FPGA-based Acceleration of Detecting Statistical Epistasis in GWAS

Authors:
Lars Wienbrandt, Jan Christian Kässens, Jorge González-Domínguez, Bertil Schmidt, David Ellinghaus, Manfred Schimmler
Year of publication:
2014
Volume:
29
Issue:
0
Issn:
1877-0509
Journal title abbreviated:
Procedia Computer Science
Journal title long:
Procedia Computer Science
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.