A scalable hierarchical lasso for gene-environment interactions.
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ABSTRACT:
SUBMITTER: Zemlianskaia N
PROVIDER: S-EPMC9928188 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
Publications
A scalable hierarchical lasso for gene-environment interactions.
Zemlianskaia Natalia N Gauderman W James WJ Lewinger Juan Pablo JP
Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 20220331 4
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 select ...[more]
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