Rare-variant association testing for sequencing data with the sequence kernel association test

Am J Hum Genet. 2011 Jul 15;89(1):82-93. doi: 10.1016/j.ajhg.2011.05.029. Epub 2011 Jul 7.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computer Simulation
  • Databases, Genetic*
  • Gene Frequency
  • Genetic Association Studies / methods*
  • Genetic Loci
  • Genetic Variation*
  • Humans
  • Models, Genetic
  • Sequence Analysis / methods*
  • Software*