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. 2022;31(4):1091-1103.
doi: 10.1080/10618600.2022.2039161. Epub 2022 Mar 31.

A scalable hierarchical lasso for gene-environment interactions

Natalia Zemlianskaia  1 , W James Gauderman  1 , Juan Pablo Lewinger  1
Affiliations

A scalable hierarchical lasso for gene-environment interactions

Natalia Zemlianskaia et al. J Comput Graph Stat. 2022.

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.

Keywords: hierarchical variable selection; joint analysis; screening rules.

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Figures

Fig. 1
Fig. 1
(a) Dynamic SAFE regions, (b) dynamic GAP SAFE regions.
Fig. 2
Fig. 2
Time comparison of proposed algorithms: mean runtime over 100 replicates on the y-axis, dual gap tolerance on the x-axis.
Fig. 3
Fig. 3
Log working set size (log10 (WS)) for all lambda pairs.
Fig. 4
Fig. 4
Model performance (top row: AUC for G×E selection, bottom row: precision for G×E selection) as a function of the number of interactions discovered. p=2500, n=100, pG=10, pG×E =5.
Fig. 5
Fig. 5
Geometric solution to the problem (19). Optimal δj maximizes the range of possible x values.
See this image and copyright information in PMC

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