{ "_id": "6a1eef8ab401979e73412be4", "Package": "spinBayes", "Type": "Package", "Title": "Semi-Parametric Gene-Environment Interaction via Bayesian\nVariable Selection", "Version": "0.2.2", "Authors@R": "c(\nperson(given = \"Jie\", family = \"Ren\", role = c(\"aut\", \"cre\"),\nemail = \"renjie0910@gmail.com\"),\nperson(given = \"Fei\", family = \"Zhou\", role = \"aut\"),\nperson(given = \"Xiaoxi\", family = \"Li\", role = \"aut\"),\nperson(given = \"Cen\", family = \"Wu\", role = \"aut\"),\nperson(given = \"Yu\", family = \"Jiang\", role = \"aut\"))", "Maintainer": "Jie Ren ", "Description": "Many complex diseases are known to be affected by the\ninteractions between genetic variants and environmental\nexposures beyond the main genetic and environmental effects.\nExisting Bayesian methods for gene-environment (G×E)\ninteraction studies are challenged by the high-dimensional\nnature of the study and the complexity of environmental\ninfluences. We have developed a novel and powerful\nsemi-parametric Bayesian variable selection method that can\naccommodate linear and nonlinear G×E interactions\nsimultaneously (Ren et al. (2020) ).\nFurthermore, the proposed method can conduct structural\nidentification by distinguishing nonlinear interactions from\nmain effects only case within Bayesian framework.\nSpike-and-slab priors are incorporated on both individual and\ngroup level to shrink coefficients corresponding to irrelevant\nmain and interaction effects to zero exactly. 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