Cluster Computing

, Volume 20, Issue 3, pp 1899–1908 | Cite as

Speed and accuracy improvement of higher-order epistasis detection on CUDA-enabled GPUs

  • Daniel Jünger
  • Christian Hundt
  • Jorge González DomínguezEmail author
  • Bertil Schmidt


The discovery of higher-order epistatic interactions is an important task in the field of genome wide association studies which allows for the identification of complex interaction patterns between multiple genetic markers. Some existing bruteforce approaches explore the whole space of k-interactions in an exhaustive manner resulting in almost intractable execution times. Computational cost can be reduced drastically by restricting the search space with suitable preprocessing filters which prune unpromising candidates. Other approaches mitigate the execution time by employing massively parallel accelerators in order to benefit from the vast computational resources of these architectures. In this paper, we combine a novel preprocessing filter, namely SingleMI, with massively parallel computation on modern GPUs to further accelerate epistasis discovery. Our implementation improves both the runtime and accuracy when compared to a previous GPU counterpart that employs mutual information clustering for prefiltering. SingleMI is open source software and publicly available at:


Genome wide association studies Epistasis detection Genomics CUDA GPU 


  1. 1.
    Buckles, B.P., Lybanon, M.: Algorithm 515: generation of a vector from the lexicographical index [G6]. ACM Trans. Math. Softw. 3(2), 180–182 (1977)CrossRefGoogle Scholar
  2. 2.
    Cattaert, T., Calle, M.L., Dudek, S.M., Hohn, J.M., Lishout, F.V., Urrea, V., Ritchie, M.D., Steel, K.V.: Model-based multifactor dimensionality reduction for detecting epistasis in case-control data in the presence of noise. Ann. Hum. Genet. 75(1), 78–89 (2011)CrossRefGoogle Scholar
  3. 3.
    Cordell, H.J.: Epistasis: what it means, what it doesn’t mean, and statistical methods to detect it in humans. Hum. Mol. Genet. 11(20), 2463–2468 (2002)CrossRefGoogle Scholar
  4. 4.
    Cordell, H.J.: Detecting gene-gene interactions that underlie human diseases. Nat. Rev. Genet. 10(6), 392–404 (2009)CrossRefGoogle Scholar
  5. 5.
    Culverhouse, R.: The use of the restricted partition method with case-control data. Hum. Hered. 63(2), 93–100 (2007)CrossRefGoogle Scholar
  6. 6.
    Duane Merrill, N.C.: CUB documentation. (2016)
  7. 7.
    Easton, D.F., Pooley, K.A., et al.: Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 447(7148), 1087–1093 (2007)CrossRefGoogle Scholar
  8. 8.
    Frayling, T.M., Timpson, N.J., et al.: A common variant in the fto gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316(5826), 889–894 (2007)CrossRefGoogle Scholar
  9. 9.
    González-Domínguez, J., Schmidt, B.: GPU-accelerated exhaustive search for third-order epistatic interactions in case-control studies. J. Comput. Sci. 8, 93–100 (2015)CrossRefGoogle Scholar
  10. 10.
    González-Domínguez, J., Ramos, S., Touriño, J., Schmidt, B.: Parallel pairwise epistasis detection on heterogeneous computing architectures. IEEE Trans. Parallel Distrib. Syst. 27(8), 2329–2340 (2016)CrossRefGoogle Scholar
  11. 11.
    Goudey, B., Abedini, M., Hopper, J.L., Inouye, M., Makalic, E., Schmidt, D.F., Wagner, J., Zhou, Z., Zobel, J., Reumann, M.: High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in genome wide association studies. Health Inf. Sci. Syst. 3(Suppl 1), S3 (2015)CrossRefGoogle Scholar
  12. 12.
    Gui, J., Andrew, A.S., Andrews, P., et al.: A robust multifactor dimensionality reduction method for detecting gene-gene interactions with application to the genetic analysis of bladder cancer susceptibility. Ann. Hum. Genet. 75(1), 20–28 (2011)CrossRefGoogle Scholar
  13. 13.
    Guo, X., Meng, Y., Yu, N., Pan, Y.: Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering. BMC Bioinform. 15(1), 102 (2014)CrossRefGoogle Scholar
  14. 14.
    Hu, X., Liu, Q., Zhang, Z., Li, Z., Wang, S., He, L., Shi, Y.: SHEsisEpi, a GPU-enhanced genome-wide SNP-SNP interaction scanning algorithm, efficiently reveals the risk genetic epistasis in bipolar disorder. Cell Res. 20(7), 854–857 (2010)CrossRefGoogle Scholar
  15. 15.
    Jünger, D.: CUDA batch reduce primitive. (2016)
  16. 16.
    Jünger, D., Hundt, C., González-Domínguez, J., Schmidt, B.: Ultra-fast detection of higher-order epistatic interactions on gpus. In: 4th International Workshop on Parallelism in Bioinformatics (PBio 2016), Grenoble, France (2016)Google Scholar
  17. 17.
    Kam-Thong, T., Czamara, D., Tsuda, K., Borgwardt, K., Lewis, C., Erhardt-Lehmann, A., Hemmer, B., Rieckmann, P., Daake, M., Weber, F., Wolf, C., Ziegler, A., Pütz, B., Holsboer, F., Schölkopf, B., Müller-Myhsok, B.: EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units. Eur. J. Hum. Genet. 19(4), 465–471 (2011)CrossRefGoogle Scholar
  18. 18.
    Kässens, J.C., Wienbrandt, L., González-Domínguez, J., Schmidt, B., Schimmler, M.: High-speed exhaustive 3-locus interaction epistasis analysis on FPGAs. J. Comput. Sci. 9, 131–136 (2015)CrossRefGoogle Scholar
  19. 19.
    Leem, S., Jeong, H.H., et al.: Fast detection of high-order epistatic interactions in genome-wide association studies using information theoretic measure. Comput. Biol. Chem. 50, 19–28 (2014)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Meng, Y.A., Yu, Y., Cupples, L.A., Farrer, L.A., Lunetta, K.L.: Performance of random forest when SNPs are in linkage disequilibrium. BMC Bioinform. 10(1), 1 (2009)CrossRefGoogle Scholar
  22. 22.
    Nelson, M.R., Kardia, S.L., Ferrel, L.E., Sing, C.F.: A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation. Genome Res. 11(3), 458–470 (2001)CrossRefGoogle Scholar
  23. 23.
    Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M.A., Bender, D., Maller, J., Sklar, P., de Bakker, P.I., Daly, M.J., Sham, P.C.: PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81(3), 559–575 (2007)CrossRefGoogle Scholar
  24. 24.
    Ritchie Lab: genomeSIMLA software. (2016)
  25. 25.
    Sluga, D., Curk, T., Zupan, B., Lotric, U.: Heterogeneous computing architecture for fast detection of SNP-SNP interactions. BMC Bioinform. 15(1), 216 (2014)CrossRefGoogle Scholar
  26. 26.
    Tuo, S., Zhang, J., Yuan, X., Zhang, Y., Liu, Z.: FHSA-SED: two-locus model detection for genome-wide association study with harmony search algorithm. PLoS ONE 11(3), 1–27 (2016)CrossRefGoogle Scholar
  27. 27.
    Wan, X., Yang, C., et al.: BOOST: a fast approach to detecting gene-gene interactions in genome-wide case-control studies. Am. J. Hum. Genet. 87(3), 325–340 (2010)CrossRefGoogle Scholar
  28. 28.
    Wan, X., Yang, C., et al.: Predictive rule inference for epistatic interaction detection in genome-wide association studies. Bioinformatics 26(1), 30–37 (2010)CrossRefGoogle Scholar
  29. 29.
    Wang, Y., Liu, G., Feng, M., Wong, L.: An empirical comparison of several recent epistatic interaction detection methods. Bioinformatics 27(21), 2936–2943 (2011)CrossRefGoogle Scholar
  30. 30.
    Xie, M., Li, J., Jiang, T.: Detecting genome-wide epistases based on the clustering of relatively frequent items. Bioinformatics 28(1), 5–12 (2012)CrossRefGoogle Scholar
  31. 31.
    Yang, Y., Houle, A.M., Letendre, J., Richter, A.: RET Gly691ser mutation is associated with primary vesicoureteral reflux in the French-Canadian population from Quebec. Hum. Mutat. 29(5), 695–702 (2008)CrossRefGoogle Scholar
  32. 32.
    Yang, C., He, Z., Wan, X., Yang, Q., Xue, H., Weichuan, Y.: SNPHarvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies. Bioinformatics 25(4), 504–511 (2009)CrossRefGoogle Scholar
  33. 33.
    Yung, L.S., Yang, C., Wan, X., Yu, W.: GBOOST: a GPU-based tool for detecting genegene interactions in genomewide case control studies. Bioinformatics 27(9), 1309–1310 (2011)CrossRefGoogle Scholar
  34. 34.
    Zhang, Y., Liu, J.S.: Bayesian inference of epistatic interactions in case-control studies. Nat. Genet. 39(9), 1167–1173 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Institut für InformatikJohannes Gutenberg-Universität MainzMainzGermany
  2. 2.Grupo de Arquitectura de ComputadoresUniversidade da CoruñaA CoruñaSpain

Personalised recommendations