Aschard Hugues, Spiegelman Donna, Laville Vincent, Kraft Pete, Wang Molin
Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris, France.
Department of Epidemiology, Harvard T.H.Chan School of Public Health, Boston, USA.
Genet Epidemiol. 2018 Apr;42(3):250-264. doi: 10.1002/gepi.22113. Epub 2018 Feb 8.
The identification of gene-environment interactions in relation to risk of human diseases has been challenging. One difficulty has been that measurement error in the exposure can lead to massive reductions in the power of the test, as well as in bias toward the null in the interaction effect estimates. Leveraging previous work on linear discriminant analysis, we develop a new test of interaction between genetic variants and a continuous exposure that mitigates these detrimental impacts of exposure measurement error in ExG testing by reversing the role of exposure and the diseases status in the fitted model, thus transforming the analysis to standard linear regression. Through simulation studies, we show that the proposed approach is valid in the presence of classical exposure measurement error as well as when there is correlation between the exposure and the genetic variant. Simulations also demonstrated that the reverse test has greater power compared to logistic regression. Finally, we confirmed that our approach eliminates bias from exposure measurement error in estimation. Computing times are reduced by as much as fivefold in this new approach. For illustrative purposes, we applied the new approach to an ExGWAS study of interactions with alcohol and body mass index among 1,145 cases with invasive breast cancer and 1,142 controls from the Cancer Genetic Markers of Susceptibility study.
识别与人类疾病风险相关的基因-环境相互作用一直具有挑战性。其中一个困难在于，暴露因素的测量误差可能导致检验效能大幅降低，以及在相互作用效应估计中出现向无效值的偏差。利用先前关于线性判别分析的工作，我们开发了一种新的基因变异与连续暴露之间相互作用的检验方法，通过在拟合模型中颠倒暴露因素和疾病状态的作用，减轻了暴露测量误差在基因-环境（ExG）检验中的这些不利影响，从而将分析转化为标准线性回归。通过模拟研究，我们表明所提出的方法在存在经典暴露测量误差以及暴露因素与基因变异之间存在相关性的情况下都是有效的。模拟还表明，与逻辑回归相比，反向检验具有更高的效能。最后，我们证实我们的方法在估计中消除了暴露测量误差带来的偏差。在这种新方法中，计算时间减少了多达五倍。为了说明目的，我们将新方法应用于一项ExGWAS研究，该研究涉及来自癌症遗传易感性标记研究的1145例浸润性乳腺癌病例和1142例对照与酒精和体重指数的相互作用。
