At the time of the study, Brandon Wagner, Hexuan Liu, and Guang Guo were with the Department of Sociology and Carolina Population Center, University of North Carolina, Chapel Hill. Guang Guo is also with the Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill. Jiang Li is with Department of Health Behavior, Gillings School of Global Public Health, University of North Carolina, Chapel Hill.
Am J Public Health. 2013 Oct;103 Suppl 1(Suppl 1):S167-73. doi: 10.2105/AJPH.2013.301415. Epub 2013 Aug 8.
We explored how gene-environment correlations can result in endogenous models, how natural experiments can protect against this threat, and if unbiased estimates from natural experiments are generalizable to other contexts.
We compared a natural experiment, the College Roommate Study, which measured genes and behaviors of college students and their randomly assigned roommates in a southern public university, with observational data from the National Longitudinal Study of Adolescent Health in 2008. We predicted exposure to exercising peers using genetic markers and estimated environmental effects on alcohol consumption. A mixed-linear model estimated an alcohol consumption variance that was attributable to genetic markers and across peer environments.
Peer exercise environment was associated with respondent genotype in observational data, but not in the natural experiment. The effects of peer drinking and presence of a general gene-environment interaction were similar between data sets.
Natural experiments, like random roommate assignment, could protect against potential bias introduced by gene-environment correlations. When combined with representative observational data, unbiased and generalizable causal effects could be estimated.
我们探讨了基因-环境相关如何导致内源性模型,自然实验如何防范这种威胁,以及自然实验的无偏估计是否可以推广到其他情境。
我们比较了一项自然实验,即“大学室友研究”,该研究测量了南方一所公立大学的大学生及其随机分配的室友的基因和行为,以及 2008 年全国青少年健康纵向研究的观察数据。我们使用遗传标记预测接触锻炼同伴的情况,并估计环境对饮酒的影响。混合线性模型估计了归因于遗传标记和同伴环境的酒精消费方差。
在观察数据中,同伴的锻炼环境与被试的基因型相关,但在自然实验中则不相关。同伴饮酒和一般基因-环境相互作用的影响在两个数据集之间相似。
自然实验,如随机室友分配,可以防范由基因-环境相关性引入的潜在偏差。当与有代表性的观察数据相结合时,可以估计无偏且可推广的因果效应。