| 陈卿,唐迅,胡永华.应用广义多因子降维法分析数量性状的交互作用[J].Chinese journal of Epidemiology,2010,31(8):938-941 |
| 应用广义多因子降维法分析数量性状的交互作用 |
| Detecting interaction for quantitative trait by generalized multifactor dimensionality reduction |
| Received:December 23, 2009 Revised:June 18, 2012 |
| DOI： |
| KeyWord: 广义多因子降维法 数量性状 交互作用 |
| English Key Word: Generalized multifactor dimensionality reduction Quantitative trait Interaction |
| FundProject:国家自然科学基金(30671807,30872173);高等学校博士学科点专项科研基金(20060001111) |
| Author Name | Affiliation | E-mail | | Chen Qing | Department of Epidemiology and Biostatistics, School of Public Health, Peking University, the Key Laboratory of Epidemiology Ministry of Education, Beijing 100191,China | | | Tang Xun | Department of Epidemiology and Biostatistics, School of Public Health, Peking University, the Key Laboratory of Epidemiology Ministry of Education, Beijing 100191,China | | | Hu Yonghua | Department of Epidemiology and Biostatistics, School of Public Health, Peking University, the Key Laboratory of Epidemiology Ministry of Education, Beijing 100191,China | yhhu@bjmu.edu.cn |
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| Abstract: |
| 介绍广义多因子降维法(GMDR)在交互作用分析,尤其是数量性状的基因-基因交互作用分析中的应用.文中简述GMDR的原理、基本步骤及其特点,并结合实例说明如何在研究中对GMDR进行应用.GMDR是无模型的交互作用分析方法,能够处理连续型结局变量,还可纳入协变量改善预测准确率,目前已成功应用于尼古丁依赖等疾病的研究.GMDR能够处理多种样本类型和结局变量类型,与其他连续变量交互作用分析方法相比具有一定优势. |
| English Abstract: |
| To introduce the application of generalized multifactor dimensionality reduction (GMDR) method for detecting interactions, especially gene-gene interactions for quantitative traits. Principles, basic steps as well as features of GMDR were discussed, illustrated with a practical research case. As an interaction analysis method, GMDR was model-free, available for studies on different outcome variables including continuous ones, and permitted adjustment for covariates to improve prediction accuracy. Evidences of its capacity had been supposed by research on different diseases, e.g. nicotine dependence. GMDR method was applicable to different types of samples and outcome variables, which was superior to other statistical approaches for continuous variables in some aspects. |
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