Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
Biostatistics. 2012 Sep;13(4):762-75. doi: 10.1093/biostatistics/kxs014. Epub 2012 Jun 14.
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.
随着大规模并行测序技术的发展，开发强大的罕见变异关联测试的需求也在不断增加。常见的方法包括负担测试和非负担测试。负担测试假设目标区域中的所有罕见变异都以相同的方向和相似的幅度对表型产生影响。最近提出的序列核关联测试（SKAT）（Wu，MC，等，2011。用于 SKAT 的测序数据的罕见变异关联测试。美国人类遗传学杂志 89,82-93]，C-alpha 测试的扩展（Neale，BM，等，2011。测试罕见变异的异常分布。PLoS Genetics 7,161-165]，提供了一种强大的测试方法，特别是在存在保护和有害变异和无效变异的情况下，但是当一个区域中的大量变异是因果关系且方向相同时，其功效不如负担测试。由于在实践中未知潜在的生物学机制，并且在整个基因组中因基因而异，因此开发一种针对这两种情况都最佳的测试方法具有重要的实际意义。在本文中，我们提出了一类测试方法，其中包括负担测试和 SKAT 作为特例，并在该类中推导出一种最优测试方法，该方法可最大程度地提高功效。我们表明，在广泛的情况下，这种最优测试方法优于负担测试和 SKAT。我们使用模拟研究和达拉斯心脏研究中的甘油三酯数据来说明结果。此外，我们还针对具有新核家族的 SKAT 推导出了样本量/功效计算公式，以方便设计新的序列关联研究。
