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Abstract:
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Many complex diseases, such as cancer, are known to be affected by the interactions between genetic variants and environmental exposures beyond the main effects. Study of gene–environment (GxE) interactions is important for elucidating the disease etiology. Existing methods for GxE analysis are challenged by the high-dimensional nature of genomic data and the complexity of environmental influences. Many studies have shown the advantages of penalization methods in detecting GxE interactions in “large p, small n” settings. However, Bayesian variable selection, which can provide fresh insight into GxE study, has not been widely examined. We propose a novel and powerful semi-parametric Bayesian variable selection model that can investigate linear and nonlinear GxE interactions simultaneously. 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 effects with important implications in a high-throughput profiling study with high-dimensional genetic variants.
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