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Institute of Electrical and Electronics Engineers, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 5(12), p. 982-994, 2015

DOI: 10.1109/tcbb.2015.2389958



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Parallelizing Epistasis Detection in GWAS on FPGA and GPU-Accelerated Computing Systems

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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High-throughput genotyping technologies (such as SNP-arrays) allow the rapid collection of up to a few million genetic markers of an individual. Detecting epistasis (based on 2-SNP interactions) in Genome-Wide Association Studies is an important but time consuming operation since statistical computations have to be performed for each pair of measured markers. Computational methods to detect epistasis therefore suffer from prohibitively long runtimes; e.g. processing a moderately-sized dataset consisting of about 500,000 SNPs and 5,000 samples requires several days using state-of-the-art tools on a standard 3GHz CPU. In this paper we demonstrate how this task can be accelerated using a combination of fine-grained and coarsegrained parallelism on two different computing systems. The first architecture is based on reconfigurable hardware (FPGAs) while the second architecture uses multiple GPUs connected to the same host. We show that both systems can achieve speedups of around four orders-of-magnitude compared to the sequential implementation. This significantly reduces the runtimes for detecting epistasis to only a few minutes for moderately-sized datasets and to a few hours for large-scale datasets.