USAT: A Unified Score-Based Association Test for Multiple Phenotype-Genotype Analysis.
USAT: A Unified Score-Based Association Test for Multiple Phenotype-Genotype Analysis.
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DOI：
10.1002/gepi.21937
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发表时间：
2016-01
影响因子：
2.1
通讯作者：
Basu S
中科院分区：
文献类型：
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作者：
Ray D;Pankow JS;Basu S
复杂疾病的全基因组关联研究（GWASs）通常收集多个相关内表型的数据。这些相关表型的多变量分析可以提高检测遗传变异的能力。多元方差分析（MANOVA）可以在GWAS水平上进行这种关联分析，但MANOVA在不同性状模型下的行为尚未得到仔细研究。在本文中，我们表明，方差分析通常是非常强大的检测关联，但也有一些情况，比如当一个遗传变异与所有性状相关时，方差分析可能没有任何检测能力。然而，在这些情况下，基于边际模型的方法比多变量方法表现得更好。我们从理论和模拟两方面研究了MANOVA的行为，并推导了MANOVA失去功率的条件。基于我们的发现，我们提出了一个统一的基于分数的测试统计量USAT，它在这种情况下比MANOVA表现得更好，在其他情况下几乎和MANOVA一样好。我们提出的测试报告了一个近似的渐近p值，并且在GWAS水平上计算非常有效地实现。我们通过大量的模拟研究了USAT、MANOVA和其他现有方法的性能，并证明了使用USAT方法检测遗传变异和多变量表型之间的关联的优势。我们将USAT应用于从社区动脉粥样硬化风险（ARIC）研究中收集的5816名白种人个体的三个相关特征数据，并发现了一些有趣的关联。
Genome-wide Association Studies (GWASs) for complex diseases often collect data on multiple correlated endo-phenotypes. Multivariate analysis of these correlated phenotypes can improve the power to detect genetic variants. Multivariate analysis of variance (MANOVA) can perform such association analysis at a GWAS level, but the behavior of MANOVA under different trait models has not been carefully investigated. In this paper, we show that MANOVA is generally very powerful for detecting association but there are situations, such as when a genetic variant is associated with all the traits, where MANOVA may not have any detection power. In these situations, marginal model based methods, however, perform much better than multivariate methods. We investigate the behavior of MANOVA, both theoretically and using simulations, and derive the conditions where MANOVA loses power. Based on our findings, we propose a unified score-based test statistic USAT that can perform better than MANOVA in such situations and nearly as well as MANOVA elsewhere. Our proposed test reports an approximate asymptotic p-value for association and is computationally very efficient to implement at a GWAS level. We have studied through extensive simulations the performance of USAT, MANOVA and other existing approaches and demonstrated the advantage of using the USAT approach to detect association between a genetic variant and multivariate phenotypes. We applied USAT to data from three correlated traits collected on 5, 816 Caucasian individuals from the Atherosclerosis Risk in Communities (ARIC,) Study and detected some interesting associations.