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 interactions simultaneously. Furthermore, the proposed method can conduct structural identification by distinguishing nonlinear interactions from main-effects-only case within the Bayesian framework.Spike-and-slab priors are incorporated on both individual and group levels to identify the sparse main and interaction effects. The proposed method conducts Bayesian variable selection more efficiently than existing methods. Simulation shows that the proposed model outperforms competing alternatives in terms of both identification and prediction. The proposed Bayesian method leads to the identification of main and interaction effects with important implications in a high-throughput profiling study with high-dimensional SNP data.

Bayesian variable selection for understanding mixtures in environmental exposures

“…The next stage in our methodological development is to extend the decision analysis selection approach for (i) all pairwise interactions and (ii) nonlinear models. While our focus is restricted to interactions between social stressors and environmental exposures, other analyses may require consideration of the complete collection of pairwise interactions 56,57 . The proposed model‐based penalization strategy () offers a path forward and welcomes hierarchical or interaction‐sparsity penalties 58 —as well as the accompanying estimation algorithms—into our decision analysis framework.…”

A general index for linear and nonlinear correlations for high dimensional genomic data

“…As far as we know, Wu et al have proposed many effective methods such as semiparametric bayesian variable selection, additive varying-coefficient model, penalized robust semiparametric approach, etc. in dealing with gene-environment interactions [26][27][28][29]. Here, we offer two ideas of combining environmental covariates with genetic data when inferring regulations from the perspective of a matrix.…”

Penalized Variable Selection for Lipid–Environment Interactions in a Longitudinal Lipidomics Study

“…First, as data contamination and outliers have been widely observed in repeated measurements, robust variable selection methods in G × E interaction studies [23,[50][51][52] can be extended to longitudinal settings. Second, recently, multiple Bayesian methods have been proposed for pinpointing important G × E interaction effects [53][54][55]. Within the framework of analyzing repeated measurements, Bayesian variable selection for interactions has not been extensively examined.…”

“…Our methods can be readily adopted to conduct marginal identification of interaction effects when the phenotypes are repeatedly measured. In addition, robust variable selection for G×E interactions have been proposed [51][52][53] . In longitudinal G×E studies, the robustness of QIF framework to data contamination in the response can be potentially improved by modifying the weight in estimating equation to downweigh the influences of outliers.…”