Abstract
High-throughput genotyping technologies allow the collection of up to a few million genetic markers (such as SNPs) of an individual within a few minutes of time. Detecting epistasis, such as 2-SNP interactions, in Genome-Wide Association Studies is an important but time consuming operation since statistical computations have to be performed for each pair of measured markers. In this work we present EpistSearch, a parallelized tool that, following the log-linear model approach, uses a novel filter to determine the interactions between all SNP-pairs. Our tool is parallelized using a hybrid combination of Pthreads and CUDA in order to take advantage of CPU/GPU architectures. Experimental results with simulated and real datasets show that EpistSearch outperforms previous approaches, either using GPUs or only CPU cores. For instance, an exhaustive analysis of a real-world dataset with 500,000 SNPs and 5,000 individuals requires less than 42 minutes on a machine with 6 CPU cores and a GTX Titan GPU.
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Maher, B.: Personal Genomes: the Case of the Missing Heritability. Nature 456(7218), 18–21 (2008)
Moore, J.H., Asselbergs, F.W., Williams, S.M.: Bioinformatics Challenges for Genome-Wide Association Studies. Bioinformatics 26(4), 445–455 (2010)
Cordell, H.J.: Detecting Gene-Gene Interactions that Underlie Human Diseases. Nature Reviews Genetics 10(6), 392–404 (2009)
Zhao, J., Jin, L.: Test for Interaction Between Two Unlinked Loci. The American Journal of Human Genetics 78(1), 15–27 (2006)
Purcell, S., et al.: PLINK: a Tool Set for Whole-Genome Association and Population-Based Linkage Anlyses. The American Journal of Human Genetics 81(3), 559–575 (2007)
Wellcome Trust Case Control Consortium, http://www.wtccc.org.uk/ (last visit: January 2014)
Wellcome Trust Case Control Consortium: Genome-Wide Association Study of 14,000 Cases of Seven Common Diseases and 3,000 Shared Controls. Nature 447(7145), 661–678 (2007)
Yang, C., He, Z., Wan, X., Yang, Q., Xue, H., Yu, W.: SNPHarvester: a Filtering-Based Approach for Detecting Epistatic Interaction in Genome-Wide Association Studies. Bioinformatics 25(4), 504–511 (2009)
Wan, X., Yang, C., Yang, Q., Xue, H., Tang, N.L., Yu, W.: Predictive Rule Inference for epistatic Interaction Detection in Genome-Wide Association Studies. Bioinformatics 26(1), 30–37 (2010)
Wan, X., Yang, C., Yang, Q., Xue, H., Tang, N.L., Yu, W.: BOOST: A Fast Approach to Detecting Gene-Gene Interactions in Genome-Wide Case-Control Studies. The American Journal of Human Genetics 87(3), 325–340 (2010)
Bi, J., Gelernter, J., Sun, J., Kranzler, H.R.: Comparing the Utility of Homogeneous Subtypes of Cocaine Use and Related Behaviors with DSM-IV Cocaine Dependence as Traits for Genetic Association Analysis. American Journal of Medical Genetics 165(2), 148–156 (2014)
Chu, M., et al.: A Genome-Wide Gene-Gene Interaction Analysis Identifies an Epistatic Gene Pair for Lung Cancer Susceptibility in Han Chinese. Cancinogenesis 32(3), 572–577 (2014)
Milne, R.L., et al.: A Large-Scale Assessment of Two-Way SNP Interactions in Breast Cancer Susceptibility Using 46,450 Cases and 42,461 Controls from the Breast Cancer Association Consortium. Human Molecular Genetics 23(7), 1934–1946 (2014)
Yung, L.S., Yang, C., Wan, X., Yu, W.: GBOOST: A GPU-Based Tool for Detecting Gene-Gene Interactions in Genome-Wide Case Control Studies. Bioinformatics 27(9), 1309–1310 (2011)
Piriyapongsa, J., Ngamphiw, C., Intarapanich, A., Kulawonganunchai, S., Assawamakin, A., Bootchai, C., Shaw, P.J., Tongsima, S.: iLOCi: a SNP Interaction Priorization Technique for Detecting Epistasis in Genome-Wide Association Studies. BMC Genomics 13(suppl. 7) (2012)
Goudey, B., Rawlinson, D., Wang, Q., Shi, F., Ferra, H., Campbell, R.M., Stern, L., Inouye, M.T., Ong, C.S., Kowalczyk, A.: GWIS - Model-Free, Fast and Exhaustive Search for Epistatic Interactions in Case-Control GWAS. BMC Genomics 14(suppl. 3) (2012)
Liu, Y., Wirawan, A., Schmidt, B.: CUDASW++ 3.0: Accelerating Smith-Waterman Protein Database Search by Coupling CPU and GPU SIMD Instructions. BMC Bioinformatics 14(177) (2013)
Liu, Y., Schmidt, B.: CUSHAW2-GPU: Empowering Faster Gapped Short-Read Alignment Using GPU Computing. IEEE Design & Test of Computers (in press)
POSIX Threads Programming, https://computing.llnl.gov/tutorials/pthreads/ (last visit: January 2014)
NVIDIA Developer CUDA Zone, https://developer.nvidia.com/category/zone/cuda-zone (last visit: January 2014)
genomeSIMLA Webpage, http://chgr.mc.vanderbilt.edu/genomeSIMLA/genomeSIMLA/Introduction.html (last visit: January 2014)
Hemani, G., Theocharidis, A., Wei, W., Haley, C.: EpiGPU: Exhaustive Pairwise Epistasis Scans Parallelized on Customer Level Graphic Cards. Bioinformatics 27(11), 1462–1465 (2011)
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 Research 20(7), 854–857 (2010)
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González-Domínguez, J., Schmidt, B., Kässens, J.C., Wienbrandt, L. (2014). Hybrid CPU/GPU Acceleration of Detection of 2-SNP Epistatic Interactions in GWAS. In: Silva, F., Dutra, I., Santos Costa, V. (eds) Euro-Par 2014 Parallel Processing. Euro-Par 2014. Lecture Notes in Computer Science, vol 8632. Springer, Cham. https://doi.org/10.1007/978-3-319-09873-9_57
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DOI: https://doi.org/10.1007/978-3-319-09873-9_57
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