BICOSS: Bayesian iterative conditional stochastic search for GWAS

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 false discovery rate and allows for smaller effect sizes to be discovered. Finally, two real world applications show the utility and flexibility of BICOSS. Conclusions When compared to widely used SMA, BICOSS provides higher recall of true SNPs while dramatically reducing false discovery rate.

Automated summary

The study presents the use of the Bayesian Iterative Conditional Stochastic Search (BICOSS) method for genome wide association studies, which effectively controls false discovery rate and increases recall of variants with small and medium effect sizes. Two real world applications also demonstrate the utility and flexibility of BICOSS.