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EpiGPU

Gibran Hemani, Athanasios Theocharidis, Wenhua Wei, Chris Haley
Division of Genetics and Genomics, The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK
Bioinformatics, Volume 27 Issue 11, June 2011

@article{Hemani:2011:EPI:1992955.1992971,

   author={Hemani, Gibran and Theocharidis, Athanasios and Wei, Wenhua and Haley, Chris},

   title={EpiGPU},

   journal={Bioinformatics},

   issue_date={June 2011},

   volume={27},

   issue={11},

   month={June},

   year={2011},

   issn={1367-4803},

   pages={1462–1465},

   numpages={4},

   url={http://dx.doi.org/10.1093/bioinformatics/btr172},

   doi={http://dx.doi.org/10.1093/bioinformatics/btr172},

   acmid={1992971},

   publisher={Oxford University Press},

   address={Oxford, UK}

}

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MOTIVATION: Hundreds of genome-wide association studies have been performed over the last decade, but as single nucleotide polymorphism (SNP) chip density has increased so has the computational burden to search for epistasis [for n SNPs the computational time resource is O(n(n-1)/2)]. While the theoretical contribution of epistasis toward phenotypes of medical and economic importance is widely discussed, empirical evidence is conspicuously absent because its analysis is often computationally prohibitive. To facilitate resolution in this field, tools must be made available that can render the search for epistasis universally viable in terms of hardware availability, cost and computational time. RESULTS: By partitioning the 2D search grid across the multicore architecture of a modern consumer graphics processing unit (GPU), we report a 92x increase in the speed of an exhaustive pairwise epistasis scan for a quantitative phenotype, and we expect the speed to increase as graphics cards continue to improve. To achieve a comparable computational improvement without a graphics card would require a large compute-cluster, an option that is often financially non-viable. The implementation presented uses OpenCL
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