Combining GPU and FPGA technology for efficient exhaustive interaction analysis in GWAS

Interaction between genes has become a major topic in quantitative genetics. It is believed that these interactions play a significant role in genetic variations causing complex diseases. Due to the number of tests required for an exhaustive search in genome-wide association studies (GWAS), a large amount of computational power is required. In this paper, we present a hybrid architecture consisting of tightly interconnected CPUs, GPUs and FPGAs and a fine-tuned software suite to outperform other implementations in pairwise interaction analysis while consuming less than 300Watts and fitting into a standard desktop computer case.

[1]  Happy Gogoi.  PROGRAMMING LANGUAGE C , 2015 .

[2]  Qiang Yang,et al.  BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies , 2010, American journal of human genetics.

[3]  Chris S. Haley,et al.  EpiGPU: exhaustive pairwise epistasis scans parallelized on consumer level graphics cards , 2011, Bioinform..

[4]  Ra Inta,et al.  The "Chimera": An Off-The-Shelf CPU/GPGPU/FPGA Hybrid Computing Platform , 2012, Int. J. Reconfigurable Comput..

[5]  I. Pe’er,et al.  Ultrafast genome-wide scan for SNP–SNP interactions in common complex disease , 2012, Genome research.

[6]  Qiang Yang,et al.  SNPHarvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies , 2009, Bioinform..

[7]  Yann Thoma,et al.  FPGA-GPU communicating through PCIe , 2015, Microprocess. Microsystems.

[8]  Cheng Soon Ong,et al.  GWIS - model-free, fast and exhaustive search for epistatic interactions in case-control GWAS , 2013, BMC Genomics.

[9]  Bertil Schmidt,et al.  FPGA-based Acceleration of Detecting Statistical Epistasis in GWAS , 2014, ICCS.

[10]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[11]  J. Piriyapongsa,et al.  iLOCi: a SNP interaction prioritization technique for detecting epistasis in genome-wide association studies , 2012, BMC Genomics.

[12]  Bruno da Silva,et al.  Comparing and combining GPU and FPGA accelerators in an image processing context , 2013, 2013 23rd International Conference on Field programmable Logic and Applications.

[13]  Simon C. Potter,et al.  Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls , 2007, Nature.

[14]  Sabela Ramos,et al.  Parallel Pairwise Epistasis Detection on Heterogeneous Computing Architectures , 2016, IEEE Transactions on Parallel and Distributed Systems.

[15]  Can Yang,et al.  GBOOST: a GPU-based tool for detecting gene-gene interactions in genome-wide case control studies , 2011, Bioinform..

[16]  Bertil Schmidt,et al.  Parallelizing Epistasis Detection in GWAS on FPGA and GPU-Accelerated Computing Systems , 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[17]  Pingfan Meng,et al.  FPGA-GPU-CPU heterogenous architecture for real-time cardiac physiological optical mapping , 2012, 2012 International Conference on Field-Programmable Technology.

[18]  Bertil Schmidt,et al.  UPC++ for bioinformatics: A case study using genome-wide association studies , 2014, 2014 IEEE International Conference on Cluster Computing (CLUSTER).

[19]  B. R. Barsdell,et al.  A Scalable Hybrid FPGA/GPU FX Correlator , 2014, 1401.8288.

[20]  Наталія Ігорівна Муліна,et al.  Programming language C , 2013 .

[21]  Jason H. Moore,et al.  BIOINFORMATICS REVIEW , 2005 .

[22]  Xiang Zhang,et al.  TEAM: efficient two-locus epistasis tests in human genome-wide association study , 2010, Bioinform..

[23]  Bertil Schmidt,et al.  High-speed exhaustive 3-locus interaction epistasis analysis on FPGAs , 2015, J. Comput. Sci..

[24]  Bertil Schmidt,et al.  Hybrid CPU/GPU Acceleration of Detection of 2-SNP Epistatic Interactions in GWAS , 2014, Euro-Par.

[25]  B. Maher.  Personal genomes: The case of the missing heritability , 2008, Nature.

[26]  Kevin Skadron,et al.  Accelerating Compute-Intensive Applications with GPUs and FPGAs , 2008, 2008 Symposium on Application Specific Processors.