BICOSS: Bayesian iterative conditional stochastic search for GWAS.

ABSTRACT:

Background

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).

Results

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

SUBMITTER: Williams J 

PROVIDER: S-EPMC9652902 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature