Mi Xiaojuan, Eskridge Kent M, George Varghese, Wang Dong
Department of Statistics, University of Nebraska, Lincoln, 68583-0963, USA.
Ann Hum Genet. 2011 Mar;75(2):255-65. doi: 10.1111/j.1469-1809.2010.00634.x. Epub 2011 Jan 17.
Coronary heart disease (CHD) is a complex disease, which is influenced not only by genetic and environmental factors but also by gene-environment (GE) interactions in interconnected biological pathways or networks. The classical methods are inadequate for identifying GE interactions due to the complex relationships among risk factors, mediating risk factors (e.g., hypertension, blood lipids, and glucose), and CHD. Our aim was to develop a two-level structural equation model (SEM) to identify genes and GE interactions in the progress of CHD to take into account the causal structure among mediating risk factors and CHD (Level 1), and hierarchical family structure (Level 2). The method was applied to the Framingham Heart Study (FHS) Offspring Cohort data. Our approach has several advantages over classical methods: (1) it provides important insight into how genes and contributing factors affect CHD by investigating the direct, indirect, and total effects; and (2) it aids the development of biological models that more realistically reflect the complex biological pathways or networks. Using our method, we are able to detect GE interaction of SERPINE1 and body mass index (BMI) on CHD, which has not been reported. We conclude that SEM modeling of GE interaction can be applied in the analysis of complex epidemiological data sets.
冠心病（CHD）是一种复杂的疾病，不仅受遗传和环境因素影响，还受相互关联的生物途径或网络中的基因 - 环境（GE）相互作用影响。由于风险因素、中介风险因素（如高血压、血脂和血糖）与冠心病之间关系复杂，传统方法不足以识别GE相互作用。我们的目标是开发一个两级结构方程模型（SEM），以识别冠心病发病过程中的基因和GE相互作用，同时考虑中介风险因素与冠心病之间的因果结构（第1级）以及分层家庭结构（第2级）。该方法应用于弗雷明汉心脏研究（FHS）子代队列数据。我们的方法相对于传统方法具有几个优点：（1）通过研究直接、间接和总效应，深入了解基因和促成因素如何影响冠心病；（2）有助于开发更真实反映复杂生物途径或网络的生物学模型。使用我们的方法，我们能够检测到SERPINE1与体重指数（BMI）对冠心病的GE相互作用，这尚未见报道。我们得出结论，GE相互作用的SEM建模可应用于复杂流行病学数据集的分析。
