In the past few years massive amounts of data have been generated for genetic analysis. Existing solutions to analyze this data concerning genome-wide gene interactions are either not powerful enough or can barely be managed with standard computers due to the tremendous amount of statistical tests to be performed. Also, common approaches using cluster or cloud technologies for parallel analysis are operating at the edge of what is currently possible. This work demonstrates how FPGAs are able to address this problem. We present a highly parallel, hardware oriented solution for genome-wide association interaction studies (GWAIS) with MB-MDR and the maxT multiple testing correction on an FPGA-based architecture. We achieve a more than 300-fold speedup over an AMD Opteron cluster with 160 cores on an FPGA-system equipped with 128 Xilinx Spartan6 LX150 low-cost FPGAs when analyzing a WTCCC-like dataset with 500,000 markers and 5,000 samples. Furthermore, we are able to keep pace with a 256-core Intel Xeon cluster running MB-MDR 4.2.2 with an approximative version of maxT, while we achieve a 190-fold speedup over the sequential execution of this version on one Xeon core.
Gundlach, S., Kässens, J. C., & Wienbrandt, L. (2016). Genome-wide association interaction studies with MB-MDR and maxT multiple testing correction on FPGAs. In Procedia Computer Science (Vol. 80, pp. 639–649). Elsevier B.V. https://doi.org/10.1016/j.procs.2016.05.354