A method for analyzing multiple continuous phenotypes in rare variant association studies allowing for flexible correlations in variant effects
A method for analyzing multiple continuous phenotypes in rare variant association studies allowing for flexible correlations in variant effects
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DOI：
10.1038/ejhg.2016.8
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发表时间：
2016-09-01
影响因子：
5.2
通讯作者：
Greenwood, Celia M. T.
中科院分区：
文献类型：
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作者：
Sun, Jianping;Oualkacha, Karim;Greenwood, Celia M. T.
对于基于区域的测序数据，可以通过分析多个相关表型来提高检测遗传关联的能力。基于这一动机，我们提出了一种新的测试方法来同时检测一组罕见变异（例如通过在小基因组区域测序获得的那些变异）与多个连续表型之间的关联。我们允许表型之间的任意相关性，并通过假设变异的影响遵循具有零平均值和特定协方差矩阵结构的多元正态分布来建立线性混合模型。为了考虑变量效应协方差矩阵中未知的相关参数，推导了基于计分型统计的数据自适应方差成分检验。由于我们的方法可以解析地计算p值，因此所提出的测试程序具有计算效率。广泛的模拟和对UK10K项目的应用表明，我们提出的多变量测试通常比单变量测试更有效，特别是当存在多效性效应或高度相关的表型时。
For region-based sequencing data, power to detect genetic associations can be improved through analysis of multiple related phenotypes. With this motivation, we propose a novel test to detect association simultaneously between a set of rare variants, such as those obtained by sequencing in a small genomic region, and multiple continuous phenotypes. We allow arbitrary correlations among the phenotypes and build on a linear mixed model by assuming the effects of the variants follow a multivariate normal distribution with a zero mean and a specific covariance matrix structure. In order to account for the unknown correlation parameter in the covariance matrix of the variant effects, a data-adaptive variance component test based on scoretype statistics is derived. As our approach can calculate the P-value analytically, the proposed test procedure is computationally efficient. Broad simulations and an application to the UK10K project show that our proposed multivariate test is generally more powerful than univariate tests, especially when there are pleiotropic effects or highly correlated phenotypes.