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Multivariate generalized multifactor dimensionality reduction to detect gene-gene interactions

机译:多元广义多因素降维以检测基因与基因的相互作用

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Background Recently, one of the greatest challenges in genome-wide association studies is to detect gene-gene and/or gene-environment interactions for common complex human diseases. Ritchie et al. (2001) proposed multifactor dimensionality reduction (MDR) method for interaction analysis. MDR is a combinatorial approach to reduce multi-locus genotypes into high-risk and low-risk groups. Although MDR has been widely used for case-control studies with binary phenotypes, several extensions have been proposed. One of these methods, a generalized MDR (GMDR) proposed by Lou et al. (2007), allows adjusting for covariates and applying to both dichotomous and continuous phenotypes. GMDR uses the residual score of a generalized linear model of phenotypes to assign either high-risk or low-risk group, while MDR uses the ratio of cases to controls.
机译:背景技术近来,全基因组关联研究中的最大挑战之一是检测常见的复杂人类疾病的基因-基因和/或基因-环境相互作用。 Ritchie等。 (2001)提出了多因素降维(MDR)的方法进行交互分析。 MDR是一种组合方法,可将多位点基因型减少为高风险和低风险组。尽管MDR已广泛用于具有二元表型的病例对照研究,但已提出了几种扩展方案。这些方法之一是Lou等人提出的广义MDR(GMDR)。 (2007年),允许调整协变量,并适用于二分和连续表型。 GMDR使用广义表型线性模型的残差评分来分配高风险或低风险组,而MDR使用病例与对照的比率。

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