Identification of gene-environment interactions in cancer studies using penalization

High-throughput cancer studies have been extensively conducted, searching for genetic markers associated with outcomes beyond clinical and environmental risk factors. Gene-environment interactions can have important implications beyond main effects. The commonly-adopted single-marker analysis cannot accommodate the joint effects of a large number of markers. The existing joint-effects methods also have limitations. Specifically, they may suffer from high computational cost, do not respect the "main effect, interaction" hierarchical structure, or use ineffective techniques. We develop a penalization method for the identification of important G. ×. E interactions and main effects. It has an intuitive formulation, respects the hierarchical structure, accommodates the joint effects of multiple markers, and is computationally affordable. In numerical study, we analyze prognosis data under the AFT (accelerated failure time) model. Simulation shows satisfactory performance of the proposed method. Analysis of an NHL (non-Hodgkin lymphoma) study with SNP measurements shows that the proposed method identifies markers with important implications and satisfactory prediction performance.