Rare-variant association testing for sequencing data with the sequence kernel association test.
paper
Cited
Public
Unavailable
- Authors
- Wu, Michael C; Lee, Seunggeun; Cai, Tianxi; Li, Yun; Boehnke, Michael; Lin, Xihong
- Year
- 2011
- Journal
- American journal of human genetics
- PMID
- 21737059
- DOI
- 10.1016/j.ajhg.2011.05.029
- PMCID
- PMC3135811
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.
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