Additive varying-coefficient model for nonlinear gene-environment interactions

作者：Wu Cen; Zhong Ping Shou; Cui Yuehua^*

来源：Statistical Applications in Genetics and Molecular Biology, 2018, 17(2): 20170008.

DOI：10.1515/sagmb-2017-0008

• B-spline
• gene-set analysis
• local quadratic approximation
• SCAD penalty
• variable selection

Gene-environment (GxE) interaction plays a pivotal role in understanding the genetic basis of complex disease. When environmental factors are measured continuously, one can assess the genetic sensitivity over different environmental conditions on a disease trait. Motivated by the increasing awareness of gene set based association analysis over single variant based approaches, we proposed an additive varying-coefficient model to jointly model variants in a genetic system. The model allows us to examine how variants in a gene set are moderated by an environment factor to affect a disease phenotype. We approached the problem from a variable selection perspective. In particular, we select variants with varying, constant and zero coefficients, which correspond to cases of GxE interaction, no GxE interaction and no genetic effect, respectively. The procedure was implemented through a two-stage iterative estimation algorithm via the smoothly clipped absolute deviation penalty function. Under certain regularity conditions, we established the consistency property in variable selection as well as effect separation of the two stage iterative estimators, and showed the optimal convergence rates of the estimates for varying effects. In addition, we showed that the estimate of non-zero constant coefficients enjoy the oracle property. The utility of our procedure was demonstrated through simulation studies and real data analysis.

• 出版日期2018-4