Foulkes A S, Reilly M, Zhou L, Wolfe M, Rader D J
Department of Biostatistics, University of Massachusetts, School of Public Health, 404 Arnold House, 715N. Pleasant Street, Amherst, MA 01003-9304, USA.
Stat Med. 2005 Mar 15;24(5):775-89. doi: 10.1002/sim.1965.
We propose using mixed effects models to characterize the association between multiple gene polymorphisms, environmental factors and measures of disease progression. Characterizing high-order gene-gene and gene-environment interactions presents an analytic challenge due to the large number of candidate genes and the complex, undescribed interactions among them. Several approaches have been proposed recently to reduce the number of candidate genes and post hoc approaches to identify gene-gene interactions are described. However, these approaches may be inadequate for identifying high-order interactions in the absence of main effects and generally do not permit us to control for potential confounders. We describe how mixed effects models and related testing procedures overcome these limitations and apply this approach to data from a cohort of subjects at risk for cardiovascular disease. Four (4) genetic polymorphisms in three genes of the same gene family are considered. The proposed modelling approach allows us first to test whether there is a significant genetic contribution to the variability observed in our disease outcome. This contribution may be through main effects of multi-locus genotypes or through an interaction between genotype and environmental factors. This approach also enables us to identify specific multi-locus genotypes that interact with environmental factors in predicting the outcome. Mixed effects models provide a flexible statistical framework for controlling for potential confounders and identifying interactions among multiple genes and environmental factors that explain the variability in measures of disease progression.
我们建议使用混合效应模型来描述多个基因多态性、环境因素与疾病进展指标之间的关联。由于候选基因数量众多且它们之间存在复杂的、未描述的相互作用，描述高阶基因-基因和基因-环境相互作用带来了分析挑战。最近提出了几种方法来减少候选基因的数量，并描述了用于识别基因-基因相互作用的事后方法。然而，在没有主效应的情况下，这些方法可能不足以识别高阶相互作用，并且通常不允许我们控制潜在的混杂因素。我们描述了混合效应模型和相关测试程序如何克服这些限制，并将这种方法应用于心血管疾病高危人群的数据。我们考虑了同一基因家族的三个基因中的四个遗传多态性。所提出的建模方法首先使我们能够测试在我们的疾病结果中观察到的变异性是否有显著的遗传贡献。这种贡献可能是通过多位点基因型的主效应，或者是通过基因型与环境因素之间的相互作用。这种方法还使我们能够识别在预测结果时与环境因素相互作用的特定多位点基因型。混合效应模型为控制潜在的混杂因素以及识别多个基因和环境因素之间的相互作用提供了一个灵活的统计框架，这些相互作用解释了疾病进展指标中的变异性。
