A Powerful and Adaptive Association Test for Rare Variants
A Powerful and Adaptive Association Test for Rare Variants
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DOI：
10.1534/genetics.114.165035
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发表时间：
2014-08-01
期刊：
影响因子：
3.3
通讯作者：
Wei, Peng
中科院分区：
文献类型：
--
作者：
Pan, Wei;Kim, Junghi;Wei, Peng
本文的重点是对二元性状和一组罕见变异（RV）之间的关联进行全球测试，尽管其应用范围可以更广泛地应用于其他类型的性状，常见变异（CV）和基因集或途径分析。我们发现，许多现有的测试有恶化的性能，在许多非相关的RV的存在下：他们的权力可以显着下降的非相关的RV的比例在该组中进行测试的增加。我们提出了一类所谓的幂分数总和（SPU）测试，其中每一个都是基于一般回归模型的分数向量，因此可以处理不同类型的性状并调整协变量，例如，主成分统计人口分层。SPU检验概括了总和检验，这是一种基于RV合并或折叠基因型的代表性负担检验，以及与其他几种强大的方差分量检验密切相关的平方和（SSU）检验;先前的研究（Basu和Pan 2011）已经证明了在许多情况下总和和SSU检验之一（而不是两者）的良好性能。SPU测试是通用的，因为它们中的一个通常是强大的，尽管它的身份随着未知的真实关联参数而变化。我们提出了一个自适应SPU（aSPU）测试，以近似最强大的SPU测试，为给定的情况下，从而保持高功率，并在各种情况下高度自适应。我们进行了广泛的模拟，以显示在存在许多非关联RV的情况下，aSPU测试的上级性能优于几种最先进的关联测试。最后，我们将SPU和aSPU测试应用于GAW 17 mini-exome序列数据，以比较其实际性能与一些现有的测试，证明其潜在的有用性。
This article focuses on conducting global testing for association between a binary trait and a set of rare variants (RVs), although its application can be much broader to other types of traits, common variants (CVs), and gene set or pathway analysis. We show that many of the existing tests have deteriorating performance in the presence of many nonassociated RVs: their power can dramatically drop as the proportion of nonassociated RVs in the group to be tested increases. We propose a class of so-called sum of powered score (SPU) tests, each of which is based on the score vector from a general regression model and hence can deal with different types of traits and adjust for covariates, e.g., principal components accounting for population stratification. The SPU tests generalize the sum test, a representative burden test based on pooling or collapsing genotypes of RVs, and a sum of squared score (SSU) test that is closely related to several other powerful variance component tests; a previous study (Basu and Pan 2011) has demonstrated good performance of one, but not both, of the Sum and SSU tests in many situations. The SPU tests are versatile in the sense that one of them is often powerful, although its identity varies with the unknown true association parameters. We propose an adaptive SPU (aSPU) test to approximate the most powerful SPU test for a given scenario, consequently maintaining high power and being highly adaptive across various scenarios. We conducted extensive simulations to show superior performance of the aSPU test over several state-of-the-art association tests in the presence of many nonassociated RVs. Finally we applied the SPU and aSPU tests to the GAW17 mini-exome sequence data to compare its practical performance with some existing tests, demonstrating their potential usefulness.