Grammatical evolution of neural networks for discovering epistasis among quantitative trait loci

Authors:
Stephen D. Turner;Scott M. Dudek;Marylyn D. Ritchie
Affiliations:
Center for Human Genetics Research, Department of Molecular Physiology S Biophysics, Vanderbilt University, Nashville, TN;Center for Human Genetics Research, Department of Molecular Physiology S Biophysics, Vanderbilt University, Nashville, TN;Center for Human Genetics Research, Department of Molecular Physiology S Biophysics, Vanderbilt University, Nashville, TN
Venue:
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Year:
2010

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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.