Relationship Between Genomic Distance-Based Regression and Kernel Machine Regression for Multi-Marker Association Testing
Relationship Between Genomic Distance-Based Regression and Kernel Machine Regression for Multi-Marker Association Testing
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DOI：
10.1002/gepi.20567
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发表时间：
2011-05-01
影响因子：
2.1
通讯作者：
Pan, Wei
中科院分区：
文献类型：
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作者：
Pan, Wei
为了检测与常见和复杂疾病的遗传关联，已经提出了两种强大但截然不同的多标记关联测试，即基于基因组距离的回归（GDBR）（Wessel and Schork [2006] Am J Hum Genet 79：821-833）和核机器回归（KMR）（Kwee et al. [2008] Am J Hum Genet 82：386-397；Wu et al. [2010] Am J嗡嗡热内特 86：929-942）。 GDBR 基于将一组受试者的多标记相似性度量与其性状值的变化相关联，而 KMR 基于通过核函数或核矩阵对多个标记对性状的影响的非参数估计。由于这两种方法都很强大且通用，但看起来却截然不同，因此了解它们的具体关系非常重要。在本报告中，我们表明，在没有其他协变量的情况下，定量或二元性状的两种方法之间存在惊人的对应关系：如果使用相同的正半定矩阵作为 GDBR 中的中心相似度矩阵和 KMR 中的核矩阵，则 GDBR 中的 F 检验统计量和 KMR 中的得分检验统计量是相等的（直到一些可忽略的常数）。结果基于两种方法与线性或逻辑（随机效应）回归模型的连接。热内特.流行病。 35: 211-216, 2011。(c) 2011 Wiley-Liss, Inc.
To detect genetic association with common and complex diseases, two powerful yet quite different multimarker association tests have been proposed, genomic distance-based regression (GDBR) (Wessel and Schork [2006] Am J Hum Genet 79: 821-833) and kernel machine regression (KMR) (Kwee et al. [2008] Am J Hum Genet 82: 386-397; Wu et al. [2010] Am J Hum Genet 86: 929-942). GDBR is based on relating a multimarker similarity metric for a group of subjects to variation in their trait values, while KMR is based on nonparametric estimates of the effects of the multiple markers on the trait through a kernel function or kernel matrix. Since the two approaches are both powerful and general, but appear quite different, it is important to know their specific relationships. In this report, we show that, under the condition that there is no other covariate, there is a striking correspondence between the two approaches for a quantitative or a binary trait: if the same positive semi-definitematrix is used as the centered similarity matrix in GDBR and as the kernel matrix in KMR, the F-test statistic in GDBR and the score test statistic in KMR are equal (up to some ignorable constants). The result is based on the connections of both methods to linear or logistic (random-effects) regression models. Genet. Epidemiol. 35: 211-216, 2011. (c) 2011 Wiley-Liss, Inc.