Abstract
The vast amount of available genomics data provides us an unprecedented ability to survey the entire genome and search for the genetic determinants of complex diseases. Until now, Genome-wide association studies have been the predominant method to associate DNA variations to disease traits. GWAS have successfully uncovered many genetic variants associated with complex diseases when the effect loci are strongly associated with the trait. However, methods for studying interaction effects among multiple loci are still lacking. Established machine learning methods such as the grammatical evolution neural networks (GENN) can be adapted to help us uncover the missing interaction effects that are not captured by GWAS studies. We used an implementation of GENN distributed in the software package ATHENA (Analysis Tool for Heritable and Environmental Network Associations) to investigate the effects of multiple GENN parameters and data noise levels on model detection and network structure. We concluded that the models produced by GENN were greatly affected by algorithm parameters and data noise levels. We also produced complex, multi-layer networks that were not produced in the previous study. In summary, GENN can produce complex, multi-layered networks when the data require it for higher fitness and when the parameter settings allow for a wide search of the complex model space.
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References
Andrew AS, Hu T, Gu J, Gui J, Ye Y, Marsit CJ, Kelsey KT, Schned AR, Tanyos SA, Pendleton EM, Mason RA, Morlock EV, Zens MS, Li Z, Moore JH, Wu X, Karagas MR (2012) HSD3B and gene-gene interactions in a pathway-based analysis of genetic susceptibility to bladder cancer. PLoS One 7(12):e51301
Breiman L (2001) Random forests. Mach Learn 45:5–32
Edwards T, Bush W, Turner S, Dudek S, Torstenson E, Schmidt M, Martin E, Ritchie M (2008) Generating linkage disequilibrium patterns in data simulations using genomesimla. Evol Comput Mach Learn Data Min Bioinform 4973:24–35
Gibson G, Riley-Berger R, Harshman L, Kopp A, Vacha S, Nuzhdin S, Wayne M (2004) Extensive sex-specific nonadditivity of gene expression in drosophila melanogaster. Genetics 167:1791–1799. 104026583
Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio T (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA 106:9362–9367
Koza JR, Rice JP (1991) Genetic generation of both the weights and architecture for a neural network. In: International joint conference on neural networks, IJCNN-91, Washington State Convention and Trade Center, Seattle, vol II. IEEE Computer Society, pp 397–404
Motsinger AA, Lee SL, Mellick G, Ritchie MD (2006) GPNN: power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease. BMC Bioinform [electronic resource] 7(1):39–39
Motsinger-Reif AA, Dudek SM, Hahn LW, Ritchie MD (2001) Comparison of approaches for machine-learning optimization of neural networks for detecting gene-gene interactions in genetic epidemiology. Genet Epidemiol 32:325–340
O’Neill M, Ryan C (2001) Grammatical evolution. IEEE Trans Evol Comput 5(4):349–358
O’Neill M, Ryan C (2003) Grammatical evolution: evolutionary automatic programming in a arbitrary language. Volume 4 of genetic programming. Kluwer, Boston
Pearson B, Lau K, Allen A, Barron J, Cool R, Davis K, DeLoache W, Feeney E, Gordon A, Igo J, Lewis A, Muscalino K, Madeline P, Penumetcha P, Rinker V, Roland K, Zhu X, Poet J, Eckdahl T, Heyer L, Campbell A (2011) Bacterial hash function using DNA-based XOR logic reveals unexpected behavior of the LuxR promoter. IBC 3, article no 10:1–10
Privman V, Zhou J, Halamek J, Katz E (2010) Realization and properties of biochemical-computing biocatalytic XOR gate based on signal change. J Phys Chem B 114:13601–13608
Ritchie MD, Holzinger ER, Dudek SM, Frase AT, Chalise P, Fridley B (2012) Meta-dimensional analysis of phenotypes using the Analysis Tool for Heritable and Environmental Network Associations (ATHENA): challenges with building large networks. In: Riolo R et al (eds) Genetic programming theory and practice X. Springer, New York
Skapura D (1995) Building neural networks. ACM, New York
Turner SD, Dudek SM, Ritchie MD (2010) Grammatical evolution of neural networks for discovering epistasis among quantitative trait loci. In: Pizzuti C, Ritchie MD, Giacobini M (eds) 8th European conference on evolutionary computation, machine learning and data mining in bioinformatics (EvoBIO 2010), Istanbul. Volume 6023 of lecture notes in computer science, pp 86–97. Springer
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Li, R., Holzinger, E.R., Dudek, S.M., Ritchie, M.D. (2014). Evaluation of Parameter Contribution to Neural Network Size and Fitness in ATHENA for Genetic Analysis. In: Riolo, R., Moore, J., Kotanchek, M. (eds) Genetic Programming Theory and Practice XI. Genetic and Evolutionary Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0375-7_12
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DOI: https://doi.org/10.1007/978-1-4939-0375-7_12
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