Robust Bayesian Bi-level Selection for Gene-Environment Interactions in Longitudinal Studies

Music City Center 

Identifying important gene-environment (G×E) interactions in high-dimensional longitudinal studies poses unique challenges in the presence of long-tailed distributions or outliers in clinical outcomes. Robust Bayesian variable selection methods have been recently shown to effectively address outliers and outcome skewness in G×E studies. However, their potential for accommodating structured sparsity in longitudinal settings has not been fully investigated. In this study, we develop a novel robust Bayesian mixed-effects model for bi-level G×E interaction analysis in longitudinal studies. The proposed method performs effective sparse group selection for main and interaction effects through structured spike-and-slab priors, while accounting for within-subject correlations. To facilitate fast computation and reliable posterior inference, we develop efficient Gibbs samplers and MCMC algorithms. The superior performance of the proposed method in variable selection, estimation, and statistical inference, compared to existing approaches, is demonstrated through extensive simulation studies and applications to longitudinal cohorts with high-dimensional G×E interactions.

Robust Bayesian mixed-effects model

Sparse group selection

Longitudinal studies

Gene-environment interaction

Spike-and-slab priors 

Section on Statistics in Genomics and Genetics