Parallel pairwise epistasis detection on heterogeneous computing architectures

IEEE Transactions on Parallel and Distributed Systems

Volumn 27, Issue 8, 2016, Pages 2329-2340

  González Domínguez, Jorge ^a   Ramos, Sabela ^b   Touriño, Juan ^b   Schmidt, Bertil ^a  

^a JOHANNES GUTENBERG UNIVERSITY   (Germany)

^b UNIVERSITY OF A CORUÑA   (Spain)

Author keywords

epistasis; GPU; High performance computing; pairwise gene gene interaction; Xeon Phi

Indexed keywords

ASSOCIATION REACTIONS; BIOINFORMATICS; GENES; PARALLEL ARCHITECTURES;

COMPUTATIONAL CAPACITY; EPISTASIS; GENE-GENE INTERACTION; GENOME-WIDE ASSOCIATION STUDIES; HETEROGENEOUS CLUSTERS; HETEROGENEOUS COMPUTING; HIGH PERFORMANCE COMPUTING; XEON PHI;

COMPUTER ARCHITECTURE;

EID: 84978640092     PISSN: 10459219     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPDS.2015.2460247     Document Type: Article

References (43)

1. B. Maher, "Personal genomes: The case of the missing heritability," Nature, vol. 456, no. 7218, pp. 18-21, 2008.
2. P. C. Phillips, "Epistasis, the essential role of gene interactions in the structure and evolution of genetic systems," Nature Rev. Genetics, vol. 9, no. 11, pp. 855-867, 2008.
3. J. H. Moore, F. W. Asselbergs, and S. M. Williams, "Bioinformatics challenges for genome-wide association studies," Bioinformatics, vol. 26, no. 4, pp. 445-455, 2010.
4. H. J. Cordell, "Detecting gene-gene interactions that underlie human diseases," Nature Rev. Genetics, vol. 10, no. 6, pp. 392-404, 2009.
5. K. van Steen, "Travelling the world of gene-gene interactions," Briefings Bioinf., vol. 13, no. 1, pp. 1-19, 2011.
6. D. Sluga, T. Curk, B. Zupan, and U. Lotric, "Heterogeneous computing architecture for fast detection of SNP-SNP interactions," BMC Bioinf., vol. 15, no. 6, pp. 216:1-216:8, 2014.
7. L. Wienbrandt, J. C. Kässens, J. González-Domínguez, B. Schmidt, D. Ellinghaus, and M. Schimmler, "FPGA-based acceleration of detecting statistical epistasis in GWAS," in Proc. 14th Int. Conf. Comput. Sci., Cairns, Australia, 2014, pp. 220-230.
8. B. Goudey, D. Rawlinson, Q. Wang, F. Shi, H. Ferra, R. M. Campbell, L. Stern, M. T Inouye, C. S. Ong, and A. Kowalczyk, "GWIS-Model-free, fast and exhaustive search for epistatic interactions in case-control GWAS," BMC Genomics, vol. 14, no. 3, pp. S10:1-S10:18, 2012.
9. J. Piriyapongsa, C. Ngamphiw, A. Intarapanich, S. Kulawonganunchai, A. Assawamakin, C. Bootchai, P. J. Shaw, and S. Tongsima, "iLOCi: A SNP interaction priorization technique for detecting epistasis in genome-wide association studies," BMC Genomics, vol. 13, no. 7, pp. S2:1-S2:15, 2012.
10. L. S. Yung, C. Yang, X. Wan, and W. Yu, "GBOOST: A GPU-based tool for detecting gene-gene interactions in genome-wide case control studies," Bioinformatics, vol. 27, no. 9, pp. 1309-1310, 2011.
11. T. Kam-Thong, C. A. Azencott, L. Cayton, B. Pütz, A. Altmann, N. Karbalai, P. G. Sämann, B. Schölkopf, B. Müller-Myhsok, and K. M. Borgwardt, "GLIDE: GPU-based linear regression for detection of epistasis," Human Heredity, vol. 73, pp. 220-236, 2012.
12. G. K. Chen and Y. Guo, "Discovering epistasis in large scale genetic association studies by exploiting graphics cards," Frontiers Genetics, vol. 4, no. 266, pp. 1-12, 2013.
13. J. González-Domínguez, B. Schmidt, J. C. Kässens, and L. Wienbrandt, "Hybrid CPU/GPU acceleration of detection of 2-SNP epistatic interactions in GWAS," in Proc. 15th Int. Eur. Conf. Parallel Distrib. Comput., Porto, Portugal, 2014, pp. 680-691.
14. B. Pütz, T. Kam-Thong, N. Karbalai, A. Altmann, and B. Müller-Myhsok, "Cost-effective GPU-grid for genome-wide epistasis calculations," Methods Inf. Med., vol. 52, pp. 91-95, 2013.
15. J. González-Domínguez, J. C. Kässens, L. Wienbrandt, and B. Schmidt, "Large-scale genome-wide association studies on a GPU cluster using a CUDA-Accelerated pgas programming model," Int. J. High Perform. Comput. Appl., 2015.
16. X. Wan, C. Yang, Q. Yang, H. Xue, X. Fan, N. L. S. Tang, and W. Yu, "BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies," The Am. J. Human Genetics, vol. 87, no. 3, pp. 325-340, 2010.
17. J. González-Domínguez, L. Wienbrandt, J. Kässens, D. Ellinghaus, M. Schimmler, and B. Schmidt, "Parallelizing epistasis detection in GWAS on FPGA and GPU-Accelerated computing systems," IEEE/ACMTrans. Comput. Biol. Bioinformat., 2015, http://ieeexplore.ieee. org/xpls/abs-All.jsparnumber=7005476
18. Texas advance computing center (TACC) User Portal. Stampede User Guide [Online]. Available: https://portal.tacc.utexas.edu/user-guides/stampede, Jul. 2015.
19. The Navy DoD supercomputing resource center (Navy DSRC). High performance computing systems: Shepard [Online]. Available: http://navydsrc.hpc.mil/hardware/index.html, Jul. 2015.
20. Y. Zheng, A. Kamil, M. B. Driscoll, H. Shan, and K. Yelick, "UPC+ +: A PGAS extension for C++," in Proc. 28th IEEE Int. Parallel Distrib. Process. Symp., Phoenix, AR, USA, 2014, pp. 1105-1114.
21. C. Yang, Z. He, X. Wan, Q. Yang, H. Xue, and W. Yu, "SNPHarvester: A filtering-based approach for detecting epistatic interaction in genome-wide association studies," Bioinformatics, vol. 25, no. 4, pp. 504-511, 2009.
22. X. Zhang, S. Huang, F. Zou, and W. Wang, "TEAM: Efficient twolocus epistasis tests in human genome-wide association study," Bioinformatics, vol. 26, no. 12, pp. i217-i227, 2010.
23. J. Wu, B. Devlin, S. Ringquist, M. Trucco, and K. Roeder, "Screen and clean: A tool for identifying interactions in genome-wide association studies," Genetic Epidemiol., vol. 34, no. 3, pp. 275-285, 2010.
24. S. Prabhu and I. Peer, "Ultrafast genome-wide scan for SNP-SNP interactions in common complex disease," Genome Res., vol. 22, no. 11, pp. 2230-2240, 2012.
25. J. González-Domínguez and B. Schmidt, "GPU-Accelerated exhaustive search for third-order epistatic interactions in casecontrol studies," J. Comput. Sci., vol. 8, pp. 93-100, 2015.
26. R. L. Milne, J. Herranz, and K. Michailidou, "A large-scale assessment of two-way SNP interactions in breast cancer susceptibility using 46,450 cases and 42,461 controls fromthe breast cancer association consortium," HumanMol. Genetics, vol. 23, no. 7, pp. 1934-1946, 2014, http://hmg.oxfordjournals.org/content/23/7/1934.short
27. M. Chu, R. Zhang, and Y. Zhao, "A genome-wide gene-gene interaction analysis identifies an epistatic gene pair for lung cancer susceptibility in Han chinese," Carcinogenesis, vol. 32, no. 3, pp. 572-577, 2014, http://carcin.oxfordjournals.org/content/35/3/572.short.
28. J. Bi, J. Gelernter, J. Sun, and H. R. Kranzler, "Comparing the utility of homogeneous subtypes of cocaine use and related behaviors with DSM-IV cocaine dependence as traits for genetic association analysis," Am. J. Med. Genetics, vol. 165, no. 2, pp. 148-156, 2014.
29. Y. Wang, G. Liu, M. Feng, and L. Wong, "An empirical comparison of several recent epistatic interaction detection methods," Bioinformatics, vol. 27, no. 21, pp. 2936-2943, 2011.
30. R. Rahman, "Intel Xeon PhiTM Core Micro-Architecture," Intel Press, Santa Clara, CA, USA, 2012.
31. G. Chrysos, "Intel Xeon PhiTM Coprocessor (codename knights corner)," Keynote talk at the 24th Hot Chips: A Symposium on High Performance Chips, Cupertino, CA, USA, 2012.
32. (2014). Intel Xeon PhiTM Coprocessor: Software Developers Guide [Online]. Available: https://www-ssl.intel.com/content/www/us/en/processors/xeon/xeon-phi-coprocessor-systemsoftware-developers-guide.html
33. T. El-Ghazawi, W. Carlson, T. Sterling, and K. Yelick, UPC: Distributed Shared-Memory Programming. Hoboken, NJ, USA: Wiley, 2005.
34. C. Bell, D. Bonachaea, R. Nishtala, and K. Yelick, "Optimizing bandwidth limited problems using one-sided communication and overlap," in Proc. 20th IEEE Int. Parallel Distrib. Process. Symp., Rhodes Island, Greece, 2006, pp. 1-10.
35. R. Nishtala, Y. Zheng, P. Hargrove, and K. Yelick, "Tuning collective communication for partitioned global address space programming models," Parallel Comput., vol. 37, no. 9, pp. 576-591, 2011.
36. J. Reinders. (2012). An overview of programming for intel Xeon and Intel Xeon Phi Coprocessors [Online]. Available: https://software.intel.com/sites/default/files/article/330164/anoverview-of-programming-for-intel-xeon-processors-And-intelxeon-phi-coprocessors-1.pdf
37. S. Ramos and T. Hoefler, "Modeling communication in cachecoherent SMP systems: A case-study with xeon phi," in Proc. 22nd Int. Symp. High-Perform. Parallel Distrib. Comput., 2013, pp. 97-108.
38. S. U. Thiagarajan, C. Congdon, S. Naik, and L. Q. Nguyen. (2014). Intel Xeon PhiTM coprocessor developer's quick start guide [Online]. Available: https://software.intel.com/sites/default/files/managed/26/d6/Intel-Xeon-Phi-Quick-Start-Developers-Guide-MPSS-3.4.pdf
39. S.-H. Chan, J. Cheung, E. Wu, H. Wang, C.-M. Liu, X. Zhu, S. Peng, R. Luo, and T. W. Lam, "MICA: A fast short-read aligner that takes full advantage of intel many integrated core architecture (MIC)," arXiv preprint, 2014.
40. T. L. Edwards, W. S. Bush, S. D. Turner, S. M. Dudek, E. S. Torstenson, M. Schmidt, E. Martin, and M. D. Ritchie, "Generating linkage disequilibrium patterns in data simulations using genomesimla," in Proc. 6th Eur. Conf. Evol. Comput., Mach. Learn. Data Mining Bioinformat., Naples, Italy, 2008, pp. 736-743.
41. A. Ramachandran, J. Vienne, R. Van Der Wijngaart, L. Koesterke, and I. Sharapov, "Performance evaluation of NAS parallel benchmarks on intel xeon phi," in Proc. 42nd Int. Conf. Parallel Process., Lyon, France, 2013, pp. 763-743.
42. G. Misra, N. Kurkure, A. Das, M. Valmiki, S. Das, and A. Gupta, "Evaluation of rodinia codes on intel xeon phi," in Proc. 4th Int. Conf. Intell. Syst., Modelling Simul., Bangkok, Thailand, 2013, pp. 415-419.
43. The Wellcome Trust Case Control Consortium, "Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls," Nature, vol. 447, no. 7145, pp. 661-678, 2007.