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Article

A screening-testing approach for detecting gene-environment interactions using sequential penalized and unpenalized multiple logistic regression.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing - 1 Jan 2015

Frost H Robert, Andrew Angeline S, Karagas Margaret R, Moore Jason H

Abstract excerpt

Gene-environment (G × E) interactions are biologically important for a wide range of environmental exposures and clinical outcomes. Because of the large number of potential interactions in genomewide association data, the standard approach fits one model per G × E interaction with multiple hypothesis correction (MHC) used to control the type I error rate. Although sometimes effective, using one model per...

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PMID: 25592580
PMCID: PMC4299918

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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