Journal Article

Multifactor dimensionality reduction for graphics processing units enables genome-wide testing of epistasis in sporadic ALS

Casey S. Greene, Nicholas A. Sinnott-Armstrong, Daniel S. Himmelstein, Paul J. Park, Jason H. Moore and Brent T. Harris

in Bioinformatics

Volume 26, issue 5, pages 694-695
Published in print March 2010 | ISSN: 1367-4803
Published online January 2010 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btq009

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Motivation: Epistasis, the presence of gene–gene interactions, has been hypothesized to be at the root of many common human diseases, but current genome-wide association studies largely ignore its role. Multifactor dimensionality reduction (MDR) is a powerful model-free method for detecting epistatic relationships between genes, but computational costs have made its application to genome-wide data difficult. Graphics processing units (GPUs), the hardware responsible for rendering computer games, are powerful parallel processors. Using GPUs to run MDR on a genome-wide dataset allows for statistically rigorous testing of epistasis.

Results: The implementation of MDR for GPUs (MDRGPU) includes core features of the widely used Java software package, MDR. This GPU implementation allows for large-scale analysis of epistasis at a dramatically lower cost than the standard CPU-based implementations. As a proof-of-concept, we applied this software to a genome-wide study of sporadic amyotrophic lateral sclerosis (ALS). We discovered a statistically significant two-SNP classifier and subsequently replicated the significance of these two SNPs in an independent study of ALS. MDRGPU makes the large-scale analysis of epistasis tractable and opens the door to statistically rigorous testing of interactions in genome-wide datasets.

Availability: MDRGPU is open source and available free of charge from http://www.sourceforge.net/projects/mdr.

Contact: jason.h.moore@dartmouth.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  1425 words. 

Subjects: Bioinformatics and Computational Biology

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