spinBayes: Semi-Parametric Gene-Environment Interaction via Bayesian
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) <arXiv:1906.01057>). 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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