title: A penalized robust semiparametric approach for gene-environment interactions.
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.
authors.affiliation: Department of Biostatistics, School of Public Health, Yale University, 60 College Street, New Haven, CT, 06520, U.S.A.|Department of Statistics, Kansas State University, 1116 Mid-Campus Drive N., Manhattan, KS, 66506, U.S.A.
authors.forename: Cen
authors.initials: C
authors.lastname: Wu
authors.affiliation: Department of Statistics, Nanjing University of Finance and Economics, Nanjing, China.
authors.forename: Xingjie
authors.initials: X
authors.lastname: Shi
authors.affiliation: Department of Statistics and Probability, Michigan State University, 619 Red Cedar Rd, East Lansing, MI, 48824, U.S.A.
authors.forename: Yuehua
authors.initials: Y
authors.lastname: Cui
authors.affiliation: Department of Biostatistics, School of Public Health, Yale University, 60 College Street, New Haven, CT, 06520, U.S.A.|VA Cooperative Studies Program Coordinating Center, West Haven, CT, 06516, U.S.A.
authors.forename: Shuangge
authors.initials: S
authors.lastname: Ma
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics; D056808:Biostatistics; D003198:Computer Simulation; D030541:Databases, Genetic; D005260:Female; D015870:Gene Expression; D059647:Gene-Environment Interaction; D006801:Humans; D016014:Linear Models; D008175:Lung Neoplasms / Q000209:etiology / Q000235:genetics; D008297:Male; D008957:Models, Genetic; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide
chemical_list: D014408:Biomarkers, Tumor
keywords: gene-environment interactions; partially linear varying coefficient model; penalized selection; robustness