Using Bayes model averaging to leverage both gene main effects and G ×  E interactions to identify genomic regions in genome-wide association studies.

ABSTRACT: Genome-wide association studies typically search for marginal associations between a single-nucleotide polymorphism (SNP) and a disease trait while gene-environment (G × E) interactions remain generally unexplored. More powerful methods beyond the simple case-control (CC) approach leverage either marginal effects or CC ascertainment to increase power. However, these potential gains depend on assumptions whose aptness is often unclear a priori. Here, we review G × E methods and use simulations to highlight performance as a function of main and interaction effects and the association of the two factors in the source population. Substantial variation in performance between methods leads to uncertainty as to which approach is most appropriate for any given analysis. We present a framework that

SUBMITTER: Moss LC 

PROVIDER: S-EPMC6375769 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature