A scalable hierarchical lasso for gene-environment interactions

We describe a regularized regression model for the selection of gene-environment (G × E) interactions. The model focuses on a single environmental exposure and induces a main-effect-before-interaction hierarchical structure. We propose an efficient fitting algorithm and screening rules that can discard large numbers of irrelevant predictors with high accuracy. We present simulation results showing that the model outperforms existing joint selection methods for (G × E) interactions in terms of selection performance, scalability and speed, and provide a real data application. Our implementation is available in the gesso R package.