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Grammatical Evolution of Neural Networks for Discovering Epistasis among Quantitative Trait Loci

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6023))

Abstract

Growing interest and burgeoning technology for discovering genetic mechanisms that influence disease processes have ushered in a flood of genetic association studies over the last decade, yet little heritability in highly studied complex traits has been explained by genetic variation. Non-additive gene-gene interactions, which are not often explored, are thought to be one source of this “missing” heritability. Here we present our assessment of the performance of grammatical evolution to evolve neural networks (GENN) for discovering gene-gene interactions which contribute to a quantitative heritable trait. We present several modifications to the GENN procedure which result in modest improvements in performance.

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References

  1. Risch, N., Merikangas, K.: The future of genetic studies of complex human disorders. Science 273(5281), 1516–1517 (1996)

    Article  Google Scholar 

  2. International hapmap consortium; The International HapMap Project. Nature 426(6968), 789–796 (2003)

    Google Scholar 

  3. International hapmap consortium; A second generation human haplotype map of over 3.1 million SNPs. Nature 449(7164), 851–861 (2007)

    Google Scholar 

  4. Maher, B.: Personal genomes: The case of the missing heritability. Nature 456(7218), 18–21 (2008)

    Article  Google Scholar 

  5. Cordell, H.J.: Genome-wide association studies: Detecting gene-gene interactions that underlie human diseases. Nat. Rev. Genet. (2009)

    Google Scholar 

  6. Wright, S.: The roles of mutation, inbreeding, crossbreeding, and selection in evolution. In: Proc. 6th Intl. Congress of Genetics, vol. 1, p. 356–366 (1932)

    Google Scholar 

  7. Moore, J.H., Williams, S.M.: Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis. Bioessays 27(6), 637–646 (2005)

    Article  Google Scholar 

  8. Ritchie, M.D., Hahn, L.W., Roodi, N., Bailey, L.R., Dupont, W.D., Parl, F.F., Moore, J.H.: Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am. J. Hum. Genet. 69(1), 138–147 (2001)

    Article  Google Scholar 

  9. Moore, J.H.: The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum. Hered. 56(1-3), 73–82 (2003)

    Article  Google Scholar 

  10. Hirschhorn, J.N.: Genomewide Association Studies – Illuminating Biologic Pathways. N. Engl. J. Med. 360(17), 1699–1701 (2009)

    Article  Google Scholar 

  11. Goldstein, D.B.: Common Genetic Variation and Human Traits. N. Engl. J. Med. 360(17), 1696–1698 (2009)

    Article  Google Scholar 

  12. Shao, H., Burrage, L.C., Sinasac, D.S., Hill, A.E., Ernest, S.R., O’Brien, W., Courtland, H.W., Jepsen, K.J., Kirby, A., Kulbokas, E.J., Daly, M.J., Broman, K.W., Lander, E.S., Nadeau, J.H.: Genetic architecture of complex traits: large phenotypic effects and pervasive epistasis. Proc. Natl. Acad. Sci. USA 105(50), 19910–19914 (2008)

    Article  Google Scholar 

  13. Carlson, C.S., Eberle, M.A., Kruglyak, L., Nickerson, D.A.: Mapping complex disease loci in whole-genome association studies. Nature 429(6990), 446–452 (2004)

    Article  Google Scholar 

  14. Kooperberg, C., Leblanc, M.: Increasing the power of identifying gene x gene interactions in genome-wide association studies. Genet. Epidemiol. 32(3), 255–263 (2008)

    Article  Google Scholar 

  15. Bellman, R.: Adaptive control processes. Princeton University Press, Princeton (1961)

    MATH  Google Scholar 

  16. Lou, X.Y., Chen, G.B., Yan, L., Ma, J.Z., Zhu, J., Elston, R.C., Li, M.D.: A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence. Am. J. Hum. Genet. 80(6), 1125–1137 (2007)

    Article  Google Scholar 

  17. Bush, W.S., Dudek, S.M., Ritchie, M.D.: Parallel multifactor dimensionality reduction: a tool for the large-scale analysis of gene-gene interactions. Bioinformatics 22(17), 2173–2174 (2006)

    Article  Google Scholar 

  18. Linder, R., Richards, T., Wagner, M.: Microarray data classified by artificial neural networks. Methods Mol. Biol. 382, 345–372 (2007)

    Article  Google Scholar 

  19. Lucek, P., Hanke, J., Reich, J., Solla, S.A., Ott, J.: Multi-locus nonparametric linkage analysis of complex trait loci with neural networks. Hum. Hered. 48(5), 275–284 (1998)

    Article  Google Scholar 

  20. Ott, J.: Neural networks and disease association studies. American Journal of Medical Genetics (Neuropsychiatric Genetics) 105(60), 61 (2001)

    Google Scholar 

  21. Porter, C.R., Crawford, E.D.: Combining artificial neural networks and transrectal ultrasound in the diagnosis of prostate cancer. Oncology (Williston. Park) 17(10), 1395–1399 (2003)

    Google Scholar 

  22. Sato, F., Shimada, Y., Selaru, F.M., Shibata, D., Maeda, M., Watanabe, G., Mori, Y., Stass, S.A., Imamura, M., Meltzer, S.J.: Prediction of survival in patients with esophageal carcinoma using artificial neural networks. Cancer 103(8), 1596–1605 (2005)

    Article  Google Scholar 

  23. Meiler, J., Baker, D.: Coupled prediction of protein secondary and tertiary structure. Proc. Natl. Acad. Sci. USA 100(21), 12105–12110 (2003)

    Article  Google Scholar 

  24. Bishop, C.M.: Neural Networks for Pattern Recognition, pp. 443–482. Oxford University Press, London (1995)

    Google Scholar 

  25. Yao, X.: Evolving artificial neural networks. Proceedings of the IEEE 87(9), 1423–1447 (1999)

    Article  Google Scholar 

  26. Ritchie, M.D., Coffey, C.S., Moore, J.H.: Genetic programming neural networks: A bioinformatics tool for human genetics. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 438–448. Springer, Heidelberg (2004)

    Google Scholar 

  27. Motsinger-Reif, A.A., Dudek, S.M., Hahn, L.W., Ritchie, M.D.: Comparison of approaches for machine-learning optimization of neural networks for detecting gene-gene interactions in genetic epidemiology. Genetic Epidemiology 32(4), 325–340 (2008)

    Article  Google Scholar 

  28. Koza, J., Rice, J.: Genetic generation of both the weights and architecture for a neural network. IEEE Transactions II (1991)

    Google Scholar 

  29. O’Neil, M., Ryan, C.: Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language, 1st edn. Kluwer Academic Publishers, Norwell (2003)

    Google Scholar 

  30. Turner, S.D., Ritchie, M.D., Bush, W.S.: Conquering the Needle-in-a-Haystack: How Correlated Input Variables Beneficially Alter the Fitness Landscape for Neural Networks. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EvoBIO 2009. LNCS, vol. 5483, pp. 80–91. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  31. Ritchie, M.D., Bartlett, J., Bush, W.S., Edwards, T.L., Motsinger, A.A., Torstenson, E.S.: Exploring epistasis in candidate genes for rheumatoid arthritis. BMC Proc. 1(suppl. 1), S70 (2007)

    Article  Google Scholar 

  32. Turner, S.D., Crawford, D.C., Ritchie, M.D.: Methods for optimizing statistical analyses in pharmacogenomics research. Expert Reviews in Clinical Pharmacology 2(5), 559–570 (2009)

    Article  Google Scholar 

  33. Edwards, T.L., Bush, W.S., Turner, S.D., Dudek, S.M., Torstenson, E.S., Schmidt, M., Martin, E., Ritchie, M.D.: Generating Linkage Disequilibrium Patterns in Data Simulations Using genomeSIMLA. In: Kortuem, G., Finney, J., Lea, R., Sundramoorthy, V. (eds.) EuroSSC 2007. LNCS, vol. 4793, pp. 24–35. Springer, Heidelberg (2007)

    Google Scholar 

  34. Cohen, P., Cohen, J., West, S.G., Aiken, L.S.: Applied Multiple Regres-sion/Correlation Analysis for the Behavioral Sciences, 3rd edn. Lawrence Erlbaum, Philadelphia (2002)

    Google Scholar 

  35. Schmidt, M.A., Hauser, E.R., Martin, E.R., Schmidt, S.: Extension of the SIMLA Package for Generating Pedigrees with Complex Inheritance Patterns: Environmental Covariates. Gene-Gene and Gene-Environment Interaction, Statistical Applications in Genetics and Molecular Biology 4(1), Article 15, 1–21 (2005)

    MathSciNet  Google Scholar 

  36. Freitas, A.: Understand the Crucial Role of Attribute Interactions in Data Mining, 16th edn., pp. 177–199 (2001)

    Google Scholar 

  37. Motsinger, A.A., Dudek, S.M., Hahn, L.W., Ritchie, M.D.: Comparison of Neural Network Optimization Approaches for Studies of Human Genetics. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 103–114. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  38. Motsinger, A.A., Hahn, L.W., Dudek, S.M., Ryckman, K.K., Ritchie, M.D.: Alternative cross-over strategies and selection techniques for grammatical evolution optimized neural networks. In: Proceedings of the 8th annual Genetic and Evolutionary Computation Conference (GECCO), vol. 8, pp. 947–948 (2006)

    Google Scholar 

  39. Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming. Lulu Enterprises, United Kingdom (2008)

    Google Scholar 

  40. Moore, J., Parker, J., Olsen, N., Aune, T.: Symbolic discriminant analysis of microarray data in autoimmune disease. Genet. Epidemiol. 23, 57–69 (2002)

    Article  Google Scholar 

  41. Baba, T., Azuma, S., Kashiwabara, S., Toyoda, Y.: Sperm from mice carrying a targeted mutation of the acrosin gene can penetrate the oocyte zona pellucida and effect fertilization. J. Biol. Chem. 269(50), 31845–31849 (1994)

    Google Scholar 

  42. Colucci-Guyon, E., Portier, M.M., Dunia, I., Paulin, D., Pournin, S., Babinet, C.: Mice lacking vimentin develop and reproduce without an obvious phenotype. Cell 79(4), 679–694 (1994)

    Article  Google Scholar 

  43. Gorry, P., Lufkin, T., Dierich, A., Rochette-Egly, C., Decimo, D., Dolle, P., Mark, M., Durand, B., Chambon, P.: The cellular retinoic acid binding protein I is dispensable. Proc. Natl. Acad. Sci. USA 91(19), 9032–9036 (1994)

    Article  Google Scholar 

  44. Gruda, M.C., van, A.J., Rizzo, C.A., Durham, S.K., Lira, S., Bravo, R.: Expression of FosB during mouse development: normal development of FosB knockout mice. Oncogene 12(10), 2177–2185 (1996)

    Google Scholar 

  45. Itohara, S., Mombaerts, P., Lafaille, J., Iacomini, J., Nelson, A., Clarke, A.R., Hooper, M.L., Farr, A., Tonegawa, S.: T cell receptor delta gene mutant mice: independent generation of alpha beta T cells and programmed rearrangements of gamma delta TCR genes. Cell 72(3), 337–348 (1993)

    Article  Google Scholar 

  46. Killeen, N., Stuart, S.G., Littman, D.R.: Development and function of T cells in mice with a disrupted CD2 gene. EMBO J. 11(12), 4329–4336 (1992)

    Google Scholar 

  47. Maxwell, S.E., Delaney, H.D.: Designing Experiments and Analyzing Data, 2nd edn. Lawrence Erlbaum Associates, Mahwah (2004)

    Google Scholar 

  48. Culverhouse, R., Suarez, B.K., Lin, J., Reich, T.: A perspective on epistasis: limits of models displaying no main effect. Am. J. Hum. Genet. 70(2), 461–471 (2002)

    Article  Google Scholar 

  49. Moore, J., Hahn, L., Ritchie, M., Thornton, T., White, B.: Application of genetic algorithms to the discovery of complex models for simulation studies in human genetics. In: Langdon, W., Cantu-Paz, E., Mathias, K., Roy, R., Davis, D., Poli, R., Balakrishnan, K., Honavar, V., Rudolph, G., Wegener, J., Bull, L., Potter, M., Schultz, A., Miller, J., Burke, E., Jonoska, N. (eds.), pp. 1150–1155. Morgan Kaufman Publishers, San Francisco (2002)

    Google Scholar 

  50. Penrod, N., Greene, C., Moore, J.: Failure to replicate a genetic association may provide important clues about genetic architecture. Presented at the annual meeting of The American Society of Human Genetics, Philadelphia PA, November 14 (2008)

    Google Scholar 

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Turner, S.D., Dudek, S.M., Ritchie, M.D. (2010). Grammatical Evolution of Neural Networks for Discovering Epistasis among Quantitative Trait Loci. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2010. Lecture Notes in Computer Science, vol 6023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12211-8_8

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  • DOI: https://doi.org/10.1007/978-3-642-12211-8_8

  • Publisher Name: Springer, Berlin, Heidelberg

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