title: A penalized robust method for identifying gene-environment interactions.
abstract: In high-throughput studies, an important objective is to identify gene-environment interactions associated with disease outcomes and phenotypes. Many commonly adopted methods assume specific parametric or semiparametric models, which may be subject to model misspecification. In addition, they usually use significance level as the criterion for selecting important interactions. In this study, we adopt the rank-based estimation, which is much less sensitive to model specification than some of the existing methods and includes several commonly encountered data and models as special cases. Penalization is adopted for the identification of gene-environment interactions. It achieves simultaneous estimation and identification and does not rely on significance level. For computation feasibility, a smoothed rank estimation is further proposed. Simulation shows that under certain scenarios, for example, with contaminated or heavy-tailed data, the proposed method can significantly outperform the existing alternatives with more accurate identification. We analyze a lung cancer prognosis study with gene expression measurements under the AFT (accelerated failure time) model. The proposed method identifies interactions different from those using the alternatives. Some of the identified genes have important implications.
authors.affiliation: School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China; Department of Biostatistics, School of Public Health, Yale University, New Haven, Connecticut, United States of America.
authors.forename: Xingjie
authors.initials: X
authors.lastname: Shi
authors.forename: Jin
authors.initials: J
authors.lastname: Liu
authors.forename: Jian
authors.initials: J
authors.lastname: Huang
authors.forename: Yong
authors.initials: Y
authors.lastname: Zhou
authors.forename: Yang
authors.initials: Y
authors.lastname: Xie
authors.forename: Shuangge
authors.initials: S
authors.lastname: Ma
mesh_terms: D004777:Environment; D059647:Gene-Environment Interaction; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics; D008297:Male; D008957:Models, Genetic; D011379:Prognosis; D012307:Risk Factors; D013997:Time Factors
keywords: gene-environment interaction; marker identification; penalization; robust rank estimation