Association tests using kernel-based measures of multi-locus genotype similarity between individuals.
Association tests using kernel-based measures of multi-locus genotype similarity between individuals.
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DOI：
10.1002/gepi.20451
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发表时间：
2010-04
影响因子：
2.1
通讯作者：
Thalamuthu, Anbupalam
中科院分区：
文献类型：
--
作者：
Mukhopadhyay, Indranil;Feingold, Eleanor;Weeks, Daniel E.;Thalamuthu, Anbupalam
在遗传关联研究中，通常希望对基因中的任何或所有单核苷酸多态(SNPs)是否与表型相关进行全面测试。有几个这样的测试，但它们中的大多数只有在关于单个SNPs的遗传效应的非常具体的假设下才有效。此外，现有的一些测试假设每个SNP的影响方向是已知的，这是一种极不可能的情况。本文提出了一种新的基于核的多个SNPs联合关联测试方法(KBAT)。我们的检验是非参数的和稳健的，并且没有对单个SNP效应的方向做出任何假设。它可以用来测试一个基因中多个相关的SNPs，也可以用来测试独立的SNPs或生物途径中的基因。我们的测试使用方差分析(ANOVA)范式来比较病例和对照之间的差异与组内的差异。使用每个标记的核函数来测量变异，然后构建复合统计量以将这些标记组合成单个测试。我们给出了仿真结果，并与Schaid等人基于U-统计量的方法进行了比较。Wessel和Schork的另一项统计数据。我们考虑了各种不同的疾病模型和假设，即基因中有多少SNP实际上与疾病相关。我们的结果表明，在最现实的情况下，我们的统计比其他统计具有更高的能力。
In a genetic association study, it is often desirable to perform an overall test of whether any or all single-nucleotide polymorphisms (SNPs) in a gene are associated with a phenotype. Several such tests exist, but most of them are powerful only under very specific assumptions about the genetic effects of the individual SNPs. In addition, some of the existing tests assume that the direction of the effect of each SNP is known, which is a highly unlikely scenario. Here we propose a new kernel-based association test (KBAT) of joint association of several SNPs. Our test is non-parametric and robust, and does not make any assumption about the directions of individual SNP effects. It can be used to test multiple correlated SNPs within a gene and can also be used to test independent SNPs or genes in a biological pathway. Our test uses an analysis of variance (ANOVA) paradigm to compare variation between cases and controls to the variation within the groups. The variation is measured using kernel functions for each marker, and then a composite statistic is constructed to combine the markers into a single test. We present simulation results comparing our statistic to the U-statistic based method by Schaid et al. and another statistic by Wessel and Schork. We consider a variety of different disease models and assumptions about how many SNPs within the gene are actually associated with disease. Our results indicate that our statistic has higher power than other statistics under most realistic conditions.
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影响因子：
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作者：
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通讯作者：
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通讯作者：
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