| Autor: |
Gauderman, W James, Kim, Andre, Conti, David V, Morrison, John, Thomas, Duncan C, Vora, Hita, Lewinger, Juan Pablo |
| Předmět: |
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| Zdroj: |
American Journal of Epidemiology; Apr2019, Vol. 188 Issue 4, p760-767, 8p |
| Abstrakt: |
Gene-environment (G × E) interaction is important for many complex traits. In a case-control study of a disease trait, logistic regression is the standard approach used to model disease as a function of a gene (G), an environmental factor (E), G × E interaction, and adjustment covariates. We propose an alternative model with G as the outcome and show how it provides a unified framework for obtaining results from all of the common G × E tests. These include the 1–degree-of-freedom (df) test of G × E interaction, the 2-df joint test of G and G × E, the case-only and empirical Bayes tests, and several 2-step tests. In the context of this unified model, we propose a novel 3-df test and demonstrate that it provides robust power across a wide range of underlying G × E interaction models. We demonstrate the 3-df test in a genome-wide scan of G × sex interaction for childhood asthma using data from the Children's Health Study (Southern California, 1993–2001). This scan identified a strong G × sex interaction at the phosphodiesterase gene 4D locus (PDE4D), a known asthma-related locus, with a strong effect in males (per-allele odds ratio = 1.70; P = 3.8 × 10−8) and virtually no effect in females. We describe a software program, G×EScan (University of Southern California, Los Angeles, California), which can be used to fit standard and unified models for genome-wide G × E studies. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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