中国医学前沿杂志（电子版）2017，Vol.9Issue(10)：47-52,6.DOI:10.12037/YXQY.2017.10-08
广义多因素降维法在心脑血管病基因-基因/环境交互作用分析及风险预测中的应用
Application of the generalized multifactor dimensionality reduction (GMDR) method in the gene-by-gene and gene-by-environment interactions and cardiovascular risk prediction
摘要
Abstract
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.关键词
心脑血管疾病/基因-基因交互作用/基因-环境交互作用/Logistic回归模型/广义多因素降维法Key words
Cardiovascular diseases/Gene-gene interaction/Gene-environmental interaction/Logistic regression model/Generalized multifactor dimensionality reduction引用本文复制引用
冯聪蕊,张伟丽..广义多因素降维法在心脑血管病基因-基因/环境交互作用分析及风险预测中的应用[J].中国医学前沿杂志（电子版）,2017,9(10):47-52,6.基金项目
国家自然科学基金面上项目资助(81270164) （81270164）
