Su Yi-Shan, Lee Wen-Chung
From the Institute of Epidemiology and Preventive Medicine (Y-SS, W-CL), College of Public Health, National Taiwan University; and Research Center for Genes, Environment and Human Health (W-CL), College of Public Health, National Taiwan University, Taipei, Taiwan.
Medicine (Baltimore). 2016 Mar;95(9):e2743. doi: 10.1097/MD.0000000000002743.
Under the assumption of gene-environment independence, unknown/unmeasured environmental factors, irrespective of what they may be, cannot confound the genetic effects. This may lead many people to believe that genetic heterogeneity across different levels of the studied environmental exposure should only mean gene-environment interaction--even though other environmental factors are not adjusted for. However, this is not true if the odds ratio is the effect measure used for quantifying genetic effects. This is because the odds ratio is a "noncollapsible" measure--a marginal odds ratio is not a weighted average of the conditional odds ratios, but instead has a tendency toward the null. In this study, the authors derive formulae for gene-environment interaction bias due to noncollapsibility. They use computer simulation and real data example to show that the bias can be substantial for common diseases. For genetic association study of nonrare diseases, researchers are advised to use collapsible measures, such as risk ratio or peril ratio.
在基因与环境独立的假设下,未知/未测量的环境因素,无论其具体是什么,都不会混淆基因效应。这可能导致许多人认为,在所研究的环境暴露的不同水平上的基因异质性仅仅意味着基因-环境相互作用——即使其他环境因素未作调整。然而,如果使用比值比作为量化基因效应的效应量度,情况并非如此。这是因为比值比是一种“不可折叠”的量度——边际比值比不是条件比值比的加权平均值,而是有趋于无效值的倾向。在本研究中,作者推导了由于不可折叠性导致的基因-环境相互作用偏倚的公式。他们使用计算机模拟和实际数据示例表明,对于常见疾病,这种偏倚可能很大。对于非罕见疾病的基因关联研究,建议研究人员使用可折叠的量度,如风险比或危险比。