{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Williams J"],"funding":["National Science Foundation"],"pagination":["475"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9652902"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["23(1)"],"pubmed_abstract":["<h4>Background</h4>Single marker analysis (SMA) with linear mixed models for genome wide association studies has uncovered the contribution of genetic variants to many observed phenotypes. However, SMA has weak false discovery control. In addition, when a few variants have large effect sizes, SMA has low statistical power to detect small and medium effect sizes, leading to low recall of true causal single nucleotide polymorphisms (SNPs).<h4>Results</h4>We present the Bayesian Iterative Conditional Stochastic Search (BICOSS) method that controls false discovery rate and increases recall of variants with small and medium effect sizes. BICOSS iterates between a screening step and a Bayesian model selection step. A simulation study shows that, when compared to SMA, BICOSS dramatically reduces "],"journal":["BMC bioinformatics"],"pubmed_title":["BICOSS: Bayesian iterative conditional stochastic search for GWAS."],"pmcid":["PMC9652902"],"funding_grant_id":["DMS 1853549","DMS 1853556"],"pubmed_authors":["Ji T","Williams J","Ferreira MAR"],"additional_accession":[]},"is_claimable":false,"name":"BICOSS: Bayesian iterative conditional stochastic search for GWAS.","description":"<h4>Background</h4>Single marker analysis (SMA) with linear mixed models for genome wide association studies has uncovered the contribution of genetic variants to many observed phenotypes. However, SMA has weak false discovery control. In addition, when a few variants have large effect sizes, SMA has low statistical power to detect small and medium effect sizes, leading to low recall of true causal single nucleotide polymorphisms (SNPs).<h4>Results</h4>We present the Bayesian Iterative Conditional Stochastic Search (BICOSS) method that controls false discovery rate and increases recall of variants with small and medium effect sizes. BICOSS iterates between a screening step and a Bayesian model selection step. A simulation study shows that, when compared to SMA, BICOSS dramatically reduces ","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Nov","modification":"2025-04-05T15:41:36.534Z","creation":"2025-04-05T15:41:36.534Z"},"accession":"S-EPMC9652902","cross_references":{"pubmed":["36371147"],"doi":["10.1186/s12859-022-05030-0"]}}