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Abstract:
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In high-throughput cancer studies, an important aim is to identify gene-environment interactions associated with the clinical outcomes. Recently, multiple marginal penalization methods have been developed and shown to be effective in G×E studies. However, within the Bayesian framework, marginal variable selection has not received much attention. In this study, we propose a novel marginal Bayesian variable selection method for G×E studies. In particular, our marginal Bayesian method is robust to data contamination and outliers in the outcome variables. With the incorporation of spike-and-slab priors, the proposed method outperforms a number of alternatives in both identification and prediction in extensive simulation studies. The utility of the marginal robust Bayesian variable selection method has been further demonstrated in the case studies using TCGA data. Some of the identified main and interaction effects from the real data analysis have important biological implications.
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