Optimal tests for rare variant effects in sequencing association studies
Optimal tests for rare variant effects in sequencing association studies
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DOI：
10.1093/biostatistics/kxs014
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发表时间：
2012-09-01
期刊：
影响因子：
2.1
通讯作者：
Lin, Xihong
中科院分区：
文献类型：
--
作者：
Lee, Seunggeun;Wu, Michael C.;Lin, Xihong
随着大规模并行测序技术的发展，非常需要开发强大的罕见变异关联测试。常见的方法包括负担和非负担测试。负荷检验假设靶区域中的所有罕见变异对表型具有相同方向和相似幅度的影响。最近提出的序列核关联测试（SKAT）（吴，M。C.的方法，及其他，2011年。使用SKAT对测序数据进行罕见变异关联检验。The American Journal of Human Genetics 89，82-93]，C-α测试的扩展（Neale，B. M.，及其他，2011年。测试罕见变异的不寻常分布。PLoS Genetics 7，161-165]提供了一种稳健的测试，其在保护性和有害变体以及无效变体的存在下特别有效，但是当区域中的大量变体是因果性的并且在相同方向上时，其不如负荷测试有效。由于潜在的生物学机制在实践中是未知的，并且在整个基因组中从一个基因到另一个基因是不同的，因此开发一种对两种情况都是最佳的测试具有实质性的实际意义。在本文中，我们提出了一类测试，包括负担测试和SKAT作为特殊情况下，并得出最佳的测试，在这个类中，最大限度地提高功率。我们表明，这种最佳的测试优于负担测试和SKAT在广泛的情况下。使用模拟研究和来自达拉斯心脏研究的甘油三酯数据来说明结果。此外，我们推导了SKAT的样本量/功效计算公式，并引入了一个新的核函数族，以便于设计新的序列关联研究。
With development of massively parallel sequencing technologies, there is a substantial need for developing powerful rare variant association tests. Common approaches include burden and non-burden tests. Burden tests assume all rare variants in the target region have effects on the phenotype in the same direction and of similar magnitude. The recently proposed sequence kernel association test (SKAT) (Wu, M. C., and others, 2011. Rare-variant association testing for sequencing data with the SKAT. The American Journal of Human Genetics 89, 82-93], an extension of the C-alpha test (Neale, B. M., and others, 2011. Testing for an unusual distribution of rare variants. PLoS Genetics 7, 161-165], provides a robust test that is particularly powerful in the presence of protective and deleterious variants and null variants, but is less powerful than burden tests when a large number of variants in a region are causal and in the same direction. As the underlying biological mechanisms are unknown in practice and vary from one gene to another across the genome, it is of substantial practical interest to develop a test that is optimal for both scenarios. In this paper, we propose a class of tests that include burden tests and SKAT as special cases, and derive an optimal test within this class that maximizes power. We show that this optimal test outperforms burden tests and SKAT in a wide range of scenarios. The results are illustrated using simulation studies and triglyceride data from the Dallas Heart Study. In addition, we have derived sample size/power calculation formula for SKAT with a new family of kernels to facilitate designing new sequence association studies.