6533b85efe1ef96bd12bfc3a
RESEARCH PRODUCT
FPGA-based Acceleration of Detecting Statistical Epistasis in GWAS
Bertil SchmidtJan Christian KässensJorge González-domínguezLars WienbrandtManfred SchimmlerDavid Ellinghaussubject
epistasis020203 distributed computing0303 health sciencesXeonWorkstationComputer scienceGenome-wide association study02 engineering and technologycomputer.software_genrelaw.inventioncontingency tables03 medical and health sciencesAccelerationFPGA technologylaw0202 electrical engineering electronic engineering information engineeringGeneral Earth and Planetary SciencesEpistasisGWASData miningpairwise gene-gene interactionField-programmable gate arraycomputer030304 developmental biologyGeneral Environmental Sciencedescription
Abstract Genotype-by-genotype interactions (epistasis) are believed to be a significant source of unexplained genetic variation causing complex chronic diseases but have been ignored in genome-wide association studies (GWAS) due to the computational burden of analysis. In this work we show how to benefit from FPGA technology for highly parallel creation of contingency tables in a systolic chain with a subsequent statistical test. We present the implementation for the FPGA-based hardware platform RIVYERA S6-LX150 containing 128 Xilinx Spartan6-LX150 FPGAs. For performance evaluation we compare against the method iLOCi[9]. iLOCi claims to outperform other available tools in terms of accuracy. However, analysis of a dataset from the Wellcome Trust Case Control Consortium (WTCCC) with about 500,000 SNPs and 5,000 samples still takes about 19 hours on a MacPro workstation with two Intel Xeon quad-core CPUs, while our FPGA-based implementation requires only 4 minutes.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2014-01-01 | Procedia Computer Science |