Evolving hard problems: Generating human genetics datasets with a complex etiology

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 eight-hundred Pareto fronts; one for each independent run of our algorithm. In each run the predictiveness of single genetic variation and pairs of genetic variants have been minimized, while the predictiveness of third, fourth, or fifth-order combinations is maximized. Two hundred runs of the algorithm are further dedicated to creating datasets with predictive four or five order interactions and minimized lower-level effects.This method and the resulting datasets will allow the capabilities of novel methods to be tested without pre-specified genetic models. This allows researchers 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 76,600 datasets are available from http://discovery.dartmouth.edu/model_free_data/ webcite.Advances in genotyping technologies are changing the way geneticists measure genetic variation. It is now technologically feasible to measure more than one million variations from across the human genome. Here we focus on SNPs, or single nucleotide polymorphisms. A SNP is a single point in a DNA sequence that differs between individuals. A major goal in human genetics is to link the state of these SNPs to disease risk. The standard approach to this problem is to measure the genotypes of people with and without a disease of interest across hundreds of thousands to millions of SNPs. Each of these SNPs is then tested individually for an association with the d