Bayesian semiparametric analysis for two-phase studies of gene-environment interaction

TL;DR

This paper develops a Bayesian semiparametric framework for analyzing gene-environment interactions in two-phase studies, addressing high-dimensional data, variable selection, and leveraging independence assumptions to improve estimation efficiency.

Contribution

It introduces a Bayesian approach combining variable selection, hierarchical priors, and nonparametric modeling for complex gene-environment interaction analysis in two-phase studies.

Findings

Effective variable selection for high-dimensional models.

Improved estimation of interaction parameters using independence assumptions.

Flexible modeling of joint distributions with nonparametric Bayes methods.

Abstract

The two-phase sampling design is a cost-efficient way of collecting expensive covariate information on a judiciously selected subsample. It is natural to apply such a strategy for collecting genetic data in a subsample enriched for exposure to environmental factors for gene-environment interaction (G x E) analysis. In this paper, we consider two-phase studies of G x E interaction where phase I data are available on exposure, covariates and disease status. Stratified sampling is done to prioritize individuals for genotyping at phase II conditional on disease and exposure. We consider a Bayesian analysis based on the joint retrospective likelihood of phases I and II data. We address several important statistical issues: (i) we consider a model with multiple genes, environmental factors and their pairwise interactions. We employ a Bayesian variable selection algorithm to reduce the dimensionality of this potentially high-dimensional model; (ii) we use the assumption of gene-gene and gene-environment independence to trade off between bias and efficiency for estimating the interaction parameters through use of hierarchical priors reflecting this assumption; (iii) we posit a flexible model for the joint distribution of the phase I categorical variables using the nonparametric Bayes construction of Dunson and Xing [J. Amer. Statist. Assoc. 104 (2009) 1042-1051].