Identifying Gene-Environment Interactions With Robust Marginal Bayesian Variable Selection.

ABSTRACT: In high-throughput genetics studies, an important aim is to identify gene-environment interactions associated with the clinical outcomes. Recently, multiple marginal penalization methods have been developed and shown to be effective in G×E studies. However, within the Bayesian framework, marginal variable selection has not received much attention. In this study, we propose a novel marginal Bayesian variable selection method for G×E studies. In particular, our marginal Bayesian method is robust to data contamination and outliers in the outcome variables. With the incorporation of spike-and-slab priors, we have implemented the Gibbs sampler based on Markov Chain Monte Carlo (MCMC). The proposed method outperforms a number of alternatives in extensive simulation studies. The utility of the ma

SUBMITTER: Lu X 

PROVIDER: S-EPMC8693717 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature