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National Institute of Environmental Health Sciences

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Your Environment. Your Health.

Publication Detail

Title:

Authors: Kawaguchi, Eric S; Li, Gang; Lewinger, Juan Pablo; Gauderman, W James

Abstract: Defined by their genetic profile, individuals may exhibit differential clinical outcomes due to an environmental exposure. Identifying subgroups based on specific exposure-modifying genes can lead to targeted interventions and focused studies. Genome-wide interaction scans (GWIS) can be performed to identify such genes, but these scans typically suffer from low power due to the large multiple testing burden. We provide a novel framework for powerful two-step hypothesis tests for GWIS with a time-to-event endpoint under the Cox proportional hazards model. In the Cox regression setting, we develop an approach that prioritizes genes for Step-2

PubMed ID: 35075649 Exiting the NIEHS site

MeSH Terms: Computer Simulation; Gene-Environment Interaction*; Genome-Wide Association Study*/methods; Humans; Models, Genetic; Polymorphism, Single Nucleotide

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