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  "Title": "Robust Bayesian Variable Selection for Gene-Environment\nInteractions",
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  "Authors@R": "c(person(given = \"Jie\",\nfamily = \"Ren\",\nrole = c(\"aut\", \"cre\"),\nemail = \"renjie0910@gmail.com\"),\nperson(given = \"Fei\",\nfamily = \"Zhou\",\nrole = \"aut\"),\nperson(given = \"Xiaoxi\",\nfamily = \"Li\",\nrole = \"aut\"),\nperson(given = \"Cen\",\nfamily = \"Wu\",\nrole = \"aut\"))",
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  "Description": "Gene-environment (G×E) interactions have important\nimplications to elucidate the etiology of complex diseases\nbeyond the main genetic and environmental effects. Outliers and\ndata contamination in disease phenotypes of G×E studies have\nbeen commonly encountered, leading to the development of a\nbroad spectrum of robust penalization methods. Nevertheless,\nwithin the Bayesian framework, the issue has not been taken\ncare of in existing studies. We develop a robust Bayesian\nvariable selection method for G×E interaction studies. The\nproposed Bayesian method can effectively accommodate\nheavy-tailed errors and outliers in the response variable while\nconducting variable selection by accounting for structural\nsparsity. In particular, the spike-and-slab priors have been\nimposed on both individual and group levels to identify\nimportant main and interaction effects. An efficient Gibbs\nsampler has been developed to facilitate fast computation. The\nMarkov chain Monte Carlo algorithms of the proposed and\nalternative methods are efficiently implemented in C++.",
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