Robust Bayesian variable selection for gene-environment interactions

TL;DR

This paper introduces a Bayesian method for gene-environment interaction studies that robustly handles outliers and heavy-tailed errors, improving variable selection accuracy in complex disease research.

Contribution

It develops a fully Bayesian robust variable selection approach using spike-and-slab priors for G×E studies, addressing data contamination issues not handled by existing methods.

Findings

Outperforms existing methods in simulations.

Effectively identifies important genetic and environmental factors.

Demonstrates robustness on real disease datasets.

Abstract

Gene-environment (G$\times$E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G$\times$E studies have been commonly encountered, leading to the development of a broad spectrum of robust regularization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a fully Bayesian robust variable selection method for G$\times$E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, for the robust sparse group selection, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects robustly. An efficient Gibbs sampler has been developed to facilitate fast computation. Extensive simulation studies and analysis of both the diabetes data with SNP measurements from the Nurses' Health Study and TCGA melanoma data with gene expression measurements demonstrate the superior performance of the proposed method over multiple competing alternatives.