Joint analysis of multiple phenotypes in association studies using allele-based clustering approach for non-normal distributions.
Joint analysis of multiple phenotypes in association studies using allele-based clustering approach for non-normal distributions.
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DOI：
10.1111/ahg.12260
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发表时间：
2018-11
影响因子：
1.9
通讯作者：
Zhang S
中科院分区：
文献类型：
--
作者：
Liang X;Sha Q;Zhang S
在复杂疾病的研究中，通常要测量几个相关的表型。也有越来越多的证据表明，联合测试单核苷酸多态(SNP)和多依赖表型之间的关联通常比一次只分析一种表型更有效。因此，开发统计方法来测试与多种表型的遗传关联变得越来越重要。在本文中，我们发展了一种基于等位基因的聚类法(ACA)，用于关联研究中多个非正常表型的联合分析。在ACA中，我们将感兴趣的SNP上的等位基因视为两类因变量，并将相关的表型作为预测因子来预测感兴趣的SNP上的等位基因。我们进行了大量的仿真研究来评估ACA的性能，并将其与自适应Fisher组合测试(AFC)、使用扩展SIMES过程的基于特征的联想测试(Tates)、Fisher组合测试(FC)、标准Manova和多表型联合模型(MultiPhen)的能力进行了比较。仿真研究表明，该方法具有正确的I类误码率，并且在某些非正态分布情况下比其他方法具有更好的性能。
In the study of complex diseases, several correlated phenotypes are usually measured. There is also increasing evidence showing that testing the association between a single-nucleotide polymorphism (SNP) and multiple-dependent phenotypes jointly is often more powerful than analyzing only one phenotype at a time. Therefore, developing statistical methods to test for genetic association with multiple phenotypes has become increasingly important. In this paper, we develop an Allele-based Clustering Approach (ACA) for the joint analysis of multiple non-normal phenotypes in association studies. In ACA, we consider the alleles at a SNP of interest as a dependent variable with two classes, and the correlated phenotypes as predictors to predict the alleles at the SNP of interest. We perform extensive simulation studies to evaluate the performance of ACA and compare the power of ACA with the powers of Adaptive Fisher’s Combination test (AFC), Trait-based Association Test that uses Extended Simes procedure (TATES), Fisher’s Combination test (FC), the standard MANOVA, and the joint model of Multiple Phenotypes (MultiPhen). Our simulation studies show that the proposed method has correct type I error rates and is much more powerful than other methods for some non-normal distributions.
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