Details

Autor(en) / Beteiligte

• Wu, Michael C.
• Lee, Seunggeun
• Cai, Tianxi
• Li, Yun
• Boehnke, Michael
• Lin, Xihong

Titel

Rare-Variant Association Testing for Sequencing Data with the Sequence Kernel Association Test

Ist Teil von

• American journal of human genetics, 2011-07, Vol.89 (1), p.82-93

Ort / Verlag

Cambridge, MA: Elsevier Inc

Erscheinungsjahr

2011

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.