Department of Biostatistics, Columbia University, New York, NY 10032, USA.
Am J Hum Genet. 2013 Jun 6;92(6):841-53. doi: 10.1016/j.ajhg.2013.04.015. Epub 2013 May 16.
Recent developments in sequencing technologies have made it possible to uncover both rare and common genetic variants. Genome-wide association studies (GWASs) can test for the effect of common variants, whereas sequence-based association studies can evaluate the cumulative effect of both rare and common variants on disease risk. Many groupwise association tests, including burden tests and variance-component tests, have been proposed for this purpose. Although such tests do not exclude common variants from their evaluation, they focus mostly on testing the effect of rare variants by upweighting rare-variant effects and downweighting common-variant effects and can therefore lose substantial power when both rare and common genetic variants in a region influence trait susceptibility. There is increasing evidence that the allelic spectrum of risk variants at a given locus might include novel, rare, low-frequency, and common genetic variants. Here, we introduce several sequence kernel association tests to evaluate the cumulative effect of rare and common variants. The proposed tests are computationally efficient and are applicable to both binary and continuous traits. Furthermore, they can readily combine GWAS and whole-exome-sequencing data on the same individuals, when available, and are also applicable to deep-resequencing data of GWAS loci. We evaluate these tests on data simulated under comprehensive scenarios and show that compared with the most commonly used tests, including the burden and variance-component tests, they can achieve substantial increases in power. We next show applications to sequencing studies for Crohn disease and autism spectrum disorders. The proposed tests have been incorporated into the software package SKAT.
近年来,测序技术的发展使得揭示罕见和常见遗传变异成为可能。全基因组关联研究(GWAS)可以检测常见变异的影响,而基于序列的关联研究可以评估罕见和常见变异对疾病风险的累积影响。为此,已经提出了许多组间关联测试,包括负担测试和方差分量测试。尽管这些测试在评估时不排除常见变异,但它们主要侧重于通过加权罕见变异效应和减轻常见变异效应来检测罕见变异的效应,因此当一个区域中的罕见和常见遗传变异都影响性状易感性时,它们会失去相当大的效力。越来越多的证据表明,给定基因座的风险变异等位基因谱可能包括新的、罕见的、低频的和常见的遗传变异。在这里,我们介绍了几种序列核关联测试来评估罕见和常见变异的累积效应。所提出的测试计算效率高,适用于二分类和连续性状。此外,当可利用时,它们可以很容易地将 GWAS 和全外显子组测序数据组合在同一组个体上,并且也适用于 GWAS 基因座的深度重测序数据。我们在综合场景下模拟的数据上评估了这些测试,并表明与最常用的测试(包括负担和方差分量测试)相比,它们可以显著提高效力。接下来,我们展示了它们在克罗恩病和自闭症谱系障碍测序研究中的应用。所提出的测试已被纳入 SKAT 软件包中。