Abstract
A goal of human genetics is to discover genetic factors that influence individuals’ susceptibility to common diseases. Most common diseases are thought to result from the joint failure of two or more interacting components instead of single component failures. This greatly complicates both the task of selecting informative genetic variations and the task of modeling interactions between them. We and others have previously developed algorithms to detect and model the relationships between these genetic factors and disease. Previously these methods have been evaluated with datasets simulated according to pre-defined genetic models. Here we develop and evaluate a model free evolution strategy to generate datasets which display a complex relationship between individual genotype and disease susceptibility. We show that this model free approach is capable of generating a diverse array of datasets with distinct gene-disease relationships for an arbitrary interaction order and sample size. We specifically generate six-hundred pareto fronts; one for each independent run of our algorithm. In each run the predictiveness of single genetic variation and pairs of genetic variations have been minimized, while the predictiveness of third, fourth, or fifth order combinations is maximized. This method and the resulting datasets will allow the capabilities of novel methods to be tested without pre-specified genetic models. This could improve our ability to evaluate which methods will succeed on human genetics problems where the model is not known in advance. We further make freely available to the community the entire pareto-optimal front of datasets from each run so that novel methods may be rigorously evaluated. These 56,600 datasets are available from http://discovery.dartmouth.edu/model_free_data/ .
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Greene, C.S., Himmelstein, D.S., Moore, J.H. (2010). A Model Free Method to Generate Human Genetics Datasets with Complex Gene-Disease Relationships. 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_7
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DOI: https://doi.org/10.1007/978-3-642-12211-8_7
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