Title: Multifactor dimensionality reduction for graphics processing units enables genome-wide testing of epistasis in sporadic ALS.
Authors: Greene, Casey S; Sinnott-Armstrong, Nicholas A; Himmelstein, Daniel S; Park, Paul J; Moore, Jason H; Harris, Brent T
Published In Bioinformatics, (2010 Mar 1)
Abstract: 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.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.MDRGPU is open source and available free of charge from http://www.sourceforge.net/projects/mdr.
PubMed ID: 20081222
MeSH Terms: Amyotrophic Lateral Sclerosis/genetics*; Databases, Genetic; Epistasis, Genetic*; Genome, Human; Genome-Wide Association Study/methods*; Genomics/methods; Humans; Polymorphism, Single Nucleotide