Gene-Environment Interactions in Genome-Wide Association Studies: A Comparative Study of Tests Applied to Empirical Studies of Type 2 Diabetes
Gene-Environment Interactions in Genome-Wide Association Studies: A Comparative Study of Tests Applied to Empirical Studies of Type 2 Diabetes
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DOI：
10.1093/aje/kwr368
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发表时间：
2012-02-01
影响因子：
5
通讯作者：
Kraft, Peter
中科院分区：
文献类型：
--
作者：
Cornelis, Marilyn C.;Tchetgen, Eric J. Tchetgen;Kraft, Peter
在全基因组关联研究 (GWAS) 背景下，哪种统计方法对于研究基因与环境 (G-E) 相互作用最有效的问题仍未解决。通过使用 2 项 2 型糖尿病病例对照 GWAS（护士健康研究，1976-2006 年和健康专业人员随访研究，1986-2006 年），作者比较了 5 种相互作用检验：基于标准逻辑回归的病例对照；仅限案例；经验贝叶斯收缩估计器的半参数最大似然估计；和2阶段测试。作者还比较了遗传主效应和 G-E 相互作用的 2 个联合检验。体重指数升高是感兴趣的暴露，并被建模为二元特征，以避免作者在连续体重指数的主效应被错误指定时观察到的夸大的 I 型错误率。尽管仅病例估计方法和半参数最大似然估计方法均假设所测试的标记物与一般人群中的暴露无关，但作者在对 2,199 例病例和 3,044 名对照进行的研究中并未观察到这些测试夸大 I 型错误的任何证据。两项联合测试均检测到具有已知边际效应的标记。使用标准、经验贝叶斯和两阶段测试，具有最显着 G-E 相互作用的基因座与对照之间的暴露密切相关。研究结果表明，利用 G-E 独立性的方法可以成为研究 GWAS 中 G-E 相互作用的有效且有效的选择。
The question of which statistical approach is the most effective for investigating gene-environment (G-E) interactions in the context of genome-wide association studies (GWAS) remains unresolved. By using 2 case-control GWAS (the Nurses' Health Study, 1976-2006, and the Health Professionals Follow-up Study, 1986-2006) of type 2 diabetes, the authors compared 5 tests for interactions: standard logistic regression-based case-control; case-only; semiparametric maximum-likelihood estimation of an empirical-Bayes shrinkage estimator; and 2-stage tests. The authors also compared 2 joint tests of genetic main effects and G-E interaction. Elevated body mass index was the exposure of interest and was modeled as a binary trait to avoid an inflated type I error rate that the authors observed when the main effect of continuous body mass index was misspecified. Although both the case-only and the semiparametric maximum-likelihood estimation approaches assume that the tested markers are independent of exposure in the general population, the authors did not observe any evidence of inflated type I error for these tests in their studies with 2,199 cases and 3,044 controls. Both joint tests detected markers with known marginal effects. Loci with the most significant G-E interactions using the standard, empirical-Bayes, and 2-stage tests were strongly correlated with the exposure among controls. Study findings suggest that methods exploiting G-E independence can be efficient and valid options for investigating G-E interactions in GWAS.