Abstract
Susceptibility to Alzheimer’s disease is likely due to complex interaction among many genetic and environmental factors. Identifying complex genetic effects in large data sets will require computational methods that extend beyond what parametric statistical methods such as logistic regression can provide. We have previously introduced a computational evolution system (CES) that uses genetic programming (GP) to represent genetic models of disease and to search for optimal models in a rugged fitness landscape that is effectively infinite in size. The CES approach differs from other GP approaches in that it is able to learn how to solve the problem by generating its own operators. A key feature is the ability for the operators to use expert knowledge to guide the stochastic search. We have previously shown that CES is able to discover nonlinear genetic models of disease susceptibility in both simulated and real data. The goal of the present study was to introduce a measure of interestingness into the modeling process. Here, we define interestingness as a measure of non-additive gene-gene interactions. That is, we are more interested in those CES models that include attributes that exhibit synergistic effects on disease risk. To implement this new feature we first pre-processed the data to measure all pairwise gene-gene interaction effects using entropy-based methods. We then provided these pre-computed measures to CES as expert knowledge and as one of three fitness criteria in three-dimensional Pareto optimization. We applied this new CES algorithm to an Alzheimer’s disease data set with approximately 520,000 genetic attributes. We show that this approach discovers more interesting models with the added benefit of improving classification accuracy. This study demonstrates the applicability of CES to genome-wide genetic analysis using expert knowledge derived from measures of interestingness.
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Acknowledgements
This work was supported by NIH grants LM011360, LM009012, LM010098 and AI59694. We would like to thank the participants of present and past Genetic Programming Theory and Practice Workshops (GPTP) for their stimulating feedback and discussion that helped formulate some of the ideas in this paper.
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Moore, J.H., Hill, D.P., Saykin, A., Shen, L. (2014). Exploring Interestingness in a Computational Evolution System for the Genome-Wide Genetic Analysis of Alzheimer’s Disease. 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_2
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