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

6533b856fe1ef96bd12b26fd

RESEARCH PRODUCT

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

Jorge González-domínguez Lars Wienbrandt Bertil Schmidt Jan Christian Kässens David Ellinghaus Manfred Schimmler

subject

Computer science Bioinformatics DNA Mutational Analysis Genome-wide association study Parallel computing Polymorphism Single Nucleotide Sensitivity and Specificity Computational biology Computer Graphics Genetics Computer architecture Field-programmable gate array Random access memory Applied Mathematics Chromosome Mapping High-Throughput Nucleotide Sequencing Reproducibility of Results Field programmable gate arrays Epistasis Genetic Signal Processing Computer-Assisted Equipment Design Random access memory Computing systems Reconfigurable computing Equipment Failure Analysis Task (computing)Epistasis Host (network)Graphics processing units Genome-Wide Association Study Biotechnology

description

This is a post-peer-review, pre-copyedit version of an article published in IEEE - ACM Transactions on Computational Biology and Bioinformatics. The final authenticated version is available online at: http://dx.doi.org/10.1109/TCBB.2015.2389958 [Abstract] 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 3 GHz CPU. In this paper, we demonstrate how this task can be accelerated using a combination of fine-grained and coarse-grained 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 moderatelysized datasets and to a few hours for large-scale datasets. London. Wellcome Trust; 076113 London. Wellcome Trust; 085475

year	journal	country	edition	language
2015-01-01

10.1109/tcbb.2015.2389958 https://hdl.handle.net/2183/20968