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diff --git a/Population_Genetics_Selection_README/index.html b/Population_Genetics_Selection_README/index.html
@@ -2345,7 +2345,7 @@ <h2 id="summary-table">Summary Table</h2>
 <td style="text-align: right;">2005</td>
 </tr>
 <tr>
-<td style="text-align: left;"><a href="#reviewholsingerke">Review-Holsinger_KE</a></td>
+<td style="text-align: left;"><a href="#review-holsinger-ke">Review-Holsinger_KE</a></td>
 <td style="text-align: left;">Holsinger KE, Weir BS. (2009) Genetics in geographically structured populations: defining, estimating and interpreting F(ST). Nat Rev Genet, 10 (9) 639-50. doi:10.1038/nrg2611. PMID 19687804</td>
 <td style="text-align: right;">2009</td>
 </tr>
@@ -2355,7 +2355,7 @@ <h2 id="summary-table">Summary Table</h2>
 <td style="text-align: right;">2016</td>
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-<td style="text-align: left;"><a href="#xpehh">XP-EHH</a></td>
+<td style="text-align: left;"><a href="#xp-ehh">XP-EHH</a></td>
 <td style="text-align: left;">Klassmann A, Gautier M. (2022) Detecting selection using extended haplotype homozygosity (EHH)-based statistics in unphased or unpolarized data. PLoS One, 17 (1) e0262024. doi:10.1371/journal.pone.0262024. PMID 35041674</td>
 <td style="text-align: right;">2022</td>
 </tr>

diff --git a/Sumstats_GWAS_with_proteomics_README/index.html b/Sumstats_GWAS_with_proteomics_README/index.html
diff --git a/Sumstats_README/index.html b/Sumstats_README/index.html
@@ -3084,7 +3084,7 @@ <h2 id="summary-table">Summary Table</h2>
 <td style="text-align: left;">EUR</td>
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-<td style="text-align: left;"><a href="#china-kadoorie-biobank-ckb">China Kadoorie Biobank (CKB)</a></td>
+<td style="text-align: left;"><a href="#china-kadoorie-biobank-ckb-">China Kadoorie Biobank (CKB)</a></td>
 <td style="text-align: left;">Biobanks_Cohorts</td>
 <td style="text-align: left;">EAS</td>
 </tr>
@@ -3094,47 +3094,47 @@ <h2 id="summary-table">Summary Table</h2>
 <td style="text-align: left;">EUR</td>
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-<td style="text-align: left;"><a href="#finngenukbb-metaanalysis">FinnGen-UKBB meta-analysis</a></td>
+<td style="text-align: left;"><a href="#finngen-ukbb-meta-analysis">FinnGen-UKBB meta-analysis</a></td>
 <td style="text-align: left;">Biobanks_Cohorts</td>
 <td style="text-align: left;">EUR</td>
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-<td style="text-align: left;"><a href="#finngen-10-december-18-2023">Finngen 10 (December 18 2023)</a></td>
+<td style="text-align: left;"><a href="#finngen-10-december-18-2023-">Finngen 10 (December 18 2023)</a></td>
 <td style="text-align: left;">Biobanks_Cohorts</td>
 <td style="text-align: left;">EUR</td>
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-<td style="text-align: left;"><a href="#finngen-11-june-24-2024">Finngen 11 (June 24 2024)</a></td>
+<td style="text-align: left;"><a href="#finngen-11-june-24-2024-">Finngen 11 (June 24 2024)</a></td>
 <td style="text-align: left;">Biobanks_Cohorts</td>
 <td style="text-align: left;">EUR</td>
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-<td style="text-align: left;"><a href="#finngen-4-november-30-2020">Finngen 4 (November 30 2020)</a></td>
+<td style="text-align: left;"><a href="#finngen-4-november-30-2020-">Finngen 4 (November 30 2020)</a></td>
 <td style="text-align: left;">Biobanks_Cohorts</td>
 <td style="text-align: left;">EUR</td>
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-<td style="text-align: left;"><a href="#finngen-5-may-11-2021">Finngen 5 (May 11 2021)</a></td>
+<td style="text-align: left;"><a href="#finngen-5-may-11-2021-">Finngen 5 (May 11 2021)</a></td>
 <td style="text-align: left;">Biobanks_Cohorts</td>
 <td style="text-align: left;">EUR</td>
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-<td style="text-align: left;"><a href="#finngen-6-january-24-2022">Finngen 6 (January 24 2022)</a></td>
+<td style="text-align: left;"><a href="#finngen-6-january-24-2022-">Finngen 6 (January 24 2022)</a></td>
 <td style="text-align: left;">Biobanks_Cohorts</td>
 <td style="text-align: left;">EUR</td>
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-<td style="text-align: left;"><a href="#finngen-7-june-1-2022">Finngen 7 (June 1 2022)</a></td>
+<td style="text-align: left;"><a href="#finngen-7-june-1-2022-">Finngen 7 (June 1 2022)</a></td>
 <td style="text-align: left;">Biobanks_Cohorts</td>
 <td style="text-align: left;">EUR</td>
 </tr>
 <tr>
-<td style="text-align: left;"><a href="#finngen-8-dec-1-2022">Finngen 8 (Dec 1 2022)</a></td>
+<td style="text-align: left;"><a href="#finngen-8-dec-1-2022-">Finngen 8 (Dec 1 2022)</a></td>
 <td style="text-align: left;">Biobanks_Cohorts</td>
 <td style="text-align: left;">EUR</td>
 </tr>
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-<td style="text-align: left;"><a href="#finngen-9-may-11-2023">Finngen 9 (May 11 2023)</a></td>
+<td style="text-align: left;"><a href="#finngen-9-may-11-2023-">Finngen 9 (May 11 2023)</a></td>
 <td style="text-align: left;">Biobanks_Cohorts</td>
 <td style="text-align: left;">EUR</td>
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@@ -3174,7 +3174,7 @@ <h2 id="summary-table">Summary Table</h2>
 <td style="text-align: left;">EUR</td>
 </tr>
 <tr>
-<td style="text-align: left;"><a href="#panukb">Pan-UKB</a></td>
+<td style="text-align: left;"><a href="#pan-ukb">Pan-UKB</a></td>
 <td style="text-align: left;">Biobanks_Cohorts</td>
 <td style="text-align: left;">EUR</td>
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@@ -3204,7 +3204,7 @@ <h2 id="summary-table">Summary Table</h2>
 <td style="text-align: left;">EUR</td>
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-<td style="text-align: left;"><a href="#ukb-fastgwaglmm">UKB fastgwa-glmm</a></td>
+<td style="text-align: left;"><a href="#ukb-fastgwa-glmm">UKB fastgwa-glmm</a></td>
 <td style="text-align: left;">Biobanks_Cohorts</td>
 <td style="text-align: left;">EUR</td>
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@@ -3214,7 +3214,7 @@ <h2 id="summary-table">Summary Table</h2>
 <td style="text-align: left;">EUR</td>
 </tr>
 <tr>
-<td style="text-align: left;"><a href="#ukb-genebased-genebass">UKB gene-based (Genebass)</a></td>
+<td style="text-align: left;"><a href="#ukb-gene-based-genebass-">UKB gene-based (Genebass)</a></td>
 <td style="text-align: left;">Biobanks_Cohorts</td>
 <td style="text-align: left;">EUR</td>
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@@ -3229,12 +3229,12 @@ <h2 id="summary-table">Summary Table</h2>
 <td style="text-align: left;">NA</td>
 </tr>
 <tr>
-<td style="text-align: left;"><a href="#giant-genetic-investigation-of-anthropometric-traits">GIANT (Genetic Investigation of ANthropometric Traits)</a></td>
+<td style="text-align: left;"><a href="#giant-genetic-investigation-of-anthropometric-traits-">GIANT (Genetic Investigation of ANthropometric Traits)</a></td>
 <td style="text-align: left;">Consortiums</td>
 <td style="text-align: left;">NA</td>
 </tr>
 <tr>
-<td style="text-align: left;"><a href="#glgc-global-lipids-genetics-consortium">GLGC (Global Lipids Genetics Consortium)</a></td>
+<td style="text-align: left;"><a href="#glgc-global-lipids-genetics-consortium-">GLGC (Global Lipids Genetics Consortium)</a></td>
 <td style="text-align: left;">Consortiums</td>
 <td style="text-align: left;">NA</td>
 </tr>
@@ -3244,7 +3244,7 @@ <h2 id="summary-table">Summary Table</h2>
 <td style="text-align: left;">NA</td>
 </tr>
 <tr>
-<td style="text-align: left;"><a href="#pgc-psychiatric-genomics-consortium">PGC (Psychiatric Genomics Consortium)</a></td>
+<td style="text-align: left;"><a href="#pgc-psychiatric-genomics-consortium-">PGC (Psychiatric Genomics Consortium)</a></td>
 <td style="text-align: left;">Consortiums</td>
 <td style="text-align: left;">NA</td>
 </tr>

diff --git a/Tools_Annotation_README/index.html b/Tools_Annotation_README/index.html
@@ -2338,6 +2338,7 @@ <h2 id="loftee">loftee</h2>
 <li><strong>URL</strong> : <a href="https://github.com/konradjk/loftee">https://github.com/konradjk/loftee</a> </li>
 <li><strong>TITLE</strong> : The mutational constraint spectrum quantified from variation in 141,456 humans. </li>
 <li><strong>CITATION</strong> : Karczewski KJ, ...&amp;, MacArthur DG. (2020) The mutational constraint spectrum quantified from variation in 141,456 humans. Nature, 581 (7809) 434-443. doi:10.1038/s41586-020-2308-7. PMID 32461654 </li>
+<li><strong>MESH_MAJOR</strong> :  </li>
 <li><strong>MESH_MINOR</strong> : Adult, Brain, Cardiovascular Diseases, Cohort Studies, Databases, Genetic, Exome, Exome Sequencing, Female, Genes, Essential, Genetic Predisposition to Disease, Genetic Variation, Genome, Human, Genome-Wide Association Study, Humans, Loss of Function Mutation, Male, Mutation Rate, Proprotein Convertase 9, RNA, Messenger, Reproducibility of Results, Whole Genome Sequencing </li>
 <li><strong>ABSTRACT</strong> : Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes<sup>1</sup>. Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases. </li>
 <li><strong>DOI</strong> : 10.1038/s41586-020-2308-7 </li>