Semi-Parametric Gene-Environment Interaction via Bayesian Variable Selection

Many complex diseases are known to be affected by the interactions between genetic variants and environmental exposures beyond the main genetic and environmental effects. Existing Bayesian methods for gene-environment (G×E) interaction studies are challenged by the high-dimensional nature of the study and the complexity of environmental influences. We have developed a novel and powerful semi-parametric Bayesian variable selection method that can accommodate linear and nonlinear G×E interactions simultaneously (Ren et al. (2019) ). Furthermore, the proposed method can conduct structural identification by distinguishing nonlinear interactions from main effects only case within Bayesian framework. Spike-and-slab priors are incorporated on both individual and group level to shrink coefficients corresponding to irrelevant main and interaction effects to zero exactly. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++.


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0.1.0 by Jie Ren, 2 years ago

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Authors: Jie Ren , Fei Zhou , Xiaoxi Li , Cen Wu , Yu Jiang

Documentation:   PDF Manual  

GPL-2 license

Imports Rcpp, splines, MASS, glmnet, utils

Linking to Rcpp, RcppArmadillo

See at CRAN