Two‐step hypothesis testing to detect gene‐environment interactions in a genome‐wide scan with a survival endpoint

Defined by their genetic profile, individuals may exhibit differential clinical outcomes due to an environmental exposure. Identifying subgroups based on specific exposure‐modifying genes can lead to targeted interventions and focused studies. Genome‐wide interaction scans (GWIS) can be performed to identify such genes, but these scans typically suffer from low power due to the large multiple testing burden. We provide a novel framework for powerful two‐step hypothesis tests for GWIS with a time‐to‐event endpoint under the Cox proportional hazards model. In the Cox regression setting, we develop an approach that prioritizes genes for Step‐2 G×E testing based on a carefully constructed Step‐1 screening procedure. Simulation results demonstrate this two‐step approach can lead to substantially higher power for identifying gene‐environment (G×E) interactions compared to the standard GWIS while preserving the family wise error rate over a range of scenarios. In a taxane‐anthracycline chemotherapy study for breast cancer patients, the two‐step approach identifies several gene expression by treatment interactions that would not be detected using the standard GWIS.