A Scalable Hierarchical Lasso for Gene–Environment Interactions. Issue 4 (2nd October 2022)

• Record Type: Journal Article
• Authors: Zemlianskaia, Natalia; Gauderman, W. James; Lewinger, Juan Pablo
• Is Part Of: Journal of computational and graphical statistics. Volume 31:Issue 4(2022)
• Page Start: 1091
• Page End: 1103
• Publication Date: 2022-10-02
• Subjects: Hierarchical variable selection -- Joint analysis -- Screening rules; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2022.2039161
• Languages: English
• ISSNs: 1061-8600

Abstract

Abstract: 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. Supplementary materials for this article are available online.