Semivarying coefficient least-squares support vector regression for analyzing high-dimensional gene-environmental data

In the context of genetics and genomic medicine, gene-environment (GxE) interactions have a great impact on the risk of human diseases. Some existing methods for identifying GxE interactions are considered to be limited, since they analyze one or a few number of G factors at a time, assume linear effects of E factors, and use inefficient selection methods. In this paper, we propose a new method to identify significant main effects and GxE interactions. This is based on a semivarying coefficient least-squares support vector regression (LS-SVR) technique, which is devised by utilizing flexible semiparametric LS-SVR approach for censored survival data. This semivarying coefficient model is used to deal with the nonlinear effects of E factors. We also derive a generalized cross validation (GCV) function for determining the optimal values of hyperparameters of the proposed method. This GCV function is also used to identify significant main effects and GxE interactions. The proposed method is evaluated through numerical studies.