Rare-variant association testing for sequencing data with the sequence kernel association test |
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Authors: | Wu Michael C Lee Seunggeun Cai Tianxi Li Yun Boehnke Michael Lin Xihong |
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Institution: | 1Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;2Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA;3Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;4Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA |
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Abstract: | 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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