Sequencing studies are increasingly being conducted to identify rare variants associated with complex traits. The limited power of classical single-marker association analysis for rare variants poses a central challenge in such studies. We propose the sequence kernel association test (SKAT), a supervised, flexible, computationally efficient regression method to test for association between genetic variants (common and rare) in a region and a continuous or dichotomous trait while easily adjusting for covariates. As a score-based variance-component test, SKAT can quickly calculate p values analytically by fitting the null model containing only the covariates, and so can easily be applied to genome-wide data. Using SKAT to analyze a genome-wide sequencing study of 1000 individuals, by segmenting the whole genome into 30 kb regions, requires only 7 hr on a laptop. Through analysis of simulated data across a wide range of practical scenarios and triglyceride data from the Dallas Heart Study, we show that SKAT can substantially outperform several alternative rare-variant association tests. We also provide analytic power and sample-size calculations to help design candidate-gene, whole-exome, and whole-genome sequence association studies.

中文翻译：

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使用序列核关联测试对测序数据进行稀有变体关联测试。

越来越多地进行测序研究以识别与复杂性状相关的罕见变异。对罕见变异的经典单标记关联分析的有限能力在此类研究中构成了核心挑战。我们提出了序列核关联测试 (SKAT)，这是一种有监督的、灵活的、计算效率高的回归方法，用于测试区域中的遗传变异（常见和罕见）与连续或二分性状之间的关联，同时轻松调整协变量。作为基于分数的方差分量测试，SKAT 可以通过拟合仅包含协变量的空模型快速分析计算 p 值，因此可以轻松应用于全基因组数据。使用 SKAT 分析 1000 个人的全基因组测序研究，将整个基因组分割成 30 kb 的区域，在笔记本电脑上只需要 7 小时。通过对来自达拉斯心脏研究的各种实际场景的模拟数据和甘油三酯数据的分析，我们表明 SKAT 可以大大优于几种替代的稀有变异关联测试。我们还提供分析能力和样本量计算，以帮助设计候选基因、全外显子组和全基因组序列关联研究。