Attacking the opioid epidemic: determining the epistatic and pleiotropic genetic architectures for chronic pain and opioid addiction
Authors: 
Wayne Joubert, Deborah Weighill, David Kainer, Sharlee Climer, Amy Justice, Kjiersten Fagnan, Daniel JacobsonAuthors Info & Claims
Wayne Joubert, Deborah Weighill, David Kainer, Sharlee Climer, Amy Justice, Kjiersten Fagnan, Daniel JacobsonAuthors Info & ClaimsArticle No.: 57, Pages 1 - 14
Abstract
We describe the CoMet application for large-scale epistatic Genome-Wide Association Studies (eGWAS) and pleiotropy studies. High performance is attained by transforming the underlying vector comparison methods into highly performant generalized distributed dense linear algebra operations. The 2-way and 3-way Proportional Similarity metric and Custom Correlation Coefficient are implemented using native or adapted GEMM kernels optimized for GPU architectures. By aggressive overlapping of communications, transfers and computations, high efficiency with respect to single GPU kernel performance is maintained up to the full Titan and Summit systems. Nearly 300 quadrillion element comparisons per second and over 2.3 mixed precision ExaOps are reached on Summit by use of Tensor Core hardware on the Nvidia Volta GPUs. Performance is four to five orders of magnitude beyond comparable state of the art. CoMet is currently being used in projects ranging from bioenergy to clinical genomics, including for the genetics of chronic pain and opioid addiction.
References
[1]
C. S. Florence, C. Zhou, F. Luo, and L. Xu, "The economic burden of prescription opioid overdose, abuse, and dependence in the united states, 2013," Medical care, vol. 54, no. 10, pp. 901--906, 2016.
[2]
N. E. Morone and D. K. Weiner, "Pain as the fifth vital sign: exposing the vital need for pain education," Clinical therapeutics, vol. 35, no. 11, pp. 1728--1732, 2013.
[3]
A. Van Zee, "The promotion and marketing of oxycontin: commercial triumph, public health tragedy," American journal of public health, vol. 99, no. 2, pp. 221--227, 2009.
[4]
C. for Behavioral Health Statistics and Quality, "2015 national survey on drug use and health: Detailed tables," 2016.
[5]
"Cdc/nchs national vital statistics system, mortality," https://wonder.cdc.gov, 2017.
[6]
K. E. Vowles, M. L. McEntee, P. S. Julnes, T. Frohe, J. P. Ney, and D. N. van der Goes, "Rates of opioid misuse, abuse, and addiction in chronic pain: a systematic review and data synthesis," Pain, vol. 156, no. 4, pp. 569--576, 2015.
[7]
P. Muhuri, J. Gfroerer, and M. Davies, "Associations of nonmedical pain reliever use and initiation of heroin use in the united states. samhsa: Cbhsq data review," 2013.
[8]
T. J. Cicero, M. S. Ellis, H. L. Surratt, and S. P. Kurtz, "The changing face of heroin use in the united states: a retrospective analysis of the past 50 years," JAMA psychiatry, vol. 71, no. 7, pp. 821--826, 2014.
[9]
R. G. Carlson, R. W. Nahhas, S. S. Martins, and R. Daniulaityte, "Predictors of transition to heroin use among initially non-opioid dependent illicit pharmaceutical opioid users: A natural history study," Drug & Alcohol Dependence, vol. 160, pp. 127--134, 2016.
[10]
A. M. Vivolo-Kantor, P. Seth, R. M. Gladden, C. L. Mattson, G. T. Baldwin, A. Kite-Powell, and M. A. Coletta, "Vital signs: trends in emergency department visits for suspected opioid overdoses---united states, july 2016--september 2017," Morbidity and Mortality Weekly Report, vol. 67, no. 9, p. 279, 2018.
[11]
D. F. Weisberg, K. S. Gordon, D. T. Barry, W. C. Becker, S. Crystal, E. J. Edelman, J. Gaither, A. J. Gordon, J. Goulet, R. D. Kerns et al., "Long-term prescription opioids and/or benzodiazepines and mortality among hiv-infected and uninfected patients," Journal of acquired immune deficiency syndromes (1999), vol. 69, no. 2, p. 223, 2015.
[12]
S. Szymczak, J. M. Biernacka, H. J. Cordell, O. González-Recio, I. R. König, H. Zhang, and Y. V. Sun, "Machine learning in genome-wide association studies," Genetic epidemiology, vol. 33, no. S1, 2009.
[13]
A. A. Brown, A. Buil, A. Viñuela, T. Lappalainen, H.-F. Zheng, J. B. Richards, K. S. Small, T. D. Spector, E. T. Dermitzakis, and R. Durbin, "Genetic interactions affecting human gene expression identified by variance association mapping," Elife, vol. 3, 2014.
[14]
T. A. Manolio, F. S. Collins, N. J. Cox, D. B. Goldstein, L. A. Hindorff, D. J. Hunter, M. I. McCarthy, E. M. Ramos, L. R. Cardon, A. Chakravarti et al., "Finding the missing heritability of complex diseases," Nature, vol. 461, no. 7265, p. 747, 2009.
[15]
D. G. Clayton, "Prediction and interaction in complex disease genetics: experience in type 1 diabetes," PLoS genetics, vol. 5, no. 7, p. e1000540, 2009.
[16]
H. Schunkert, I. R. König, S. Kathiresan, M. P. Reilly, T. L. Assimes, H. Holm, M. Preuss, A. F. Stewart, M. Barbalic, C. Gieger et al., "Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease," Nature genetics, vol. 43, no. 4, p. 333, 2011.
[17]
M. I. McCarthy, G. R. Abecasis, L. R. Cardon, D. B. Goldstein, J. Little, J. P. Ioannidis, and J. N. Hirschhorn, "Genome-wide association studies for complex traits: consensus, uncertainty and challenges," Nature reviews genetics, vol. 9, no. 5, p. 356, 2008.
[18]
T. F. Mackay, "Epistasis and quantitative traits: using model organisms to study gene-gene interactions," Nature Reviews Genetics, vol. 15, no. 1, p. 22, 2014.
[19]
D. L. Aylor, W. Valdar, W. Foulds-Mathes, R. J. Buus, R. A. Verdugo, R. S. Baric, M. T. Ferris, J. A. Frelinger, M. Heise, M. B. Frieman et al., "Genetic analysis of complex traits in the emerging collaborative cross," Genome research, vol. 21, no. 8, pp. 1213--1222, 2011.
[20]
J. Gonzalez-Dominguez, B. Schmidt, J. C. Kassens, and L. Wienbrandt, "Hybrid cpu/gpu acceleration of detection of 2-snp epistatic interactions in gwas," in European Conference on Parallel Processing. Springer, 2014, pp. 680--691.
[21]
S. Climer, A. R. Templeton, and W. Zhang, "Allele-specific network reveals combinatorial interaction that transcends small effects in psoriasis gwas," PLoS Comput Biol, vol. 10, no. 9, p. e1003766, 2014.
[22]
S. Basu, K. Kumbier, J. B. Brown, and B. Yu, "iterative random forests to discover predictive and stable high-order interactions," Proceedings of the National Academy of Sciences, p. 201711236, 2018.
[23]
S. Mukherjee, S. Kim, V. K. Ramanan, L. E. Gibbons, K. Nho, M. M. Glymour, N. Ertekin-Taner, T. J. Montine, A. J. Saykin, P. K. Crane et al., "Gene-based gwas and biological pathway analysis of the resilience of executive functioning," Brain imaging and behavior, vol. 8, no. 1, pp. 110--118, 2014.
[24]
S. Climer, W. Yang, L. de las Fuentes, V. G. Da vila Roma n, and C. C. Gu, "A Custom Correlation Coefficient (CCC) Approach for Fast Identification of Multi-SNP Association Patterns in Genome-Wide SNPs Data," Genetic Epidemiology, vol. 38, no. 7, 2014, http://www.ncbi.nlm.nih.gov/ /25168954.
[25]
D. A. Weighill and D. A. Jacobson, "3-way networks: application of hypergraphs for modelling increased complexity in comparative genomics," PLoS computational biology, vol. 11, no. 3, p. e1004079, 2015.
[26]
D. Zhou, J. Huang, and B. Schölkopf, "Learning with hypergraphs: Clustering, classification, and embedding," in Advances in neural information processing systems, 2007, pp. 1601--1608.
[27]
W. Joubert, J. Nance, D. Weighill, and D. Jacobson, "Parallel Accelerated Vector Similarity Calculations for Genomics Applications," Parallel Computing, vol. 75, July 2018, pp. 130--145, https://www.sciencedirect.com/science/article/pii/S016781911830084X.
[28]
D. Jacobson and G. Emerton, "Gsa-pca: gene set generation by principal component analysis of the laplacian matrix of a metabolic network," BMC bioinformatics, vol. 13, no. 1, p. 197, 2012.
[29]
S. Van Dongen, "Graph clustering via a discrete uncoupling process," SIAM Journal on Matrix Analysis and Applications, vol. 30, no. 1, pp. 121--141, 2008.
[30]
W. Joubert, J. Nance, S. Climer, D. Weighill, and D. Jacobson, "Parallel Accelerated Custom Correlation Coefficient Calculations for Genomics Applications," Parallel Computing, to appear, https://arxiv.org/abs/1705.08213.
[31]
W.-H. Wei, G. Hemani, and C. S. Haley, "Detecting epistasis in human complex traits," Nature Reviews Genetics, 2014, http://www.nature.com/nrg/journal/v15/n11/full/nrg3747.html.
[32]
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. {Online}. Available: http://bioinformatics.oxfordjournals.org/content/27/9/1309.abstract
[33]
Q. Wang, F. Shi, A. Kowalczyk, R. M. Campbell, B. Goudey, D. Rawlinson, A. Harwood, H. Ferra, and A. Kowalczyk, "GW-ISFI: A universal GPU interface for exhaustive search of pairwise interactions in case-control GWAS in minutes," in 2014 IEEE International Conference on Bioinformatics and Biomedicine, 2014, http://ieeexplore.ieee.org/document/6999192.
[34]
J. Gonzalez-Dominguez, S. Ramos, J. Tourino, and B. Schmidt, "Parallel Pairwise Epistasis Detection on Heterogeneous Computing Architectures," IEEE Transactions on Parallel and Distributed Systems, 2016, http://ieeexplore.ieee.org/document/7165657/.
[35]
J. Gonzalez-Dominguez and B. Schmidt, "GPU-accelerated exhaustive search for third-order epistatic interactions in case-control studies," Journal of Computational Science, vol. 8, pp. 93 - 100, 2015. {Online}. Available: http://www.sciencedirect.com/science/article/pii/S1877750315000393
[36]
B. Goudey, M. Abedini, J. L. Hopper, M. Inouye, E. Makalic, D. F. Schmidt, J. Wagner, Z. Zhou, J. Zobel, and M. Reumann, "High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in Genome Wide Association Studies," in Proceedings of the HISA BIG DATA 2013 Conference, 2015, https://link.springer.com/article/10.1186/2047-2501-3-S1-S3.
[37]
G. R. Luecke, N. T. Weeks, B. M. Groth, M. Kraeva, L. Ma, L. M. Kramer, J. E. Koltes, and J. M. Reecy, "Fast Epistasis Detection in Large-Scale GWAS for Intel Xeon Phi Clusters," in Trustcom/BigDataSE/ISPA, 2015 IEEE, 2011, http://ieeexplore.ieee.org/document/7345653/.
[38]
N. T. Weeks, G. R. Luecke, B. M. Groth, M. Kraeva, L. Ma, L. M. Kramer, J. E. Koltes, and J. M. Reecy, "High-performance epistasis detection in quantitative trait GWAS," The International Journal of High Performance Computing Applications, vol. 0, no. 0, p. 1094342016658110, 0. {Online}. Available
[39]
J. Gonzalez-Dominguez, J. C. Kassens, 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., vol. 29, no. 4, pp. 506--510, Nov. 2015. {Online}. Available
[40]
J. C. Kassens, J. Gonzalez-Dominguez, L. Wienbrandt, and B. Schmidt, "UPC++ for bioinformatics: A case study using genome-wide association studies," in 2014 IEEE International Conference on Cluster Computing (CLUSTER), Sept 2014, pp. 248--256.
[41]
I. S. Haque, V. S. Pande, and W. P. Walters, "Anatomy of High-Performance 2D Similarity Calculations," Journal of Chemical Information and Modeling, vol. 51, no. 9, pp. 2345--2351, 2011. {Online}. Available
[42]
S. Tomov, R. Nath, H. Ltaief, and J. Dongarra, "Dense linear algebra solvers for multicore with GPU accelerators," in Parallel Distributed Processing, Workshops and Phd Forum (IPDPSW), 2010 IEEE International Symposium on, April 2010, pp. 1--8, http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5470941.
[43]
J. Choi, J. J. Dongarra, R. Pozo, and D. W. Walker, "ScaLA-PACK: A scalable linear algebra library for distributed memory concurrent computers," in Frontiers of Massively Parallel Computation, 1992., Fourth Symposium on the. IEEE, 1992, pp. 120--127, http://ieeexplore.ieee.org/document/234898/.
[44]
E. Solomonik, D. Matthews, J. Hammond, and J. Demmel, "Cyclops Tensor Framework: Reducing communication and eliminating load imbalance in massively parallel contractions," in Parallel Distributed Processing (IPDPS), 2013 IEEE 27th International Symposium on, 2013, pp. 813--824, http://ieeexplore.ieee.org/document/6569864/?arnumber=6569864.
[45]
V. Anatharaj, F. Foertter, W. Joubert, and J. Wells, "Approaching Exascale: Application Requirements for OLCF Leadership Computing," Oak Ridge National Laboratory, Technical Report ORNL/TM-2013/186, 2013, https://www.olcf.ornl.gov/wp-content/uploads/2013/01/OLCF_Requirements_TM_2013_Final1.pdf.
[46]
J. Dongarra, "Experiments with Energy Savings and Short Precision," in Salishan2018 Proceedings.
[47]
W. Joubert, R. K. Archibald, M. A. Berrill, W. M. Brown, M. Eisenbach, R. Grout, J. Larkin, J. Levesque, B. Messer, M. R. Norman, and et al., "Accelerated application development: The ORNL Titan experience," Computers and Electrical Engineering, vol. 46, May 2015.
[48]
"The Extreme Heterogeneity Virtual Workshop 2018," https://www.orau.gov/ExHeterogeneity2018/agenda.htm.
[49]
"PLASMA," http://icl.cs.utk.edu/plasma/software.
[50]
"BLIS," https://github.com/flame/blis.
[51]
"OpenBLAS: An optimized BLAS library," http://www.openblas.net/.
[52]
D. I. Lyakh, "Portable Heterogeneous High-Performance Computing via Domain-Specific Virtualization," 4th ADAC Workshop, 2017, https://iadac.github.io/events/adac4/liakh.pdf.
[53]
Innovative Computing Laboratory, "SLATE," http://icl.utk.edu/slate/.
- Attacking the opioid epidemic: determining the epistatic and pleiotropic genetic architectures for chronic pain and opioid addiction
Recommendations
Attacking the opioid epidemic: determining the epistatic and pleiotropic genetic architectures for chronic pain and opioid addiction
SC '18: Proceedings of the International Conference for High Performance Computing, Networking, Storage, and AnalysisWe describe the CoMet application for large-scale epistatic Genome-Wide Association Studies (eGWAS) and pleiotropy studies. High performance is attained by transforming the underlying vector comparison methods into highly performant generalized ...
Curbing the Opioid Epidemic at Its Root: The Effect of Provider Discordance After Opioid Initiation
Although medical research has addressed the clinical management of chronic opioid users, little is known about how operational interventions shortly after opioid initiation can impact a patient’s likelihood of long-term opioid use. Using a nationwide U.S. ...
Balanced Opioid Prescribing via a Clinical Trade-Off: Pain Relief vs. Adverse Effects of Discomfort, Dependence, and Tolerance/Hypersensitivity
In the backdrop of the opioid epidemic, opioid prescribing has distinct medical and social challenges. Overprescribing contributes to the ongoing opioid epidemic, whereas underprescribing yields inadequate pain relief. Moreover, opioids have serious ...
Comments
Information & Contributors
Information
Published In
November 2018
932 pages
Sponsors
In-Cooperation
- IEEE CS
Publisher
IEEE Press
Publication History
Published: 11 November 2018
Check for updates
Qualifiers
- Research-article
Conference
SC18
Sponsor:
SC18: The International Conference for High Performance Computing, Networking, Storage and Analysis
November 11 - 16, 2018
Texas, Dallas
Acceptance Rates
Overall Acceptance Rate 1,516 of 6,373 submissions, 24%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 432Total Downloads
- Downloads (Last 12 months)10
- Downloads (Last 6 weeks)1
Reflects downloads up to 15 Oct 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in