Using Bayes model averaging to leverage both gene main effects and G × E interactions to identify genomic regions in genome‐wide association studies
Year: 2018
Type: article
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 (a) balances the robustness of a CC approach with the power of the case-only (CO) approach; (b) incorporates main SNP effects; (c) allows for incorporation of prior information; and (d) allows the data to determine the most appropriate model. Our framework is based on Bayes model averaging, which provides a principled statistical method for incorporating model uncertainty. We average over inclusion of parameters corresponding to the main and G × E interaction effects and the G-E association in controls. The resulting method exploits the joint evidence for main and interaction effects while gaining power from a CO equivalent analysis. Through simulations, we demonstrate that our approach detects SNPs within a wide range of scenarios with increased power over current methods. We illustrate the approach on a gene-environment scan in the USC Children's Health Study.
Source: Genetic Epidemiology
DOI: 10.1002/gepi.22171
Institution: University of Southern California
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Awards: Imputation-based approach to identify low frequency variants in prostate cancer, G129235184, Environmental Genomics, G4595396146, HIERARCHICAL MODELING OF INTERACTIONS IN GENOME-WIDE AND PATHWAY-BASED STUDIES, Incorporating intermediate biomarkers of folate with colorectal cancer, Gene-Environment Analyses of Early Life Exposures and Asthma in Ethnically Diverse Children, G5576693751, Using functional genomics to inform gene environment interactions for colorectal cancer, G6553752722, G6649700411, Environmental Exposures, Host Factors and Human Disease, Statistical Methods for Integrative Genomics in Cancer
OpenAlex ID: W2901031256