A penalized robust semiparametric approach for gene–environment interactions |
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Authors: | Cen Wu Xingjie Shi Yuehua Cui Shuangge Ma |
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Affiliation: | 1. Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, U.S.A.;2. Department of Statistics, Kansas State University, Manhattan, KS, U.S.A.;3. Department of Statistics, Nanjing University of Finance and Economics, Nanjing, China;4. Department of Statistics and Probability, Michigan State University, East Lansing, MI, U.S.A.;5. VA Cooperative Studies Program Coordinating Center, West Haven, CT, U.S.A. |
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Abstract: | In genetic and genomic studies, gene‐environment (G×E) interactions have important implications. Some of the existing G×E interaction methods are limited by analyzing a small number of G factors at a time, by assuming linear effects of E factors, by assuming no data contamination, and by adopting ineffective selection techniques. In this study, we propose a new approach for identifying important G×E interactions. It jointly models the effects of all E and G factors and their interactions. A partially linear varying coefficient model is adopted to accommodate possible nonlinear effects of E factors. A rank‐based loss function is used to accommodate possible data contamination. Penalization, which has been extensively used with high‐dimensional data, is adopted for selection. The proposed penalized estimation approach can automatically determine if a G factor has an interaction with an E factor, main effect but not interaction, or no effect at all. The proposed approach can be effectively realized using a coordinate descent algorithm. Simulation shows that it has satisfactory performance and outperforms several competing alternatives. The proposed approach is used to analyze a lung cancer study with gene expression measurements and clinical variables. Copyright © 2015 John Wiley & Sons, Ltd. |
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Keywords: | gene– environment interactions robustness partially linear varying coefficient model penalized selection |
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