671221   \n",
       "2630866            ENST00000461287.1         31654739       31681849   \n",
       "2631351            ENST00000375832.4         31674640       31685581   \n",
       "2631639            ENST00000503322.1         31674681       31685695   \n",
       "2631675            ENST00000409239.1         31679548       31681842   \n",
       "2631917            ENST00000375825.3         31683133       31685581   \n",
       "2633805            ENST00000480039.1         31686371       31694491   \n",
       "2644781            ENST00000495859.1         31686425       31689622   \n",
       "2644928            ENST00000437288.1         31694815       31698394   \n",
       "2648064            ENST00000375784.3         31698358       31707540   \n",
       "...                              ...              ...            ...   \n",
       "26779              ENST00000377173.2         29053985       29055090   \n",
       "26772              ENST00000377175.1         29011990       29013017   \n",
       "26601              ENST00000377179.3         28962562       28973093   \n",
       "26560              ENST00000377186.3         28911654       28912314   \n",
       "4842751            ENST00000347617.6         34204650       34214008   \n",
       "24109              ENST00000414543.1         28870779       28891766   \n",
       "4844009            ENST00000476320.1         34214157       34217247   \n",
       "4847832            ENST00000607016.1         34247456       34360451   \n",
       "4847857            ENST00000605528.1         34256547       34393825   \n",
       "4848118            ENST00000344700.3         34385231       34393902   \n",
       "4851088            ENST00000244458.2         34433916       34503006   \n",
       "4853180            ENST00000374037.3         34505579       34524110   \n",
       "22215              ENST00000452236.2         28539407       28583989   \n",
       "4853423            ENST00000374023.3         34555065       34664636   \n",
       "20771              ENST00000412168.2         28493702       28502729   \n",
       "19091              ENST00000361902.1         28471073       28495992   \n",
       "18852              ENST00000289788.4         28399707       28411279   \n",
       "4854045            ENST00000244520.5         34725183       34741571   \n",
       "17993              ENST00000361028.1         28346732       28367511   \n",
       "16373              ENST00000341464.5         28317691       28336947   \n",
       "4855013            ENST00000192788.5         34759857       34850915   \n",
       "6309               ENST00000439158.1         28292470       28324048   \n",
       "5718               ENST00000259883.3         28249314       28270326   \n",
       "4862410            ENST00000361288.4         34845555       34855866   \n",
       "5116               ENST00000343684.3         28227098       28228736   \n",
       "4816               ENST00000377294.2         28212401       28227011   \n",
       "3024               ENST00000531941.1         28192664       28201260   \n",
       "691                ENST00000457389.2         28109688       28127250   \n",
       "158                ENST00000340487.4         28092338       28097860   \n",
       "11                 ENST00000377325.1         28048753       28057341   \n",
       "\n",
       "         Transcript start (bp)  Transcript end (bp) Protein stable ID  \\\n",
       "2187189               31539831             31542101   ENSP00000403495   \n",
       "2187777               31543344             31546113   ENSP00000398698   \n",
       "2187949               31548332             31550202   ENSP00000410481   \n",
       "2188273               31553901             31556686   ENSP00000405900   \n",
       "2205633               31556672             31560762   ENSP00000342156   \n",
       "2186467               31514647             31526606   ENSP00000365316   \n",
       "2185022               31512239             31514414   ENSP00000365321   \n",
       "2127794               31497996             31508313   ENSP00000365347   \n",
       "2123980               31496494             31498009   ENSP00000365362   \n",
       "2207335               31582961             31584798   ENSP00000365227   \n",
       "2209271               31588497             31605548   ENSP00000365175   \n",
       "2245560               31606805             31608714   ENSP00000413225   \n",
       "2123486               31465863             31478901   ENSP00000382103   \n",
       "2541096               31620193             31625859   ENSP00000365083   \n",
       "2541963               31626075             31628549   ENSP00000365076   \n",
       "2541969               31629006             31634060   ENSP00000365071   \n",
       "2545890               31633013             31637847   ENSP00000365046   \n",
       "2549588               31633879             31641323   ENSP00000365040   \n",
       "2549804               31637944             31641323   ENSP00000365024   \n",
       "2549902               31644461             31651817   ENSP00000365023   \n",
       "2551666               31654726             31671221   ENSP00000379282   \n",
       "2630866               31654739             31681849   ENSP00000475702   \n",
       "2631351               31674662             31678372   ENSP00000364992   \n",
       "2631639               31674681             31685695   ENSP00000421232   \n",
       "2631675               31679548             31681842   ENSP00000386842   \n",
       "2631917               31683133             31685581   ENSP00000364985   \n",
       "2633805               31691121             31692750   ENSP00000419306   \n",
       "2644781               31686425             31689456   ENSP00000433207   \n",
       "2644928               31694815             31696654   ENSP00000413164   \n",
       "2648064               31698358             31704344   ENSP00000364940   \n",
       "...                        ...                  ...               ...   \n",
       "26779                 29053985             29055090   ENSP00000366378   \n",
       "26772                 29011990             29013017   ENSP00000366380   \n",
       "26601                 28962562             28973037   ENSP00000366384   \n",
       "26560                 28911654             28912314   ENSP00000366391   \n",
       "4842751               34204650             34214008   ENSP00000288245   \n",
       "24109                 28870779             28876766   ENSP00000400058   \n",
       "4844009               34214157             34217247   ENSP00000417604   \n",
       "4847832               34247456             34360451   ENSP00000476119   \n",
       "4847857               34256547             34393825   ENSP00000475027   \n",
       "4848118               34385231             34392998   ENSP00000363169   \n",
       "4851088               34433916             34503006   ENSP00000244458   \n",
       "4853180               34505579             34524091   ENSP00000363149   \n",
       "22215                 28539407             28555112   ENSP00000395259   \n",
       "4853423               34555065             34664624   ENSP00000363135   \n",
       "20771                 28493702             28502729   ENSP00000392398   \n",
       "19091                 28471073             28483570   ENSP00000354581   \n",
       "18852                 28400432             28411244   ENSP00000289788   \n",
       "4854045               34725183             34741571   ENSP00000244520   \n",
       "17993                 28346732             28367511   ENSP00000354305   \n",
       "16373                 28317727             28334523   ENSP00000341883   \n",
       "4855013               34759857             34845291   ENSP00000192788   \n",
       "6309                  28292514             28303911   ENSP00000413705   \n",
       "5718                  28249349             28270326   ENSP00000259883   \n",
       "4862410               34845555             34855866   ENSP00000354633   \n",
       "5116                  28227098             28228736   ENSP00000345716   \n",
       "4816                  28212401             28220002   ENSP00000366509   \n",
       "3024                  28192664             28194985   ENSP00000433307   \n",
       "691                   28109716             28121851   ENSP00000402948   \n",
       "158                   28092338             28097860   ENSP00000366527   \n",
       "11                    28048753             28057341   ENSP00000366542   \n",
       "\n",
       "        Protein stable ID version  ... Transcript count  \\\n",
       "2187189         ENSP00000403495.1  ...                4   \n",
       "2187777         ENSP00000398698.2  ...                1   \n",
       "2187949         ENSP00000410481.2  ...                4   \n",
       "2188273         ENSP00000405900.2  ...               24   \n",
       "2205633         ENSP00000342156.5  ...                6   \n",
       "2186467         ENSP00000365316.4  ...                5   \n",
       "2185022         ENSP00000365321.4  ...                6   \n",
       "2127794         ENSP00000365347.2  ...               25   \n",
       "2123980         ENSP00000365362.2  ...                1   \n",
       "2207335         ENSP00000365227.3  ...                5   \n",
       "2209271         ENSP00000365175.4  ...               14   \n",
       "2245560         ENSP00000413225.1  ...               30   \n",
       "2123486         ENSP00000382103.3  ...                5   \n",
       "2541096         ENSP00000365083.2  ...                3   \n",
       "2541963         ENSP00000365076.1  ...                1   \n",
       "2541969         ENSP00000365071.1  ...                8   \n",
       "2545890         ENSP00000365046.4  ...                8   \n",
       "2549588         ENSP00000365040.2  ...                2   \n",
       "2549804         ENSP00000365024.4  ...                3   \n",
       "2549902         ENSP00000365023.3  ...                9   \n",
       "2551666         ENSP00000379282.3  ...               18   \n",
       "2630866         ENSP00000475702.1  ...                1   \n",
       "2631351         ENSP00000364992.4  ...                2   \n",
       "2631639         ENSP00000421232.1  ...                1   \n",
       "2631675         ENSP00000386842.1  ...                4   \n",
       "2631917         ENSP00000364985.3  ...                3   \n",
       "2633805         ENSP00000419306.1  ...               11   \n",
       "2644781         ENSP00000433207.1  ...                2   \n",
       "2644928         ENSP00000413164.1  ...               10   \n",
       "2648064         ENSP00000364940.3  ...                4   \n",
       "...                           ...  ...              ...   \n",
       "26779           ENSP00000366378.2  ...                1   \n",
       "26772           ENSP00000366380.1  ...                1   \n",
       "26601           ENSP00000366384.3  ...                2   \n",
       "26560           ENSP00000366391.3  ...                1   \n",
       "4842751         ENSP00000288245.9  ...                7   \n",
       "24109           ENSP00000400058.1  ...                7   \n",
       "4844009         ENSP00000417604.1  ...                8   \n",
       "4847832         ENSP00000476119.1  ...                1   \n",
       "4847857         ENSP00000475027.1  ...                1   \n",
       "4848118         ENSP00000363169.1  ...                6   \n",
       "4851088         ENSP00000244458.2  ...                6   \n",
       "4853180         ENSP00000363149.3  ...                2   \n",
       "22215           ENSP00000395259.2  ...                4   \n",
       "4853423         ENSP00000363135.3  ...                3   \n",
       "20771           ENSP00000392398.2  ...                4   \n",
       "19091           ENSP00000354581.1  ...                3   \n",
       "18852           ENSP00000289788.4  ...                3   \n",
       "4854045         ENSP00000244520.5  ...                4   \n",
       "17993           ENSP00000354305.1  ...                2   \n",
       "16373           ENSP00000341883.5  ...                3   \n",
       "4855013         ENSP00000192788.5  ...                2   \n",
       "6309            ENSP00000413705.1  ...               18   \n",
       "5718            ENSP00000259883.3  ...                2   \n",
       "4862410         ENSP00000354633.4  ...                2   \n",
       "5116            ENSP00000345716.3  ...                1   \n",
       "4816            ENSP00000366509.2  ...                2   \n",
       "3024            ENSP00000433307.1  ...                8   \n",
       "691             ENSP00000402948.2  ...                4   \n",
       "158             ENSP00000366527.3  ...                1   \n",
       "11              ENSP00000366542.1  ...                1   \n",
       "\n",
       "             Source of transcript name         Transcript name  \\\n",
       "2187189           HGNC transcript name                 LTA-002   \n",
       "2187777           HGNC transcript name                 TNF-001   \n",
       "2187949           HGNC transcript name                 LTB-001   \n",
       "2188273           HGNC transcript name                LST1-011   \n",
       "2205633           HGNC transcript name                NCR3-001   \n",
       "2186467           HGNC transcript name             NFKBIL1-003   \n",
       "2185022           HGNC transcript name            ATP6V1G2-002   \n",
       "2127794           HGNC transcript name              DDX39B-006   \n",
       "2123980           HGNC transcript name               MCCD1-001   \n",
       "2207335           HGNC transcript name                AIF1-001   \n",
       "2209271           HGNC transcript name              PRRC2A-001   \n",
       "2245560           HGNC transcript name                BAG6-030   \n",
       "2123486           HGNC transcript name                MICB-002   \n",
       "2541096           HGNC transcript name                APOM-003   \n",
       "2541963           HGNC transcript name             C6orf47-001   \n",
       "2541969           HGNC transcript name              GPANK1-003   \n",
       "2545890           HGNC transcript name              CSNK2B-004   \n",
       "2549588        UniProt transcript name  CSNK2B-LY6G5B-1181-001   \n",
       "2549804           HGNC transcript name              LY6G5B-001   \n",
       "2549902           HGNC transcript name              LY6G5C-001   \n",
       "2551666           HGNC transcript name             ABHD16A-001   \n",
       "2630866  Clone-based (Vega) transcript    XXbac-BPG32J3.20-001   \n",
       "2631351           HGNC transcript name              LY6G6F-001   \n",
       "2631639        UniProt transcript name               MEGT1-001   \n",
       "2631675           HGNC transcript name              LY6G6E-005   \n",
       "2631917           HGNC transcript name              LY6G6D-001   \n",
       "2633805           HGNC transcript name             C6orf25-009   \n",
       "2644781           HGNC transcript name              LY6G6C-002   \n",
       "2644928           HGNC transcript name               DDAH2-010   \n",
       "2648064           HGNC transcript name               CLIC1-001   \n",
       "...                                ...                     ...   \n",
       "26779             HGNC transcript name               OR2B3-001   \n",
       "26772             HGNC transcript name               OR2W1-001   \n",
       "26601             HGNC transcript name              ZNF311-001   \n",
       "26560             HGNC transcript name            C6orf100-001   \n",
       "4842751           HGNC transcript name               HMGA1-001   \n",
       "24109             HGNC transcript name              TRIM27-003   \n",
       "4844009           HGNC transcript name              C6orf1-001   \n",
       "4847832           HGNC transcript name               NUDT3-001   \n",
       "4847857           HGNC transcript name         RPS10-NUDT3-001   \n",
       "4848118           HGNC transcript name               RPS10-005   \n",
       "4851088           HGNC transcript name             PACSIN1-001   \n",
       "4853180           HGNC transcript name               SPDEF-001   \n",
       "22215             HGNC transcript name              SCAND3-001   \n",
       "4853423           HGNC transcript name            C6orf106-001   \n",
       "20771             HGNC transcript name                GPX5-001   \n",
       "19091             HGNC transcript name                GPX6-001   \n",
       "18852             HGNC transcript name             ZSCAN23-001   \n",
       "4854045           HGNC transcript name               SNRPC-001   \n",
       "17993             HGNC transcript name             ZSCAN12-001   \n",
       "16373             HGNC transcript name             ZKSCAN3-002   \n",
       "4855013           HGNC transcript name            UHRF1BP1-001   \n",
       "6309              HGNC transcript name             ZSCAN31-001   \n",
       "5718              HGNC transcript name               PGBD1-001   \n",
       "4862410           HGNC transcript name               TAF11-001   \n",
       "5116              HGNC transcript name               NKAPL-001   \n",
       "4816              HGNC transcript name             ZKSCAN4-001   \n",
       "3024              HGNC transcript name              ZSCAN9-005   \n",
       "691               HGNC transcript name             ZKSCAN8-003   \n",
       "158               HGNC transcript name             ZSCAN16-001   \n",
       "11                HGNC transcript name              ZNF165-001   \n",
       "\n",
       "             Source of gene name           Gene name  \\\n",
       "2187189              HGNC Symbol                 LTA   \n",
       "2187777              HGNC Symbol                 TNF   \n",
       "2187949              HGNC Symbol                 LTB   \n",
       "2188273              HGNC Symbol                LST1   \n",
       "2205633              HGNC Symbol                NCR3   \n",
       "2186467              HGNC Symbol             NFKBIL1   \n",
       "2185022              HGNC Symbol            ATP6V1G2   \n",
       "2127794              HGNC Symbol              DDX39B   \n",
       "2123980              HGNC Symbol               MCCD1   \n",
       "2207335              HGNC Symbol                AIF1   \n",
       "2209271              HGNC Symbol              PRRC2A   \n",
       "2245560              HGNC Symbol                BAG6   \n",
       "2123486              HGNC Symbol                MICB   \n",
       "2541096              HGNC Symbol                APOM   \n",
       "2541963              HGNC Symbol             C6orf47   \n",
       "2541969              HGNC Symbol              GPANK1   \n",
       "2545890              HGNC Symbol              CSNK2B   \n",
       "2549588      UniProtKB Gene Name  CSNK2B-LY6G5B-1181   \n",
       "2549804              HGNC Symbol              LY6G5B   \n",
       "2549902              HGNC Symbol              LY6G5C   \n",
       "2551666              HGNC Symbol             ABHD16A   \n",
       "2630866  Clone-based (Vega) gene    XXbac-BPG32J3.20   \n",
       "2631351              HGNC Symbol              LY6G6F   \n",
       "2631639      UniProtKB Gene Name               MEGT1   \n",
       "2631675              HGNC Symbol              LY6G6E   \n",
       "2631917              HGNC Symbol              LY6G6D   \n",
       "2633805              HGNC Symbol             C6orf25   \n",
       "2644781              HGNC Symbol              LY6G6C   \n",
       "2644928              HGNC Symbol               DDAH2   \n",
       "2648064              HGNC Symbol               CLIC1   \n",
       "...                          ...                 ...   \n",
       "26779                HGNC Symbol               OR2B3   \n",
       "26772                HGNC Symbol               OR2W1   \n",
       "26601                HGNC Symbol              ZNF311   \n",
       "26560                HGNC Symbol            C6orf100   \n",
       "4842751              HGNC Symbol               HMGA1   \n",
       "24109                HGNC Symbol              TRIM27   \n",
       "4844009              HGNC Symbol              C6orf1   \n",
       "4847832              HGNC Symbol               NUDT3   \n",
       "4847857              HGNC Symbol         RPS10-NUDT3   \n",
       "4848118              HGNC Symbol               RPS10   \n",
       "4851088              HGNC Symbol             PACSIN1   \n",
       "4853180              HGNC Symbol               SPDEF   \n",
       "22215                HGNC Symbol              SCAND3   \n",
       "4853423              HGNC Symbol            C6orf106   \n",
       "20771                HGNC Symbol                GPX5   \n",
       "19091                HGNC Symbol                GPX6   \n",
       "18852                HGNC Symbol             ZSCAN23   \n",
       "4854045              HGNC Symbol               SNRPC   \n",
       "17993                HGNC Symbol             ZSCAN12   \n",
       "16373                HGNC Symbol             ZKSCAN3   \n",
       "4855013              HGNC Symbol            UHRF1BP1   \n",
       "6309                 HGNC Symbol             ZSCAN31   \n",
       "5718                 HGNC Symbol               PGBD1   \n",
       "4862410              HGNC Symbol               TAF11   \n",
       "5116                 HGNC Symbol               NKAPL   \n",
       "4816                 HGNC Symbol             ZKSCAN4   \n",
       "3024                 HGNC Symbol              ZSCAN9   \n",
       "691                  HGNC Symbol             ZKSCAN8   \n",
       "158                  HGNC Symbol             ZSCAN16   \n",
       "11                   HGNC Symbol              ZNF165   \n",
       "\n",
       "         GENCODE basic annotation  Transcript length (including UTRs and CDS)  \\\n",
       "2187189                       1.0                                        1507   \n",
       "2187777                       1.0                                        1676   \n",
       "2187949                       1.0                                         897   \n",
       "2188273                       1.0                                         726   \n",
       "2205633                       1.0                                        1042   \n",
       "2186467                       NaN                                        1411   \n",
       "2185022                       1.0                                        1261   \n",
       "2127794                       1.0                                        1529   \n",
       "2123980                       1.0                                         955   \n",
       "2207335                       1.0                                         711   \n",
       "2209271                       1.0                                        6861   \n",
       "2245560                       NaN                                         513   \n",
       "2123486                       1.0                                        2346   \n",
       "2541096                       1.0                                         922   \n",
       "2541963                       1.0                                        2475   \n",
       "2541969                       1.0                                        2793   \n",
       "2545890                       1.0                                        1061   \n",
       "2549588                       1.0                                        2487   \n",
       "2549804                       1.0                                        2654   \n",
       "2549902                       NaN                                         950   \n",
       "2551666                       1.0                                        2103   \n",
       "2630866                       1.0                                        2332   \n",
       "2631351                       1.0                                         920   \n",
       "2631639                       1.0                                        1266   \n",
       "2631675                       1.0                                         776   \n",
       "2631917                       1.0                                         402   \n",
       "2633805                       1.0                                         790   \n",
       "2644781                       1.0                                         937   \n",
       "2644928                       NaN                                         724   \n",
       "2648064                       1.0                                        1254   \n",
       "...                           ...                                         ...   \n",
       "26779                         1.0                                        1106   \n",
       "26772                         1.0                                        1028   \n",
       "26601                         1.0                                        2730   \n",
       "26560                         1.0                                         466   \n",
       "4842751                       1.0                                        1773   \n",
       "24109                         NaN                                        1813   \n",
       "4844009                       1.0                                        1297   \n",
       "4847832                       1.0                                        9905   \n",
       "4847857                       1.0                                         876   \n",
       "4848118                       1.0                                         650   \n",
       "4851088                       1.0                                        4229   \n",
       "4853180                       1.0                                        1895   \n",
       "22215                         1.0                                        4877   \n",
       "4853423                       1.0                                        4418   \n",
       "20771                         1.0                                        1540   \n",
       "19091                         1.0                                        1712   \n",
       "18852                         1.0                                        3126   \n",
       "4854045                       1.0                                         842   \n",
       "17993                         1.0                                        5495   \n",
       "16373                         1.0                                        1794   \n",
       "4855013                       1.0                                        9570   \n",
       "6309                          1.0                                        3050   \n",
       "5718                          1.0                                        3100   \n",
       "4862410                       1.0                                        1580   \n",
       "5116                          1.0                                        1639   \n",
       "4816                          1.0                                        2375   \n",
       "3024                          NaN                                         579   \n",
       "691                           1.0                                        2071   \n",
       "158                           1.0                                        1328   \n",
       "11                            1.0                                        2107   \n",
       "\n",
       "         Gene stable ID38 Gene start (bp)38 Gene end (bp)38  \n",
       "2187189   ENSG00000226979        31572054.0      31574324.0  \n",
       "2187777   ENSG00000232810        31575565.0      31578336.0  \n",
       "2187949   ENSG00000227507        31580525.0      31582522.0  \n",
       "2188273   ENSG00000204482        31586124.0      31588909.0  \n",
       "2205633   ENSG00000204475        31588895.0      31593006.0  \n",
       "2186467   ENSG00000204498        31546870.0      31558829.0  \n",
       "2185022   ENSG00000213760        31544462.0      31548427.0  \n",
       "2127794   ENSG00000198563        31530219.0      31542448.0  \n",
       "2123980   ENSG00000204511        31528962.0      31530232.0  \n",
       "2207335   ENSG00000204472        31615217.0      31617021.0  \n",
       "2209271   ENSG00000204469        31620715.0      31637771.0  \n",
       "2245560   ENSG00000204463        31639028.0      31652705.0  \n",
       "2123486   ENSG00000204516        31494881.0      31511124.0  \n",
       "2541096   ENSG00000204444        31652416.0      31658210.0  \n",
       "2541963   ENSG00000204439        31658298.0      31660778.0  \n",
       "2541969   ENSG00000204438        31661228.0      31666283.0  \n",
       "2545890   ENSG00000204435        31665236.0      31670343.0  \n",
       "2549588   ENSG00000263020        31666102.0      31673546.0  \n",
       "2549804   ENSG00000240053        31670167.0      31673776.0  \n",
       "2549902   ENSG00000204428        31676684.0      31684040.0  \n",
       "2551666   ENSG00000204427        31686955.0      31703356.0  \n",
       "2630866   ENSG00000204422        31686962.0      31714072.0  \n",
       "2631351   ENSG00000204424        31706885.0      31710595.0  \n",
       "2631639   ENSG00000250641        31706904.0      31717918.0  \n",
       "2631675   ENSG00000255552        31711771.0      31714065.0  \n",
       "2631917   ENSG00000244355        31715356.0      31717804.0  \n",
       "2633805   ENSG00000204420        31718594.0      31726714.0  \n",
       "2644781   ENSG00000204421        31718648.0      31721746.0  \n",
       "2644928   ENSG00000213722        31727038.0      31730617.0  \n",
       "2648064   ENSG00000213719        31730581.0      31739763.0  \n",
       "...                   ...               ...             ...  \n",
       "26779     ENSG00000204703        29086208.0      29087313.0  \n",
       "26772     ENSG00000204704        29044213.0      29045240.0  \n",
       "26601     ENSG00000197935        28994785.0      29005316.0  \n",
       "26560     ENSG00000204709        28943877.0      28944537.0  \n",
       "4842751   ENSG00000137309        34236873.0      34246231.0  \n",
       "24109     ENSG00000204713        28903002.0      28923988.0  \n",
       "4844009   ENSG00000186577        34246381.0      34249108.0  \n",
       "4847832   ENSG00000272325        34279679.0      34392669.0  \n",
       "4847857   ENSG00000270800        34284887.0      34426071.0  \n",
       "4848118   ENSG00000124614        34417454.0      34426069.0  \n",
       "4851088   ENSG00000124507        34466061.0      34535229.0  \n",
       "4853180   ENSG00000124664        34537802.0      34556333.0  \n",
       "22215     ENSG00000232040        28570535.0      28616212.0  \n",
       "4853423   ENSG00000196821        34587288.0      34696859.0  \n",
       "20771     ENSG00000224586        28525881.0      28534955.0  \n",
       "19091     ENSG00000198704        28503296.0      28528215.0  \n",
       "18852     ENSG00000187987        28431930.0      28443502.0  \n",
       "4854045   ENSG00000124562        34757505.0      34773857.0  \n",
       "17993     ENSG00000158691        28378955.0      28399734.0  \n",
       "16373     ENSG00000189298        28349947.0      28369172.0  \n",
       "4855013   ENSG00000065060        34792015.0      34883138.0  \n",
       "6309      ENSG00000235109        28324693.0      28356271.0  \n",
       "5718      ENSG00000137338        28281572.0      28302549.0  \n",
       "4862410   ENSG00000064995        34877462.0      34888071.0  \n",
       "5116      ENSG00000189134        28259297.0      28260958.0  \n",
       "4816      ENSG00000187626        28241697.0      28252269.0  \n",
       "3024      ENSG00000137185        28224886.0      28233482.0  \n",
       "691       ENSG00000198315        28141883.0      28159460.0  \n",
       "158       ENSG00000196812        28124609.0      28130082.0  \n",
       "11        ENSG00000197279        28080568.0      28089563.0  \n",
       "\n",
       "[207 rows x 33 columns]"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_bed.loc[(gene_bed['Gene start (bp)']-31538497).abs().sort_values().index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'TAP1' in gene_bed['Gene name'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ch6845/tools/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "\"None of [Index(['ENSG00000206337'], dtype='object', name='Gene stable ID')] are in the [index]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-65-1642ad9616a9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mgene_to_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mgene_bed_indexed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mstr\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mcatalog_chr6_short\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'SNP_GENE_IDS'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgene_to_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/tools/miniconda3/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, arg, na_action)\u001b[0m\n\u001b[1;32m   3380\u001b[0m         \"\"\"\n\u001b[1;32m   3381\u001b[0m         new_values = super(Series, self)._map_values(\n\u001b[0;32m-> 3382\u001b[0;31m             arg, na_action=na_action)\n\u001b[0m\u001b[1;32m   3383\u001b[0m         return self._constructor(new_values,\n\u001b[1;32m   3384\u001b[0m                                  index=self.index).__finalize__(self)\n",
      "\u001b[0;32m~/tools/miniconda3/lib/python3.7/site-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m_map_values\u001b[0;34m(self, mapper, na_action)\u001b[0m\n\u001b[1;32m   1216\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1217\u001b[0m         \u001b[0;31m# mapper is a function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1218\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1220\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnew_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m<ipython-input-65-1642ad9616a9>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgene_to_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mgene_bed_indexed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mstr\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mcatalog_chr6_short\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'SNP_GENE_IDS'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgene_to_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/tools/miniconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1498\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1499\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1500\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1501\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1502\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/tools/miniconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1900\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Cannot index with multidimensional key'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1902\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_iterable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1903\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1904\u001b[0m             \u001b[0;31m# nested tuple slicing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/tools/miniconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_iterable\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1203\u001b[0m             \u001b[0;31m# A collection of keys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1204\u001b[0m             keyarr, indexer = self._get_listlike_indexer(key, axis,\n\u001b[0;32m-> 1205\u001b[0;31m                                                          raise_missing=False)\n\u001b[0m\u001b[1;32m   1206\u001b[0m             return self.obj._reindex_with_indexers({axis: [keyarr, indexer]},\n\u001b[1;32m   1207\u001b[0m                                                    copy=True, allow_dups=True)\n",
      "\u001b[0;32m~/tools/miniconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1159\u001b[0m         self._validate_read_indexer(keyarr, indexer,\n\u001b[1;32m   1160\u001b[0m                                     \u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1161\u001b[0;31m                                     raise_missing=raise_missing)\n\u001b[0m\u001b[1;32m   1162\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/tools/miniconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1244\u001b[0m                 raise KeyError(\n\u001b[1;32m   1245\u001b[0m                     u\"None of [{key}] are in the [{axis}]\".format(\n\u001b[0;32m-> 1246\u001b[0;31m                         key=key, axis=self.obj._get_axis_name(axis)))\n\u001b[0m\u001b[1;32m   1247\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1248\u001b[0m             \u001b[0;31m# We (temporarily) allow for some missing keys with .loc, except in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"None of [Index(['ENSG00000206337'], dtype='object', name='Gene stable ID')] are in the [index]\""
     ]
    }
   ],
   "source": [
    "gene_to_name=lambda x: gene_bed_indexed.loc[x.split(',')] if type(x)==str else x\n",
    "\n",
    "catalog_chr6_short['SNP_GENE_IDS'].map(gene_to_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for idx,row in catalog_chr6_short.iterrows():\n",
    "    print(row['DISEASE/TRAIT'],'-----',row['MAPPED_TRAIT'])\n",
    "    \n",
    "    if idx==1000:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"Crohn's disease\", 'Ulcerative colitis',\n",
       "       'Alcohol consumption (transferrin glycosylation)', ...,\n",
       "       'Educational attainment (MTAG)', 'Cognitive performance (MTAG)',\n",
       "       'Self-reported math ability'], dtype=object)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catalog_chr6_short['DISEASE/TRAIT'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"Crohn's disease\", 'ulcerative colitis', 'alcohol drinking', ...,\n",
       "       'chronic kidney disease, APOL1 risk genotype carrier status',\n",
       "       'Microalbuminuria, type I diabetes mellitus',\n",
       "       'response to ketogenic diet, epilepsy'], dtype=object)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catalog_chr6_short['MAPPED_TRAIT'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "catalog_chr6_short[catalog_chr6_short['DOWNSTREAM_GENE_DISTANCE'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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