More and more rare genetic variants are being detected in the human genome, and it is believed that besides common variants, some rare variants also explain part of the phenotypic variance for human diseases. Due to the importance of rare variants, many statistical methods have been proposed to test for associations between rare variants and human traits. However, in existing studies, most methods only test for associations between multiple loci and one trait; therefore, the joint information of multiple traits has not been considered simultaneously and sufficiently. In this article, we present a study of testing for associations between rare variants and multiple traits, where trait value can be binary, ordinal, quantitative and/or any mixture of them. Based on the method of generalized Kendall’s τ, a nonparametric method called NM-RV is proposed. A new kernel function for U-statistic, which could incorporate the information of each rare variant itself, is also presented and is expected to enhance the power of rare variant analysis. We further consider the asymptotic distribution of the proposed association test statistic. Our simulation work suggests that the proposed method is more powerful and robust than existing methods in testing for associations between rare variants and multiple traits,especially for multivariate ordinal traits.

中文翻译：

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一种测试罕见变异和多个性状之间关联的非参数方法。

越来越多的罕见基因变异在人类基因组中被发现，人们相信除了常见变异之外，一些罕见变异也解释了人类疾病的部分表型变异。由于罕见变异的重要性，人们提出了许多统计方法来测试罕见变异与人类特征之间的关联。然而，在现有研究中，大多数方法仅测试多个基因座与一个性状之间的关联；因此，多种性状的联合信息没有得到同时、充分的考虑。在本文中，我们提出了一项测试罕见变异和多个性状之间关联的研究，其中性状值可以是二元的、序数的、定量的和/或它们的任意混合。基于广义Kendall's τ方法，提出了一种称为NM-RV的非参数方法。还提出了一种新的 U 统计核函数，可以合并每个稀有变异本身的信息，预计将增强稀有变异分析的能力。我们进一步考虑所提出的关联检验统计量的渐近分布。我们的模拟工作表明，在测试稀有变异和多个性状之间的关联（特别是对于多变量序数性状）方面，所提出的方法比现有方法更强大、更稳健。