期刊
HUMAN HEREDITY
卷 63, 期 2, 页码 111-119出版社
KARGER
DOI: 10.1159/000099183
关键词
gene-environment interaction; power and sample size calculations; genome-wide association scans
资金
- NCI NIH HHS [P01 CA08796, U01 CA098233, R01 CA52862] Funding Source: Medline
- NIEHS NIH HHS [U01 ES015090, P01 ES07048] Funding Source: Medline
- NATIONAL CANCER INSTITUTE [R01CA052862, U01CA098233] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES [U01ES015090] Funding Source: NIH RePORTER
Complex disease by definition results from the interplay of genetic and environmental factors. However, it is currently unclear how gene-environment interaction can best be used to locate complex disease susceptibility loci, particularly in the context of studies where between 1,000 and 1,000,000 markers are scanned for association with disease. We present a joint test of marginal association and gene-environment interaction for case-control data. We compare the power and sample size requirements of this joint test to other analyses: the marginal test of genetic association, the standard test for gene-environment interaction based on logistic regression, and the case-only test for interaction that exploits gene-environment independence. Although for many penetrance models the joint test of genetic marginal effect and interaction is not the most powerful, it is nearly optimal across all penetrance models we considered. In particular, it generally has better power than the marginal test when the genetic effect is restricted to exposed subjects and much better power than the tests of gene-environment interaction when the genetic effect is not restricted to a particular exposure level. This makes the joint test an attractive tool for large-scale association scans where the true gene-environment interaction model is unknown. Copyright (c) 2007 S. Karger AG, Basel.
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