Fan Wei, Shen Chao, Guo Zhirong
Department of Epidemiology, School of Public Health, Soochow University, Suzhou 215123, China.
Department of Epidemiology, School of Public Health, Soochow University, Suzhou 215123, China; Email:
Zhonghua Liu Xing Bing Xue Za Zhi. 2015 Nov;36(11):1305-10.
This paper introduces a method called model-based multifactor dimensionality reduction (MB-MDR), which was firstly proposed by Calle et al., and can be applied for detecting gene-gene or gene-environment interactions in genetic studies. The basic principle and characteristics of MB-MDR as well as the operation in R program are briefly summarized. Besides, the detailed procedure of MB-MDR is illustrated by using example. Compared with classical MDR, MB-MDR has similar principle, which merges multi-locus genotypes into a one-dimensional construct and can be used in the study with small sample size. However, there is some difference between MB-MDR and classical MDR. First, it has higher statistical power than MDR and other MDR in the presence of different noises due to the different way the genotype cells merged. Second, compared with MDR, it can deal with all binary and quantitative traits, adjust marginal effects of factors and confounders. MBMDR could be a useful method in the analyses of gene-gene/environment interactions.
本文介绍了一种名为基于模型的多因素降维法（MB-MDR）的方法，该方法由卡列等人首次提出，可用于在基因研究中检测基因-基因或基因-环境相互作用。简要总结了MB-MDR的基本原理、特点以及在R程序中的操作。此外，通过实例说明了MB-MDR的详细步骤。与经典多因素降维法相比，MB-MDR具有相似的原理，即将多位点基因型合并为一维结构，可用于小样本量研究。然而，MB-MDR与经典多因素降维法之间存在一些差异。首先，由于基因型细胞合并方式不同，在存在不同噪声的情况下，它比多因素降维法及其他多因素降维法具有更高的统计效能。其次，与多因素降维法相比，它可以处理所有二元和定量性状，调整因素和混杂因素的边际效应。MB-MDR可能是基因-基因/环境相互作用分析中的一种有用方法。
