心脑血管疾病是由易感基因和环境因素共同作用引起的复杂性遗传疾病.在影响心脑血管疾病发病的因素中,基因与基因之间、基因与环境之间均存在着交互作用.近年来,随着被发现的易感基因和环境因素越来越多,单独使用Logistic回归模型等方法处理交互作用存在"维度灾难"等局限性,导致研究结果假阳性率增加.广义多因素降维法引入计分统计量的原理,能够列举所有基因型和环境因素的组合情况,分析端粒长度等连续型结局变量,且能够纳入协变量,校正年龄、性别等影响心脑血管疾病发病的混杂因素,从而控制混杂因素引起的结果偏移乃至错误.广义多因素降维法将高维数据降为一维模型后,结合Logistic回归模型能够更好地预测心脑血管疾病的发病风险,对阐明心脑血管疾病的发病机制具有重要意义.%Gene-gene or gene-environment interactions may be responsible for the complex disorders such as cardiovascular diseases (CVD), and it has become a daunting task to determine which interaction of genetic factors is associated with common complex diseases. In recent years, with the increase number of susceptible genes and environmental factors, single-locus analysis, such as the logistic regression model was proved to be inapplicable in detecting and characterizing interactions among multiple factors of complex cardiovascular disorders. It can result in false positive rate of research results because of the limitation of "curse of dimensionality". In contrast, the "generalized multifactor dimensionality reduction (GMDR)" method introduces the principle of score statistic, which can enumerate the combination of all genes and the environment factors. GMDR permits adjustment for covariates, provides a unified framework for coherently handling both dichotomous and quantitative phenotypes, and makes up the limitation of logistic regression. This review will systematically assess the application of GMDR and logistic regression model in analyzing multiple susceptible genes and environmental factors interactions and in the prediction model of cardiovascular risk.
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