EpiGPU: Exhaustive pairwise epistasis scans parallelised on consumer level graphics cards

G. Hemani, A. Theocharidis, Wenhua Wei, C. Haley

Research output: Contribution to journalArticlepeer-review

Abstract

MOTIVATION: Hundreds of GWASs have been performed over the last decade, but as 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 two-dimensional search grid across the multi-core 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 - an open source library designed to run on any commercially available GPU, and on any operating system. AVAILABILITY: The software is free, open source, platform independent, and GPU-vendor independent. It can be downloaded from http://sourceforge.net/projects/epigpu/ CONTACT: gib.hemani@roslin.ed.ac.uk.
Original languageEnglish
Pages (from-to)1462-1465
JournalBioinformatics
Volume27
Issue number11
DOIs
Publication statusPublished - 2011

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