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Table of Contents
Vol. 73, No. 4, 2012
Issue release date: September 2012
Section title: Original Paper
Hum Hered 2012;73:220–236
(DOI:10.1159/000341885)

GLIDE: GPU-Based Linear Regression for Detection of Epistasis

Kam-Thong T.a, c · Azencott C.-A.a · Cayton L.b · Pütz B.c · Altmann A.c · Karbalai N.c · Sämann P.G.d · Schölkopf B.b · Müller-Myhsok B.c · Borgwardt K.M.a
aMachine Learning and Computational Biology Research Group, Max Planck Institutes Tübingen, and bDepartment for Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen, and cStatistical Genetics and dNeuroimaging Research Group, Max Planck Institute of Psychiatry, Munich, Germany
email Corresponding Author

Bertram Müller-Myhsok

Statistical Genetics Group, Max Planck Institute of Psychiatry

Kraepelinstrasse 2

DE–80804 Munich (Germany)

Tel. +49 893 062 2246, E-Mail bmm@mpipsykl.mpg.de


Abstract

Due to recent advances in genotyping technologies, mapping phenotypes to single loci in the genome has become a standard technique in statistical genetics. However, one-locus mapping fails to explain much of the phenotypic variance in complex traits. Here, we present GLIDE, which maps phenotypes to pairs of genetic loci and systematically searches for the epistatic interactions expected to reveal part of this missing heritability. GLIDE makes use of the computational power of consumer-grade graphics cards to detect such interactions via linear regression. This enabled us to conduct a systematic two-locus mapping study on seven disease data sets from the Wellcome Trust Case Control Consortium and on in-house hippocampal volume data in 6 h per data set, while current single CPU-based approaches require more than a year’s time to complete the same task.

© 2012 S. Karger AG, Basel


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References

  1. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, et al: Finding the missing heritability of complex diseases. Nature 2009;461:747–753.
  2. Cordell HJ, Wedig GC, Jacobs KB, Elston RC: Multilocus linkage tests based on affected relative pairs. Am J Hum Genet 2000;66:1273–1286.
  3. Cox NJ, Frigge M, Nicolae DL, Concannon P, Hanis CL, Bell GI, Kong A: Loci on chromosomes 2 (NIDDM1) and 15 interact to increase susceptibility to diabetes in Mexican Americans. Nat Genet 1999;21:213–215.
  4. Cho JH, Nicolae DL, Gold LH, Fields CT, LaBuda MC, Rohal PM, Pickles MR, Qin L, Fu Y, Mann JS, et al: Identification of novel susceptibility loci for inflammatory bowel disease on chromosomes 1p, 3q, and 4q: evidence for epistasis between 1p and IBD1. Proc Natl Acad Sci USA 1998;95:7502–7507.
  5. Williams SM, Ritchie MD, Phillips JA 3rd, Dawson E, Prince M, Dzhura E, Willis A, Semenya A, Summar M, White BC, et al: Multilocus analysis of hypertension: a hierarchical approach. Hum Hered 2004;57:28–38.
  6. Ashworth A, Lord CJ, Reis-Filho JS: Genetic interactions in cancer progression and treatment. Cell 2011;145:30–38.
  7. Tan H, Chen Q, Sust S, Buckholtz JW, Meyers JD, Egan MF, Mattay VS, Meyer-Lindenberg A, Weinberger DR, Callicott JH: Epistasis between catechol-O-methyltransferase and type II metabotropic glutamate receptor 3 genes on working memory brain function. Proc Natl Acad Sci USA 2007;104:12536–12541.
  8. Marchini J, Donnelly P, Cardon LR: Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat Genet 2005;37:413–417.
  9. Zhang X, Pan F, Xie Y, Zou F, Wang W: COE: a general approach for efficient genome-wide two-locus epistasis test in disease association study. J Comput Biol 2010;17:401–415.
  10. Zhang X, Huang S, Zou F, Wang W: TEAM: efficient two-locus epistasis tests in human genome-wide association study. Bioinformatics 2010;26:i217–i227.
  11. 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 2010;20:854–857.

    External Resources

  12. Kam-Thong T, Czamara D, Tsuda K, Borgwardt K, Lewis CM, Erhardt-Lehmann A, Hemmer B, Rieckmann P, Daake M, Weber F, et al: EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units. Eur J Hum Genet 2011;19:465–471.
  13. Kam-Thong T, Pütz B, Karbalai N, Müller-Myhsok B, Borgwardt K: Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs. Bioinformatics 2011;27:i214–i221.
  14. Yung LS, Yang C, Wan X, Yu W: GBOOST: a GPU-based tool for detecting gene-gene interactions in genome-wide case control studies. Bioinformatics 2011;27:1309–1310.
  15. Hemani G, Theocharidis A, Wei W, Haley C: EpiGPU: exhaustive pairwise epistasis scans parallelized on consumer level graphics cards. Bioinformatics 2011;27:1462–1465.
  16. Wellcome Trust Case Control Consortium: Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 2007;447:661–678.
  17. Wan X, Yang C, Yang Q, Xue H, Fan X, Tang NLS, Yu W: BOOST: a fast approach to detecting gene-gene interactions in genome-wide case-control studies. Am J Hum Genet 2010;87:325–340.
  18. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, et al: PLINK: a toolset for whole-genome association and population-based linkage analysis. Am J Hum Genet 2007;41:559–575.
  19. Schüpbach T, Xenarios I, Bergmann S, Kapur K: FastEpistasis: a high performance computing solution for quantitative trait epistasis. Bioinformatics 2010;26:1468–1469.

    External Resources

  20. Becker T, Herold C, Meesters C, Matthesen M, Baur MP: Significance levels in genome-wide interaction analysis (GWIA). Ann Hum Genet 2011;75:29–35.

    External Resources

  21. Shiina T, Hosomichi K, Inoko H, Kulski JK: The HLA genomic loci map: expression, interaction, diversity and disease. J Hum Genet 2009;54:15–39.
  22. Nejentsev S, Howson JM, Walker NM, Szeszko J, Field SF, Stevens HE, Reynolds P, Hardy M, King E, Masters J, et al: Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A. Nature 2007;450:887–892.
  23. Geuze E, Vermetten E, Bremner JD: MR-based in vivo hippocampal volumetrics: 1. Review of methodologies currently employed. Mol Psychiatry 2005;10:147–159.
  24. Peper JS, Brouwer RM, Boomsma DI, Kahn RS, Hulshoff Pol HE: Genetic influences on human brain structure: a review of brain imaging studies in twins. Hum Brain Mapp 2007;28:464–473.

    External Resources

  25. Kohli M, Lucae S, Saemann P, Schmidt M, Demirkan A, Hek K, Czamara D, Alexander M, Salyakina D, Ripke S, et al: The neuronal transporter gene SLC6A15 confers risk to major depression. Neuron 2011;70:252–265.
  26. Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR: MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 2010;34:816–834.
  27. Stein JL, Medland SE, Vasquez AA, Hibar DP, Senstad RE, Winkler AM, Toro R, Appel K, Bartecek R, Bergmann Ø, et al: Identification of common variants associated with human hippocampal and intracranial volumes. Nat Genet 2012;44:552–561.
  28. Fedorova IM, Jacobson MA, Basile A, Jacobson KA: Behavioral characterization of mice lacking the A3 adenosine receptor: sensitivity to hypoxic neurodegeneration. Cell Mol Neurobiol 2003;23:431–447.
  29. Jursky F, Nelson N: Developmental expression of GABA transporters GAT1 and GAT4 suggests involvement in brain maturation. J Neurochem 1996;67:857–867.
  30. Yirmiya R, Goshen I: Immune modulation of learning, memory, neural plasticity and neurogenesis. Brain Behav Immun 2011;25:181–213.
  31. Simpson TR, Quezada SA, Allison JP: Regulation of CD4 T cell activation and effector function by inducible costimulator (ICOS). Curr Opin Immunol 2010;22:326–332.
  32. Conway SJ, Kaartinen V: TGFβ superfamily signaling in the neural crest lineage. Cell Adh Migr 2011;5:232–236.

    External Resources

  33. Miquelajauregui A, Van de Putte T, Polyakov A, Nityanandam A, Boppana S, Seuntjens E, Karabinos A, Higashi Y, Huylebroeck D, Tarabykin V: Smad-interacting protein-1 (Zfhx1b) acts upstream of Wnt signaling in the mouse hippocampus and controls its formation. Proc Natl Acad Sci USA 2007;104:12919–12924.
  34. Inkster B, Nichols TE, Saemann PG, Auer DP, Holsboer F, Muglia P, Matthews PM: Pathway-based approaches to imaging genetics association studies: Wnt signaling, GSK3beta substrates and major depression. Neuroimage 2010;53:908–917.
  35. Yoneda M, Fujita T, Yamada Y, Yamada K, Fujii A, Inagaki T, Nakagawa H, Shimada A, Kishikawa M, Nagaya M, et al: Late infantile Hirschsprung disease-mental retardation syndrome with a 3-bp deletion in ZFHX1B. Neurology 2002;59:1637–1640.
  36. Gianfrancesco F, Esposito T, Penco S, Maglione V, Liquori CL, Patrosso MC, Zuffardi O, Ciccodicola A, Marchuk DA, Squitieri F: ZPLD1 gene is disrupted in a patient with balanced translocation that exhibits cerebral cavernous malformations. Neuroscience 2008;155:345–349.
  37. Fonfria E, Murdock PR, Cusdin FS, Benham CD, Kelsell RE, McNulty S: Tissue distribution profiles of the human TRPM cation channel family. J Recept Signal Transduct Res 2006;26:159–178.
  38. Yagi T, Takeichi M: Cadherin superfamily genes: functions, genomic organization, and neurologic diversity. Gene Dev 2000;14:1169–1180.
  39. Hellevik O: Linear versus logistic regression when the dependent variable is a dichotomy. Quality & Quantity 2009;43:59–74.

    External Resources

  40. Zhao L, Chen Y, Schaffner DW: Comparison of logistic regression and linear regression in modeling percentage data. Appl Environ Microbiol 2001;67:2129–2135.
  41. Hennings JM, Owashi T, Binder EB, Horstmann S, Menke A, Kloiber S, Dose T, Wollweber B, Spieler D, Messer T, et al: Clinical characteristics and treatment outcome in a representative sample of depressed inpatients – findings from the Munich Antidepressant Response Signature (MARS) project. J Psychiatr Res 2009;43:215–229.
  42. Binder EB, Salyakina D, Lichtner P, Wochnik GM, Ising M, Putz B, Papiol S, Seaman S, Lucae S, Kohli MA, et al: Polymorphisms in FKBP5 are associated with increased recurrence of depressive episodes and rapid response to antidepressant treatment. Nat Genet 2004;36:1319–1325.
  43. Ashburner J: A fast diffeomorphic image registration algorithm. Neuroimage 2007;38:95–113.
  44. Ashburner J, Friston KJ: Unified segmentation. Neuroimage 2005;26:839–851.
  45. Amunts K, Kedo O, Kindler M, Pieperhoff P, Mohlberg H, Shah NJ, Habel U, Schneider F, Zilles K: Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anat Embryol 2005;210:343–352.
  46. Eickhoff SB, Stephan KE, Mohlberg H, Grefkes C, Fink GR, Amunts K, Zilles K: A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage 2005;25:1325–1335.

Author Contacts

Bertram Müller-Myhsok

Statistical Genetics Group, Max Planck Institute of Psychiatry

Kraepelinstrasse 2

DE–80804 Munich (Germany)

Tel. +49 893 062 2246, E-Mail bmm@mpipsykl.mpg.de


Article / Publication Details

First-Page Preview
Abstract of Original Paper

Received: 1/14/2012 1:43:53 AM
Accepted: 7/13/2012
Published online: 9/4/2012
Issue release date: September 2012

Number of Print Pages: 17
Number of Figures: 8
Number of Tables: 13

ISSN: 0001-5652 (Print)
eISSN: 1423-0062 (Online)

For additional information: http://www.karger.com/HHE


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References

  1. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, et al: Finding the missing heritability of complex diseases. Nature 2009;461:747–753.
  2. Cordell HJ, Wedig GC, Jacobs KB, Elston RC: Multilocus linkage tests based on affected relative pairs. Am J Hum Genet 2000;66:1273–1286.
  3. Cox NJ, Frigge M, Nicolae DL, Concannon P, Hanis CL, Bell GI, Kong A: Loci on chromosomes 2 (NIDDM1) and 15 interact to increase susceptibility to diabetes in Mexican Americans. Nat Genet 1999;21:213–215.
  4. Cho JH, Nicolae DL, Gold LH, Fields CT, LaBuda MC, Rohal PM, Pickles MR, Qin L, Fu Y, Mann JS, et al: Identification of novel susceptibility loci for inflammatory bowel disease on chromosomes 1p, 3q, and 4q: evidence for epistasis between 1p and IBD1. Proc Natl Acad Sci USA 1998;95:7502–7507.
  5. Williams SM, Ritchie MD, Phillips JA 3rd, Dawson E, Prince M, Dzhura E, Willis A, Semenya A, Summar M, White BC, et al: Multilocus analysis of hypertension: a hierarchical approach. Hum Hered 2004;57:28–38.
  6. Ashworth A, Lord CJ, Reis-Filho JS: Genetic interactions in cancer progression and treatment. Cell 2011;145:30–38.
  7. Tan H, Chen Q, Sust S, Buckholtz JW, Meyers JD, Egan MF, Mattay VS, Meyer-Lindenberg A, Weinberger DR, Callicott JH: Epistasis between catechol-O-methyltransferase and type II metabotropic glutamate receptor 3 genes on working memory brain function. Proc Natl Acad Sci USA 2007;104:12536–12541.
  8. Marchini J, Donnelly P, Cardon LR: Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat Genet 2005;37:413–417.
  9. Zhang X, Pan F, Xie Y, Zou F, Wang W: COE: a general approach for efficient genome-wide two-locus epistasis test in disease association study. J Comput Biol 2010;17:401–415.
  10. Zhang X, Huang S, Zou F, Wang W: TEAM: efficient two-locus epistasis tests in human genome-wide association study. Bioinformatics 2010;26:i217–i227.
  11. 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 2010;20:854–857.

    External Resources

  12. Kam-Thong T, Czamara D, Tsuda K, Borgwardt K, Lewis CM, Erhardt-Lehmann A, Hemmer B, Rieckmann P, Daake M, Weber F, et al: EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units. Eur J Hum Genet 2011;19:465–471.
  13. Kam-Thong T, Pütz B, Karbalai N, Müller-Myhsok B, Borgwardt K: Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs. Bioinformatics 2011;27:i214–i221.
  14. Yung LS, Yang C, Wan X, Yu W: GBOOST: a GPU-based tool for detecting gene-gene interactions in genome-wide case control studies. Bioinformatics 2011;27:1309–1310.
  15. Hemani G, Theocharidis A, Wei W, Haley C: EpiGPU: exhaustive pairwise epistasis scans parallelized on consumer level graphics cards. Bioinformatics 2011;27:1462–1465.
  16. Wellcome Trust Case Control Consortium: Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 2007;447:661–678.
  17. Wan X, Yang C, Yang Q, Xue H, Fan X, Tang NLS, Yu W: BOOST: a fast approach to detecting gene-gene interactions in genome-wide case-control studies. Am J Hum Genet 2010;87:325–340.
  18. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, et al: PLINK: a toolset for whole-genome association and population-based linkage analysis. Am J Hum Genet 2007;41:559–575.
  19. Schüpbach T, Xenarios I, Bergmann S, Kapur K: FastEpistasis: a high performance computing solution for quantitative trait epistasis. Bioinformatics 2010;26:1468–1469.

    External Resources

  20. Becker T, Herold C, Meesters C, Matthesen M, Baur MP: Significance levels in genome-wide interaction analysis (GWIA). Ann Hum Genet 2011;75:29–35.

    External Resources

  21. Shiina T, Hosomichi K, Inoko H, Kulski JK: The HLA genomic loci map: expression, interaction, diversity and disease. J Hum Genet 2009;54:15–39.
  22. Nejentsev S, Howson JM, Walker NM, Szeszko J, Field SF, Stevens HE, Reynolds P, Hardy M, King E, Masters J, et al: Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A. Nature 2007;450:887–892.
  23. Geuze E, Vermetten E, Bremner JD: MR-based in vivo hippocampal volumetrics: 1. Review of methodologies currently employed. Mol Psychiatry 2005;10:147–159.
  24. Peper JS, Brouwer RM, Boomsma DI, Kahn RS, Hulshoff Pol HE: Genetic influences on human brain structure: a review of brain imaging studies in twins. Hum Brain Mapp 2007;28:464–473.

    External Resources

  25. Kohli M, Lucae S, Saemann P, Schmidt M, Demirkan A, Hek K, Czamara D, Alexander M, Salyakina D, Ripke S, et al: The neuronal transporter gene SLC6A15 confers risk to major depression. Neuron 2011;70:252–265.
  26. Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR: MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 2010;34:816–834.
  27. Stein JL, Medland SE, Vasquez AA, Hibar DP, Senstad RE, Winkler AM, Toro R, Appel K, Bartecek R, Bergmann Ø, et al: Identification of common variants associated with human hippocampal and intracranial volumes. Nat Genet 2012;44:552–561.
  28. Fedorova IM, Jacobson MA, Basile A, Jacobson KA: Behavioral characterization of mice lacking the A3 adenosine receptor: sensitivity to hypoxic neurodegeneration. Cell Mol Neurobiol 2003;23:431–447.
  29. Jursky F, Nelson N: Developmental expression of GABA transporters GAT1 and GAT4 suggests involvement in brain maturation. J Neurochem 1996;67:857–867.
  30. Yirmiya R, Goshen I: Immune modulation of learning, memory, neural plasticity and neurogenesis. Brain Behav Immun 2011;25:181–213.
  31. Simpson TR, Quezada SA, Allison JP: Regulation of CD4 T cell activation and effector function by inducible costimulator (ICOS). Curr Opin Immunol 2010;22:326–332.
  32. Conway SJ, Kaartinen V: TGFβ superfamily signaling in the neural crest lineage. Cell Adh Migr 2011;5:232–236.

    External Resources

  33. Miquelajauregui A, Van de Putte T, Polyakov A, Nityanandam A, Boppana S, Seuntjens E, Karabinos A, Higashi Y, Huylebroeck D, Tarabykin V: Smad-interacting protein-1 (Zfhx1b) acts upstream of Wnt signaling in the mouse hippocampus and controls its formation. Proc Natl Acad Sci USA 2007;104:12919–12924.
  34. Inkster B, Nichols TE, Saemann PG, Auer DP, Holsboer F, Muglia P, Matthews PM: Pathway-based approaches to imaging genetics association studies: Wnt signaling, GSK3beta substrates and major depression. Neuroimage 2010;53:908–917.
  35. Yoneda M, Fujita T, Yamada Y, Yamada K, Fujii A, Inagaki T, Nakagawa H, Shimada A, Kishikawa M, Nagaya M, et al: Late infantile Hirschsprung disease-mental retardation syndrome with a 3-bp deletion in ZFHX1B. Neurology 2002;59:1637–1640.
  36. Gianfrancesco F, Esposito T, Penco S, Maglione V, Liquori CL, Patrosso MC, Zuffardi O, Ciccodicola A, Marchuk DA, Squitieri F: ZPLD1 gene is disrupted in a patient with balanced translocation that exhibits cerebral cavernous malformations. Neuroscience 2008;155:345–349.
  37. Fonfria E, Murdock PR, Cusdin FS, Benham CD, Kelsell RE, McNulty S: Tissue distribution profiles of the human TRPM cation channel family. J Recept Signal Transduct Res 2006;26:159–178.
  38. Yagi T, Takeichi M: Cadherin superfamily genes: functions, genomic organization, and neurologic diversity. Gene Dev 2000;14:1169–1180.
  39. Hellevik O: Linear versus logistic regression when the dependent variable is a dichotomy. Quality & Quantity 2009;43:59–74.

    External Resources

  40. Zhao L, Chen Y, Schaffner DW: Comparison of logistic regression and linear regression in modeling percentage data. Appl Environ Microbiol 2001;67:2129–2135.
  41. Hennings JM, Owashi T, Binder EB, Horstmann S, Menke A, Kloiber S, Dose T, Wollweber B, Spieler D, Messer T, et al: Clinical characteristics and treatment outcome in a representative sample of depressed inpatients – findings from the Munich Antidepressant Response Signature (MARS) project. J Psychiatr Res 2009;43:215–229.
  42. Binder EB, Salyakina D, Lichtner P, Wochnik GM, Ising M, Putz B, Papiol S, Seaman S, Lucae S, Kohli MA, et al: Polymorphisms in FKBP5 are associated with increased recurrence of depressive episodes and rapid response to antidepressant treatment. Nat Genet 2004;36:1319–1325.
  43. Ashburner J: A fast diffeomorphic image registration algorithm. Neuroimage 2007;38:95–113.
  44. Ashburner J, Friston KJ: Unified segmentation. Neuroimage 2005;26:839–851.
  45. Amunts K, Kedo O, Kindler M, Pieperhoff P, Mohlberg H, Shah NJ, Habel U, Schneider F, Zilles K: Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anat Embryol 2005;210:343–352.
  46. Eickhoff SB, Stephan KE, Mohlberg H, Grefkes C, Fink GR, Amunts K, Zilles K: A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage 2005;25:1325–1335.