A Scalable Hierarchical Lasso for Gene–Environment Interactions
提出一种正则化回归模型，用于选择基因-环境交互作用，强调主效应优先于交互效应的层次结构，并开发了高效算法和筛选规则，在模拟和实际数据中表现优于现有方法。
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.