<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Hoppmann AS</submitter><funding>Bundesministerium für Bildung und Forschung</funding><funding>Deutsche Forschungsgemeinschaft</funding><funding>Seventh Framework Programme</funding><pagination>e0162466</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC5017755</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>11(9)</volume><pubmed_abstract>Genome-wide association studies (GWAS) evaluate associations between genetic variants and a trait or disease of interest free of prior biological hypotheses. GWAS require stringent correction for multiple testing, with genome-wide significance typically defined as association p-value &lt;5*10-8. This study presents a new tool that uses external information about genes to prioritize SNP associations (GenToS). For a given list of candidate genes, GenToS calculates an appropriate statistical significance threshold and then searches for trait-associated variants in summary statistics from human GWAS. It thereby allows for identifying trait-associated genetic variants that do not meet genome-wide significance. The program additionally tests for enrichment of significant candidate gene associations</pubmed_abstract><journal>PloS one</journal><pubmed_title>GenToS: Use of Orthologous Gene Information to Prioritize Signals from Human GWAS.</pubmed_title><pmcid>PMC5017755</pmcid><funding_grant_id>SFB 1140</funding_grant_id><funding_grant_id>SFB 992</funding_grant_id><funding_grant_id>KO 3598/3-1</funding_grant_id><funding_grant_id>602300</funding_grant_id><funding_grant_id>031 A538A</funding_grant_id><funding_grant_id>FACE</funding_grant_id><pubmed_authors>Lausch E</pubmed_authors><pubmed_authors>Kottgen A</pubmed_authors><pubmed_authors>Schlosser P</pubmed_authors><pubmed_authors>Backofen R</pubmed_authors><pubmed_authors>Hoppmann AS</pubmed_authors></additional><is_claimable>false</is_claimable><name>GenToS: Use of Orthologous Gene Information to Prioritize Signals from Human GWAS.</name><description>Genome-wide association studies (GWAS) evaluate associations between genetic variants and a trait or disease of interest free of prior biological hypotheses. GWAS require stringent correction for multiple testing, with genome-wide significance typically defined as association p-value &lt;5*10-8. This study presents a new tool that uses external information about genes to prioritize SNP associations (GenToS). For a given list of candidate genes, GenToS calculates an appropriate statistical significance threshold and then searches for trait-associated variants in summary statistics from human GWAS. It thereby allows for identifying trait-associated genetic variants that do not meet genome-wide significance. The program additionally tests for enrichment of significant candidate gene associations</description><dates><release>2016-01-01T00:00:00Z</release><publication>2016</publication><modification>2026-05-04T15:07:23.823Z</modification><creation>2019-03-26T22:48:06Z</creation></dates><accession>S-EPMC5017755</accession><cross_references><pubmed>27612175</pubmed><doi>10.1371/journal.pone.0162466</doi></cross_references></HashMap>