Université Paris-Sud, Le Kremlin Bicêtre, France.
Am J Epidemiol. 2011 Jan 15;173(2):225-35. doi: 10.1093/aje/kwq352. Epub 2010 Nov 17.
The use of a reference control panel in genome-wide association studies is an interesting solution to the problem of how to reduce costs. In such designs, data on relevant environmental factors are usually collected only in cases, making it more difficult to deal with potential gene-environment interactions when testing for genetic association. However, under certain circumstances, neglecting an existing interaction with the environment may be detrimental in terms of statistical power to detect the genetic factor. In this paper, the authors propose a novel method based on a multinomial logistic regression model to overcome the lack of environmental exposure information in controls, by contrasting both exposed and unexposed cases with the control sample. For each case group, a genetic effect-size parameter is estimated, and the genetic association and the gene-environment interaction are tested jointly. The authors evaluate the performance of this method through asymptotic computations and simulations of cases and population controls under different models. In the presence of a gene-environment interaction, this approach outperforms other available methods that test for genetic association and gene-environment interaction either separately or jointly. Interestingly, it even has better power than the joint test requiring full knowledge of the environmental information in both cases and controls.
在全基因组关联研究中使用参考对照组是降低成本的一个有趣解决方案。在这种设计中，通常仅在病例中收集相关环境因素的数据，这使得在检测遗传关联时更难以处理潜在的基因-环境相互作用。然而，在某些情况下，忽略与环境的现有相互作用可能会对检测遗传因素的统计功效产生不利影响。在本文中，作者提出了一种基于多项逻辑回归模型的新方法，通过将暴露和未暴露的病例与对照样本进行对比，克服了对照中缺乏环境暴露信息的问题。对于每个病例组，估计一个遗传效应大小参数，并联合检验遗传关联和基因-环境相互作用。作者通过不同模型下的病例和人群对照的渐近计算和模拟评估了该方法的性能。在存在基因-环境相互作用的情况下，这种方法优于其他可用的方法，这些方法要么分别测试遗传关联和基因-环境相互作用，要么联合测试。有趣的是，它甚至比需要同时了解病例和对照中环境信息的联合测试具有更好的功效。
