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当环境暴露被错误指定时,纳入基因-环境相互作用的遗传关联检验的稳健性。

On the robustness of tests of genetic associations incorporating gene-environment interaction when the environmental exposure is misspecified.

机构信息

Department of Epidemiology, Harvard University, Boston, MA 02115, USA.

出版信息

Epidemiology. 2011 Mar;22(2):257-61. doi: 10.1097/EDE.0b013e31820877c5.

Abstract

We consider the robustness of tests of genetic associations that incorporate gene-environment interactions when the environmental exposure is misspecified, which is likely the case when the exposure is continuous. We formally prove that, under the null hypothesis of no genetic association, misspecified ordinary logistic regression and profile likelihood (Chatterjee and Carroll, Biometrika. 2005;92:399-418) analyses of case-control data both consistently estimate the null parameters of no genetic main effect and interaction, provided that genetic and environmental factors are unrelated in the underlying population. However, we argue that the associated likelihood ratio test, score test, and Wald test statistics obtained using the estimated information matrix have incorrect type-1 error rates due to model mis-specification. Based on these observations, we propose the use of the sandwich estimator of variance in conjunction with the consistent maximum (profile) likelihood estimates to construct Wald-type test statistics with correct type-1 error rate for the null of no genetic association.

摘要

我们考虑了在环境暴露被错误指定的情况下,纳入基因-环境相互作用的遗传关联测试的稳健性,当暴露是连续的时候,这种情况很可能会发生。我们正式证明,在没有遗传关联的零假设下,错误指定的普通逻辑回归和轮廓似然(Chatterjee 和 Carroll,生物统计学。2005;92:399-418)分析病例对照数据都一致地估计了没有遗传主效应和交互作用的零参数,前提是遗传和环境因素在基础人群中没有关联。然而,我们认为,由于模型误设,使用估计的信息矩阵获得的相关似然比检验、得分检验和 Wald 检验统计量具有不正确的一类错误率。基于这些观察,我们提出使用方差的夹层估计器结合一致的最大(轮廓)似然估计来构建 Wald 型检验统计量,对于没有遗传关联的零假设,具有正确的一类错误率。

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