B1264
Title: Iterative empirical Bayes for GWAS
Authors: Jacob Williams - Virginia Tech (United States)
Shuangshuang Xu - Virginia Tech (United States) [presenting]
Marco Ferreira - Virginia Tech (United States)
Abstract: Genome-wide association studies (GWAS) are popular for identifying causal loci for observed phenotypes. Common GWAS procedures, single marker association testing (SMA), identify causal loci by investigating the effect of single nucleotide polymorphisms (SNPs). However, SMA ignores the highly correlated structure of the SNPs themselves by investigating the effect of each SNP individually. Thus, SMA suffers from a high false discovery rate (FDR), ultimately leading to muddled results. A novel Iterative Empirical Bayes (IEB) method is proposed for more precise GWAS results. IEB successfully identifies the causal SNPs by iterating between a screening step and a model selection step. An extensive simulation study shows that, compared to popular SMA methods, IEB achieves a high recall of true causal SNPs while dramatically decreasing FDR. We illustrate the application of IEB with two case studies on plant science and human health.
