A hierarchical clustering method for dimension reduction in joint analysis of multiple phenotypes.
A hierarchical clustering method for dimension reduction in joint analysis of multiple phenotypes.
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DOI：
10.1002/gepi.22124
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发表时间：
2018-06
影响因子：
2.1
通讯作者：
Zhang S
中科院分区：
文献类型：
--
作者：
Liang X;Sha Q;Rho Y;Zhang S
全基因组关联研究（GWAS）已成为一种非常有效的研究工具，以确定潜在的各种复杂疾病的遗传变异。尽管GWAS成功地确定了遗传变异与复杂疾病之间数千种可重复的关联，但总的来说，遗传变异与单一表型之间的关联通常很弱。越来越多的人认识到，多种表型的联合分析可能比单变量分析更强大，并且可以揭示复杂疾病的潜在生物学机制。在本文中，我们开发了一种新的变量减少方法，使用层次聚类方法（HCM）联合分析关联研究中的多种表型。所提出的方法包括两个步骤。第一步通过对每个表型簇使用代表性表型来应用降维技术。然后，在第二步中使用现有的方法来测试遗传变异与代表性表型之间的关联，而不是个体表型。我们进行了广泛的模拟研究，以比较使用HCM与不使用HCM的MANOVA， MultiPhen和TATES的功率。我们的仿真研究表明，在大多数情况下，使用HCM比不使用HCM更强大。我们还通过分析肺功能研究的全基因组基因分型数据来说明使用HCM的有用性。
Genome-wide association studies (GWAS) have become a very effective research tool to identify genetic variants of underlying various complex diseases. In spite of the success of GWAS in identifying thousands of reproducible associations between genetic variants and complex disease, in general, the association between genetic variants and a single phenotype is usually weak. It is increasingly recognized that joint analysis of multiple phenotypes can be potentially more powerful than the univariate analysis, and can shed new light on underlying biological mechanisms of complex diseases. In this paper, we develop a novel variable reduction method using hierarchical clustering method (HCM) for joint analysis of multiple phenotypes in association studies. The proposed method involves two steps. The first step applies a dimension reduction technique by using a representative phenotype for each cluster of phenotypes. Then, existing methods are used in the second step to test the association between genetic variants and the representative phenotypes rather than the individual phenotypes. We perform extensive simulation studies to compare the powers of MANOVA, MultiPhen, and TATES using HCM with those of without using HCM. Our simulation studies show that using HCM is more powerful than without using HCM in most scenarios. We also illustrate the usefulness of using HCM by analyzing a whole-genome genotyping data from a lung function study.
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