Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection

By Ryan J. Urbanowicz, Jeff Kiralis, Jonathan M. Fisher, Jason H. Moore. 2012

35 citations recorded by the source corpus.

Algorithms designed to detect complex genetic disease associations are initially evaluated using simulated datasets. Typical evaluations vary constraints that influence the correct detection of underlying models (i.e. number of loci, heritability, and minor allele frequency). Such studies neglect to account for model architecture (i.e. the unique specification and arrangement of penetrance values comprising the genetic model), which alone can influence the detectability of a model. In order to design a simulation study which efficiently takes architecture into account, a reliable metric is needed for model selection. We evaluate three metrics as predictors of relative model detection difficulty derived from previous works: (1) Penetrance table variance (PTV), (2) customized odds ratio (COR), and (3) our own Ease of Detection Measure (EDM), calculated from the penetrance values and respective genotype frequencies of each simulated genetic model. We evaluate the reliability of these metrics across three very different data search algorithms, each with the capacity to detect epistatic interactions. We find that a model’s EDM and COR are each stronger predictors of model detection success than heritability. This study formally identifies and evaluates metrics which quantify model detection difficulty. We utilize these metrics to intelligently select models from a population of potential architectures. This allows for an improved simulation study design which accounts for differences in detection difficulty attributed to model architecture. We implement the calculation and utilization of EDM and COR into GAMETES, an algorithm which rapidly and precisely generates pure, strict, n-locus epistatic models.

Subfields: Genetics

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Citations in the corpus

Automating Biomedical Data Science Through Tree-Based Pipeline Optimization (2016, methodology): “The 8 predictive SNPs were simulated as four separate purely epistatic models, additively combined using the newly-added “hierarchical” data simulation feature in GAMETES.”
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science (2016, methodology): “We generate a total of 12 genetic models and 360 associated data sets using GAMETES [20], an open source software package designed to generate a diverse spectrum of pure, strict epistatic genetic models.”
Detecting gene-gene interactions using a permutation-based random forest method (2016, methodology): “Data and models constructed this way can also be ranked by the relative ease of detection metric (EDM), a score that is calculated directly from the model itself, and can lead to further generation of models based on the needs of simulation studies [33].”