<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Zhang Z</submitter><funding>NCI NIH HHS</funding><funding>National Institutes of Health</funding><funding>Cancer Prevention Research Institute of Texas</funding><pagination>146-155</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC8682785</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>31(1)</volume><pubmed_abstract>Genotype imputation is widely used in genetic studies to boost the power of GWAS, to combine multiple studies for meta-analysis and to perform fine mapping. With advances of imputation tools and large reference panels, genotype imputation has become mature and accurate. However, the uncertain nature of imputed genotypes can cause bias in the downstream analysis. Many studies have compared the performance of popular imputation approaches, but few investigated bias characteristics of downstream association analyses. Herein, we showed that the imputation accuracy is diminished if the real genotypes contain minor alleles. Although these genotypes are less common, which is particularly true for loci with low minor allele frequency, a large discordance between imputed and observed genotypes sign</pubmed_abstract><journal>Human molecular genetics</journal><pubmed_title>False positive findings during genome-wide association studies with imputation: influence of allele frequency and imputation accuracy.</pubmed_title><pmcid>PMC8682785</pmcid><funding_grant_id>R01CA242218</funding_grant_id><funding_grant_id>RR170048</funding_grant_id><funding_grant_id>U19CA203654</funding_grant_id><funding_grant_id>R03 CA256222</funding_grant_id><funding_grant_id>R01 CA242218</funding_grant_id><funding_grant_id>U19 CA203654</funding_grant_id><pubmed_authors>Amos CI</pubmed_authors><pubmed_authors>Zhang Z</pubmed_authors><pubmed_authors>Xiao X</pubmed_authors><pubmed_authors>Zhou W</pubmed_authors><pubmed_authors>Zhu D</pubmed_authors></additional><is_claimable>false</is_claimable><name>False positive findings during genome-wide association studies with imputation: influence of allele frequency and imputation accuracy.</name><description>Genotype imputation is widely used in genetic studies to boost the power of GWAS, to combine multiple studies for meta-analysis and to perform fine mapping. With advances of imputation tools and large reference panels, genotype imputation has become mature and accurate. However, the uncertain nature of imputed genotypes can cause bias in the downstream analysis. Many studies have compared the performance of popular imputation approaches, but few investigated bias characteristics of downstream association analyses. Herein, we showed that the imputation accuracy is diminished if the real genotypes contain minor alleles. Although these genotypes are less common, which is particularly true for loci with low minor allele frequency, a large discordance between imputed and observed genotypes sign</description><dates><release>2021-01-01T00:00:00Z</release><publication>2021 Dec</publication><modification>2025-04-05T16:10:47.14Z</modification><creation>2025-04-05T16:10:47.14Z</creation></dates><accession>S-EPMC8682785</accession><cross_references><pubmed>34368847</pubmed><doi>10.1093/hmg/ddab203</doi></cross_references></HashMap>