A penalized robust semiparametric approach for gene-environment interactions

Stat Med. 2015 Dec 30;34(30):4016-30. doi: 10.1002/sim.6609. Epub 2015 Aug 3.

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.

Keywords: gene-environment interactions; partially linear varying coefficient model; penalized selection; robustness.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Biomarkers, Tumor / genetics
  • Biostatistics
  • Computer Simulation
  • Databases, Genetic
  • Female
  • Gene Expression
  • Gene-Environment Interaction*
  • Humans
  • Linear Models
  • Lung Neoplasms / etiology
  • Lung Neoplasms / genetics
  • Male
  • Models, Genetic*
  • Models, Statistical*
  • Polymorphism, Single Nucleotide

Substances

  • Biomarkers, Tumor