title: Rare-variant association testing for sequencing data with the sequence kernel association test.
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.
authors: Department of Biostatistics, The University of North Carolina at Chapel Hill, 27599, USA.
authors: Michael C
authors: MC
authors: Wu
authors: Seunggeun
authors: S
authors: Lee
authors: Tianxi
authors: T
authors: Cai
authors: Yun
authors: Y
authors: Li
authors: Michael
authors: M
authors: Boehnke
authors: Xihong
authors: X
authors: Lin
mesh_terms: D003198:Computer Simulation; D030541:Databases, Genetic; D005787:Gene Frequency; D056726:Genetic Association Studies / Q000379:methods*; D056426:Genetic Loci; D014644:Genetic Variation; D006801:Humans; D008957:Models, Genetic; D017421:Sequence Analysis / Q000379:methods*; D012984:Software