Semiparametric Bayesian variable selection for gene-environment interactions.

ABSTRACT: Many complex diseases are known to be affected by the interactions between genetic variants and environmental exposures beyond the main genetic and environmental effects. Study of gene-environment (G×E) interactions is important for elucidating the disease etiology. Existing Bayesian methods for G×E interaction studies are challenged by the high-dimensional nature of the study and the complexity of environmental influences. Many studies have shown the advantages of penalization methods in detecting G×E interactions in "large p, small n" settings. However, Bayesian variable selection, which can provide fresh insight into G×E study, has not been widely examined. We propose a novel and powerful semiparametric Bayesian variable selection model that can investigate linear and nonlinear G×E inte

SUBMITTER: Ren J 

PROVIDER: S-EPMC7467082 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature