期刊
AMERICAN JOURNAL OF EPIDEMIOLOGY
卷 183, 期 3, 页码 237-247出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/aje/kwv198
关键词
case-control; gene discovery; gene-environment independence; genome-wide association; modular methods; multiple testing; screening test; weighted hypothesis test
资金
- National Institutes of Health [P30 CA046592, R21 ES20811, U19 CA148107, P30 CA014089, T32 ES013678]
- Intramural Research Program of the National Cancer Institute
- National Science Foundation [DMS-1406712]
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1406712] Funding Source: National Science Foundation
The number of methods for genome-wide testing of gene-environment (G-E) interactions continues to increase, with the aim of discovering new genetic risk factors and obtaining insight into the disease-gene-environment relationship. The relative performance of these methods, assessed on the basis of family-wise type I error rate and power, depends on underlying disease-gene-environment associations, estimates of which may be biased in the presence of exposure misclassification. This simulation study expands on a previously published simulation study of methods for detecting G-E interactions by evaluating the impact of exposure misclassification. We consider 7 single-step and modular screening methods for identifying G-E interaction at a genome-wide level and 7 joint tests for genetic association and G-E interaction, for which the goal is to discover new genetic susceptibility loci by leveraging G-E interaction when present. In terms of statistical power, modular methods that screen on the basis of the marginal disease-gene relationship are more robust to exposure misclassification. Joint tests that include main/marginal effects of a gene display a similar robustness, which confirms results from earlier studies. Our results offer an increased understanding of the strengths and limitations of methods for genome-wide searches for G-E interaction and joint tests in the presence of exposure misclassification.
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