Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Application of Machine-Learning Methods to Understand Gene Expression Regulation Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

2. Identification of Novel Genetic Models of Glaucoma Using the “EMERGENT” Genetic Programming-Based Artificial Intelligence System

verfasst von : Jason H. Moore, Casey S. Greene, Douglas P. Hill

Erschienen in: Genetic Programming Theory and Practice XII

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The genetic basis for primary open-angle glaucoma (POAG) is not yet understood but is likely the result of many interacting genetic variants that influence risk in the context of our local ecology. The complexity of the genotype to phenotype mapping relationship for common diseases like POAG necessitates analytical approaches that move beyond parametric statistical methods such as logistic regression that assume a particular mathematical model. This is particularly important in the era of big data where it is routine to collect and analyze data sets with hundreds of thousands of measured genetic variants in thousands of human subjects. We introduce here the Exploratory Modeling for Extracting Relationships using Genetic and Evolutionary Navigation Techniques (EMERGENT) algorithm as an artificial intelligence approach to the genetic analysis of common human diseases. EMERGENT builds models of genetic variation from lists of mathematical functions using a form of genetic programming called computational evolution. A key feature of the system is the ability to utilize pre-processed expert knowledge giving it the ability to explore model space much as a human would. We describe this system in detail and then apply it to the genetic analysis of POAG in the Glaucoma Gene Environment Initiative (GLAUGEN) study that included approximately 1,272 subjects with the disease and 1057 healthy controls. A total of 657,366 single-nucleotide polymorphisms (SNPs) from across the human genome were measured in these subjects and available for analysis. Analysis using the EMERGENT framework revealed a best model consisting of six SNPs that map to at least six different genes. Two of these genes have previously been associated with POAG in several studies. The others represent new hypotheses about the genetic basis of POAG. All of the SNPs are involved in non-additive gene-gene interactions. Further, the six genes are all directly or indirectly related through biological interactions to the vascular endothelial growth factor (VEGF) gene that is an actively investigated drug target for POAG. This study demonstrates the routine application of an artificial intelligence-based system for the genetic analysis of complex human diseases.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Application of Machine-Learning Methods to Understand Gene Expression Regulation
Nächstes Kapitel Inheritable Epigenetics in Genetic Programming
Literatur
Banzhaf W, Francone FD, Keller RE, Nordin P (1998) Genetic programming: an introduction: on the automatic evolution of computer programs and its applications. Morgan Kaufmann Publishers Inc., San FranciscoMATH
Banzhaf W, Beslon G, Christensen S, Foster J, Kepes F, Lefort V, Miller J, Radman M, Ramsden J (2006) From artificial evolution to computational evolution: a research agenda. Nature Rev Genet 7:729–735CrossRef
Bush WS, Moore JH (2012) Chapter 11: Genome-wide association studies. PLoS Comput Biol 8(12):e1002822. doi:10.1371/journal.pcbi.1002822CrossRef
Clark AF, Yorio T (2003) Ophthalmic drug discovery. Nat Rev Drug Discov 2(6):448–459. doi:10.1038/nrd1106CrossRef
Cooke Bailey JN, Sobrin L, Pericak-Vance MA, Haines JL, Hammond CJ, Wiggs JL (2013) Advances in the genomics of common eye diseases. Hum Mol Genet 22(R1):R59–65. doi:10.1093/hmg/ddt396CrossRef
Cornelis MC, Agrawal A, Cole JW, Hansel NN, Barnes KC, Beaty TH, Bennett SN, Bierut LJ, Boerwinkle E, Doheny KF, Feenstra B, Feingold E, Fornage M, Haiman CA, Harris EL, Hayes MG, Heit JA, Hu FB, Kang JH, Laurie CC, Ling H, Manolio TA, Marazita ML, Mathias RA, Mirel DB, Paschall J, Pasquale LR, Pugh EW, Rice JP, Udren J, van Dam RM, Wang X, Wiggs JL, Williams K, Yu K (2010) The gene, environment association studies consortium (geneva): maximizing the knowledge obtained from gwas by collaboration across studies of multiple conditions. Genet Epidemiol 34(4):364–372. doi:10.1002/gepi.20492CrossRef
Fan R, Zhong M, Wang S, Zhang Y, Andrew A, Karagas M, Chen H, Amos CI, Xiong M, Moore JH (2011) Entropy-based information gain approaches to detect and to characterize gene-gene and gene-environment interactions/correlations of complex diseases. Genet Epidemiol 35(7):706–721. doi:10.1002/gepi.20621CrossRef
Fogel GB, Corne DW (eds) (2003) Evolutionary computation in bioinformatics. Morgan Kaufmann Publishers Inc.
Greene CS, Hill DP, Moore JH (2009a) Environmental noise improves epistasis models of genetic data discovered using a computational evolution system. In: Proceedings of the 11th annual conference on genetic and evolutionary computation, ACM, New York, NY, GECCO '09, pp 1785–1786. doi:10.1145/1569901.1570160. http://​doi.​acm.​org/​10.​1145/​1569901.​1570160
Greene CS, Hill DP, Moore JH (2009b) Environmental sensing of expert knowledge in a computational evolution system for complex problem solving in human genetics. In: Riolo RL, O’Reilly UM, McConaghy T (eds) Genetic programming theory and practice VII, genetic and evolutionary computation. Springer, Ann Arbor, pp 19–36
Hahn LW, Ritchie MD, Moore JH (2003) Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 19(3):376–382CrossRef
Hirschhorn JN, Daly MJ (2005) Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 6(2):95–108. doi:10.1038/nrg1521CrossRef
Horn J, Nafpliotis N, Goldberg DE (1994) A niched pareto genetic algorithm for multiobjective optimization. In: Evolutionary Computation, 1994. IEEE world congress on computational intelligence, proceedings of the first IEEE conference on, pp 82–87 vol.1. doi:10.1109/ICEC.1994.350037
Hornby GS (2006) Alps: the age-layered population structure for reducing the problem of premature convergence. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation, ACM, New York, NY, GECCO '06, pp 815–822. doi:10.1145/1143997.1144142
Horsley MB, Kahook MY (2010) Anti-vegf therapy for glaucoma. Curr Opin Ophthalmol 21(2):112–117. doi:10.1097/ICU.0b013e3283360aadCrossRef
Hu T, Sinnott-Armstrong NA, Kiralis JW, Andrew AS, Karagas MR, Moore JH (2011) Characterizing genetic interactions in human disease association studies using statistical epistasis networks. BMC Bioinform 12:364. doi:10.1186/1471-2105-12-364CrossRef
Hu T, Chen Y, Kiralis JW, Moore JH (2013) Visen: methodology and software for visualization of statistical epistasis networks. Genet Epidemiol 37(3):283–285. doi:10.1002/gepi.21718CrossRef
Mailman MD, Feolo M, Jin Y, Kimura M, Tryka K, Bagoutdinov R, Hao L, Kiang A, Paschall J, Phan L, Popova N, Pretel S, Ziyabari L, Lee M, Shao Y, Wang ZY, Sirotkin K, Ward M, Kholodov M, Zbicz K, Beck J, Kimelman M, Shevelev S, Preuss D, Yaschenko E, Graeff A, Ostell J, Sherry ST (2007) The ncbi dbgap database of genotypes and phenotypes. Nat Genet 39(10):1181–1186. doi:10.1038/ng1007-1181CrossRef
Moore J (2003) The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Human Heredity 56:73–82CrossRef
Moore JH, Williams SM (2005) Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis. Bioessays 27(6):637–646. doi:10.1002/bies.20236CrossRef
Moore JH, Williams SM (2009) Epistasis and its implications for personal genetics. Am J Hum Genet 85(3):309–320. doi:10.1016/j.ajhg.2009.08.006CrossRef
Moore JH, Parker JS, Olsen NJ, Aune TM (2002) Symbolic discriminant analysis of microarray data in autoimmune disease. Genet Epidemiol 23(1):57–69CrossRef
Moore JH, Barney N, Tsai CT, Chiang FT, Gui J, White BC (2007) Symbolic modeling of epistasis. Hum Hered 63(2):120–133. doi:10.1159/000099184CrossRef
Moore JH, Andrews PC, Barney N, White BC (2008a) Development and evaluation of an open-ended computational evolution system for the genetic analysis of susceptibility to common human diseases. In: Marchiori E, Moore JH (eds) EvoBIO, Springer, lecture notes in computer science, vol 4973, pp 129–140
Moore JH, Barney N, White BC (2008b) Solving complex problems in human genetics using genetic programming: the importance of theorist-Practitionercomputer interaction. Springer US, pp 69–85
Moore JH, Greene CS, Andrews PC, White BC (2008c) Does complexity matter? artificial evolution, computational evolution and the genetic analysis of epistasis in common human diseases. In: Riolo RL, Soule T, Worzel B (eds) Genetic programming theory and practice VI, genetic and evolutionary computation. Springer, Ann Arbor, pp 125–145. doi:10.1007/978-0-387-87623-8-9
Moore JH, Asselbergs FW, Williams SM (2010) Bioinformatics challenges for genome-wide association studies. Bioinformatics 26(4):445–455. doi:10.1093/bioinformatics/btp713CrossRef
Moore JH, Hill DP, Fisher JM, Lavender N, Kidd LC (2011) Human-computer interaction in a computational evolution system for the genetic analysis of cancer. In: Riolo R, Vladislavleva E, Moore JH (eds) Genetic programming theory and practice IX, genetic and evolutionary computation, Springer, Ann Arbor, pp 153–171. doi:10.1007/978-1-4614-1770-5-9
Moore JH, Hill DP, Sulovari A, Kidd L (2012) Genetic analysis of prostate cancer using computational evolution, pareto-optimization and post-processing. In: Riolo R, Vladislavleva E, Ritchie MD, Moore JH (eds) Genetic programming theory and practice X, genetic and evolutionary computation. Springer, Ann Arbor, pp 87–101. doi:10.1007/978-1-4614-6846-2-7. http://​dx.​doi.​org/​10.​1007/​978-1-4614-6846-2-7
Moore JH, Hill DP, Saykin A, Shen L (2013) Exploring interestingness in a computational evolution system for the genome-wide genetic analysis of alzheimer’s disease. In: Riolo R, Moore JH, Kotanchek M (eds) Genetic programming theory and practice XI, genetic and evolutionary computation. Springer, pp 31–45. doi:10.1007/978-1-4939-0375-7-2
Osaadon P, Fagan XJ, Lifshitz T, Levy J (2014) A review of anti-vegf agents for proliferative diabetic retinopathy. Eye (Lond) 28(5):510–520. doi:10.1038/eye.2014.13CrossRef
Pattin KA, Payne JL, Hill DP, Caldwell T, Fisher JM, Moore JH (2010) Exploiting expert knowledge of protein-protein interactions in a computational evolution system for detecting epistasis. In: Riolo R, McConaghy T, Vladislavleva E (eds) Genetic programming theory and practice VIII, genetic and evolutionary computation, vol 8. Springer, Ann Arbor. (Chap 12, pp 195–210, http://​www.​springer.​com/​computer/​ai/​book/​978-1-4419-7746-5)
Payne J, Greene C, Hill D, Moore J (2010) Exploitation of linkage learning in evolutionary algorithms. Springer. (chap 10: Sensible initialization of a computational evolution system using expert knowledge for epistasis analysis in human genetics), pp 215–226
Ritchie M, Hahn L, Roodi N, Bailey L, Dupont W, Parl F, Moore J (2001) Multifactor dimensionality reduction reveals high-order interactions among estrogen metabolism genes in sporadic breast cancer. Am J Hum Genetics 69:138–147CrossRef
Ritchie MD, White BC, Parker JS, Hahn LW, Moore JH (2003) Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases. BMC Bioinform 4:28. doi:10.1186/1471-2105-4-28CrossRef
SooHoo JR, Seibold LK, Kahook MY (2014) The link between intravitreal antivascular endothelial growth factor injections and glaucoma. Curr Opin Ophthalmol 25(2):127–133. doi:10.1097/ICU.0000000000000036CrossRef
Spector L, Robinson A (2002) Genetic programming and autoconstructive evolution with the push programming language. Genet Program Evolvable Mach pp 7–40
Tyler AL, Asselbergs FW, Williams SM, Moore JH (2009) Shadows of complexity: what biological networks reveal about epistasis and pleiotropy. Bioessays 31(2):220–227. doi:10.1002/bies.200800022CrossRef
Wang WYS, Barratt BJ, Clayton DG, Todd JA (2005) Genome-wide association studies: theoretical and practical concerns. Nat Rev Genet 6(2):109–118. doi:10.1038/nrg1522CrossRef
Wang X, Harmon J, Zabrieskie N, Chen Y, Grob S, Williams B, Lee C, Kasuga D, Shaw PX, Buehler J, Wang N, Zhang K (2010) Using the utah population database to assess familial risk of primary open angle glaucoma. Vision Res 50(23):2391–2395. doi:10.1016/j.visres.2010.09.018CrossRef
Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H, Klemm A, Flicek P, Manolio T, Hindorff L, Parkinson H (2014) The nhgri gwas catalog, a curated resource of snp-trait associations. Nucleic Acids Res 42(Database issue):D1001–6. doi:10.1093/nar/gkt1229CrossRef
Wong AK, Park CY, Greene CS, Bongo LA, Guan Y, Troyanskaya OG (2012) Imp: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks. Nucleic Acids Res 40(Web Server issue):W484–90. doi:10.1093/nar/gks458CrossRef
Metadaten
Titel
Identification of Novel Genetic Models of Glaucoma Using the “EMERGENT” Genetic Programming-Based Artificial Intelligence System
verfasst von
Jason H. Moore
Casey S. Greene
Douglas P. Hill
Copyright-Jahr
2015
Buch
Genetic Programming Theory and Practice XII

Print ISBN: 978-3-319-16029-0

Electronic ISBN: 978-3-319-16030-6

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-16030-6_2