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The variance component tests used in genome-wide association studies (GWAS) including large sample sizes become computationally exhaustive when the number of genetic markers is over a few hundred thousand. We present an extremely fast variance components-based two-step method, GRAMMAR-Gamma, developed as an analytical approximation within a framework of the score test approach. Using simulated and real human GWAS data sets, we show that this method provides unbiased estimates of the SNP effect and has a power close to that of the likelihood ratio test-based method. The computational complexity of our method is close to its theoretical minimum, that is, to the complexity of the analysis that ignores genetic structure. The running time of our method linearly depends on sample size, whereas this dependency is quadratic for other existing methods. Simulations suggest that GRAMMAR-Gamma may be used for association testing in whole-genome resequencing studies of large human cohorts.

Original publication

DOI

10.1038/ng.2410

Type

Journal article

Journal

Nat Genet

Publication Date

10/2012

Volume

44

Pages

1166 - 1170

Keywords

Algorithms, Arabidopsis, Computer Simulation, Genetic Markers, Genome-Wide Association Study, Humans, Likelihood Functions, Linear Models, Models, Genetic, Normal Distribution, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Sequence Analysis, DNA