  In the genomic analysis, it is significant while challenging to identify
markers associated with cancer outcomes or phenotypes. Based on the biological
mechanisms of cancers and the characteristics of datasets as well, this paper
proposes a novel integrative interaction approach under the semiparametric
model, in which the genetic factors and environmental factors are included as
the parametric and nonparametric components, respectively. The goal of this
approach is to identify the genetic factors and gene-gene interactions
associated with cancer outcomes, and meanwhile, estimate the nonlinear effects
of environmental factors. The proposed approach is based on the threshold
gradient directed regularization (TGDR) technique. Simulation studies indicate
that the proposed approach outperforms in the identification of main effects
and interactions, and has favorable estimation and prediction accuracy compared
with the alternative methods. The analysis of non-small-cell lung carcinomas
(NSCLC) datasets from The Cancer Genome Atlas (TCGA) are conducted, showing
that the proposed approach can identify markers with important implications and
have favorable performance in prediction accuracy, identification stability,
and computation cost.
