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 G×E testing based on a carefully constructed Step-1 screening procedure. Simulation results demonstrate this two-step approach can lead to substantially higher power for identifying gene-environment ( G×E ) interactions compared to the standard GWIS while preserving the family wise error rate over a range of scenarios. In a taxane- anthracycline chemotherapy study for breast cancer patients, the two-step approach identifies several gene expression by treatment interactions that would not be detected using the standard GWIS.
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Authors | Eric S Kawaguchi, Gang Li, Juan Pablo Lewinger, W James Gauderman |
Journal | Statistics in medicine
(Stat Med)
Vol. 41
Issue 9
Pg. 1644-1657
(04 30 2022)
ISSN: 1097-0258 [Electronic] England |
PMID | 35075649
(Publication Type: Journal Article, Research Support, N.I.H., Extramural)
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Copyright | © 2022 John Wiley & Sons Ltd. |
Topics |
- Computer Simulation
- Gene-Environment Interaction
- Genome-Wide Association Study
(methods)
- Humans
- Models, Genetic
- Polymorphism, Single Nucleotide
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