Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, 800 E. Leigh St., PO Box 980126, Richmond, VA, 23298-0126, USA,
Behav Genet. 2014 Mar;44(2):165-81. doi: 10.1007/s10519-014-9642-1. Epub 2014 Feb 16.
The study of gene-environment interaction (G × E) has garnered widespread attention. The most common way to assess interaction effects is in a regression model with a G × E interaction term that is a product of the values specified for the genotypic (G) and environmental (E) variables. In this paper we discuss the circumstances under which interaction can be modeled as a product term and cases in which use of a product term is inappropriate and may lead to erroneous conclusions about the presence and nature of interaction effects. In the case of a binary coded genetic variant (as used in dominant and recessive models, or where the minor allele occurs so infrequently that it is not observed in the homozygous state), the regression coefficient corresponding to a significant interaction term reflects a slope difference between the two genotype categories and appropriately characterizes the statistical interaction between the genetic and environmental variables. However, when using a three-category polymorphic genotype, as is commonly done when modeling an additive effect, both false positive and false negative results can occur, and the nature of the interaction can be misrepresented. We present a reparameterized regression equation that accurately captures interaction effects without the constraints imposed by modeling interactions using a single cross-product term. In addition, we provide a series of recommendations for making conclusions about the presence of meaningful G × E interactions, which take into account the nature of the observed interactions and whether they map onto sensible genotypic models.
基因-环境相互作用（G×E）的研究受到了广泛关注。评估相互作用效应最常用的方法是在回归模型中使用 G×E 相互作用项，该项是指定的基因型（G）和环境（E）变量值的乘积。在本文中，我们讨论了可以将相互作用建模为乘积项的情况，以及使用乘积项不适当且可能导致关于相互作用效应的存在和性质的错误结论的情况。在二元编码遗传变异的情况下（如在显性和隐性模型中使用，或者在少数等位基因如此罕见以至于在纯合状态下未观察到），对应于显著相互作用项的回归系数反映了两种基因型类别的斜率差异，并恰当地描述了遗传和环境变量之间的统计相互作用。然而，当使用常见的三类别多态基因型来模拟加性效应时，可能会出现假阳性和假阴性结果，并且相互作用的性质可能会被错误表示。我们提出了一个重新参数化的回归方程，该方程可以准确捕捉相互作用效应，而无需使用单个交叉乘积项来建模相互作用所施加的限制。此外，我们提供了一系列关于存在有意义的 G×E 相互作用的结论的建议，这些建议考虑了观察到的相互作用的性质以及它们是否映射到合理的基因型模型。
