11 However, standard methods used to test for association with single common genetic variants are underpowered for rare variants unless sample sizes or effect sizes are very large. Rare genetic variants, here defined as alleles with a frequency less than 1%–5%, can play key roles in influencing complex disease and traits. 5 Although considerable resources have been devoted to sequence mapping and genotype calling, 6–9 successful application of sequencing to the study of complex traits requires novel statistical methods that allow researchers to test efficiently for association given data on rare variants 10 and to perform sample-size and power calculations to help design sequencing-based association studies. The advent of massively parallel sequencing 2 has transformed human genetics 3,4 and has the potential to explain some of this missing heritability through identification of trait-associated rare variants.
Genome-wide association studies (GWASs) have identified more than 1000 genetic loci associated with many human diseases and traits, 1 yet common variants identified through GWASs often explain only a small proportion of trait heritability. We also provide analytic power and sample-size calculations to help design candidate-gene, whole-exome, and whole-genome sequence association studies. 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. 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. 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.
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. The limited power of classical single-marker association analysis for rare variants poses a central challenge in such studies. Sequencing studies are increasingly being conducted to identify rare variants associated with complex traits.
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