GRAT is a novel two-stage testing procedure that identifies all of the significant associations more efficiently than testing all the SNPs. In the first-stage a small number of informative SNPs, or proxies, across the genome are tested. Based on their observed associations, our approach locates the regions which may contain significant SNPs and only tests additional SNPs from those regions. We show through simulations and analysis of real GWAS datasets that the proposed two-stage procedure increases the computational speed by a factor of 10. Additionally, efficient implementation of our software increases the computational speed relative to state of the art testing approaches by a factor of 75.
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J Comput Biol. 2013 Oct;20(10):817-30. doi: 10.1089/cmb.2013.0087.
Efficiently identifying significant associations in genome-wide association studies.
Kostem E, Eskin E.