A scalable hierarchical lasso for gene-environment interactions.

hierarchical variable selection joint analysis screening rules

Journal

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
ISSN: 1061-8600
Titre abrégé: J Comput Graph Stat
Pays: United States
ID NLM: 101470926

Informations de publication

Date de publication:
2022
Historique:
entrez: 16 2 2023
pubmed: 17 2 2023
medline: 17 2 2023
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 36793591
doi: 10.1080/10618600.2022.2039161
pmc: PMC9928188
mid: NIHMS1798410
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1091-1103

Subventions

Organisme : NCI NIH HHS
ID : P01 CA196569
Pays : United States
Organisme : NIEHS NIH HHS
ID : T32 ES013678
Pays : United States

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Auteurs

Natalia Zemlianskaia (N)

Division of Biostatistics, Department of Preventive Medicine, University or Southern California.

W James Gauderman (WJ)

Division of Biostatistics, Department of Preventive Medicine, University or Southern California.

Juan Pablo Lewinger (JP)

Division of Biostatistics, Department of Preventive Medicine, University or Southern California.

Classifications MeSH