Department of Human Development, Cornell University, Ithaca, NY, USA.
J Child Psychol Psychiatry. 2016 Nov;57(11):1258-1267. doi: 10.1111/jcpp.12579. Epub 2016 May 31.
Standard models used to test gene-environment interaction (G × E) hypotheses make the causal assumption that there are no unobserved variables that could be biasing the interaction estimate. Whether this assumption can be met in nonexperimental studies is unclear because the interactive biological pathways from genetic polymorphisms and environments to behavior, and the confounders that can be introduced along these pathways, are often not delineated. This is problematic in the context of studies focused on caregiver-child dyads, in which common genes and environments induce gene-environment correlation. To address the impact of sources of bias in G × E models specifically assessing the interaction between child genotype and caregiver behavior, we provide a causal framework that integrates biological and statistical concepts of G × E, and assess the magnitude of bias introduced by various confounding pathways in different causal circumstances.
A simulation assessed the magnitude of bias introduced by four types of confounding pathways in different causal models. Unadjusted and adjusted statistical models were then applied to the simulated data to assess the efficacy of these procedures to capture unbiased G × E estimates. Finally, the simulation was run under null effects of the genotype to assess the impact of biasing sources on the false-positive rate.
Common environmental pathways between caregiver and child inflated G × E estimates and raised the false-positive rate. Evocative effects of the child also inflated G × E estimates.
Gene-environment interaction studies should be approached with consideration to the causal pathways at play and the confounding opportunities along these pathways to facilitate the inclusion of adequate statistical controls and correct inferences from study findings. Bridging biological and statistical concepts of G × E can significantly improve research design and the communication of how a G × E process fits into a broader developmental framework.
用于检验基因-环境交互作用（G×E）假设的标准模型做出了因果假设，即不存在可能影响交互作用估计的未观察到的变量。由于遗传多态性和环境与行为之间的交互生物学途径以及可能沿着这些途径引入的混杂因素通常没有被阐明，因此在非实验研究中，这一假设是否能够成立尚不清楚。在关注照顾者-儿童对子的研究中，这一问题尤为突出，因为常见的基因和环境会导致基因-环境相关。为了解决专门评估儿童基因型与照顾者行为之间相互作用的 G×E 模型中偏差来源的影响，我们提供了一个因果框架，该框架整合了 G×E 的生物学和统计概念，并评估了不同因果情况下各种混杂途径引入偏差的大小。
模拟评估了四种混杂途径在不同因果模型中引入偏差的大小。然后，将未经调整和调整后的统计模型应用于模拟数据，以评估这些程序捕获无偏 G×E 估计的效果。最后，在基因型无效应的情况下运行模拟，以评估偏置源对假阳性率的影响。
照顾者和儿童之间的共同环境途径会夸大 G×E 估计值，并提高假阳性率。儿童的唤起效应也会夸大 G×E 估计值。
在进行基因-环境相互作用研究时，应考虑到因果途径和这些途径中的混杂机会，以便为充分的统计控制和从研究结果中得出正确推论提供便利。将 G×E 的生物学和统计学概念结合起来，可以显著改善研究设计和沟通，使 G×E 过程如何融入更广泛的发展框架。
