GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures

By Ryan J. Urbanowicz, Jeff Kiralis, Nicholas A. Sinnott-Armstrong, Tamra Heberling, Jonathan M. Fisher, Jason H. Moore. 2012

214 citations recorded by the source corpus.

Geneticists who look beyond single locus disease associations require additional strategies for the detection of complex multi-locus effects. Epistasis, a multi-locus masking effect, presents a particular challenge, and has been the target of bioinformatic development. Thorough evaluation of new algorithms calls for simulation studies in which known disease models are sought. To date, the best methods for generating simulated multi-locus epistatic models rely on genetic algorithms. However, such methods are computationally expensive, difficult to adapt to multiple objectives, and unlikely to yield models with a precise form of epistasis which we refer to as pure and strict. Purely and strictly epistatic models constitute the worst-case in terms of detecting disease associations, since such associations may only be observed if all n-loci are included in the disease model. This makes them an attractive gold standard for simulation studies considering complex multi-locus effects. We introduce GAMETES, a user-friendly software package and algorithm which generates complex biallelic single nucleotide polymorphism (SNP) disease models for simulation studies. GAMETES rapidly and precisely generates random, pure, strict n-locus models with specified genetic constraints. These constraints include heritability, minor allele frequencies of the SNPs, and population prevalence. GAMETES also includes a simple dataset simulation strategy which may be utilized to rapidly generate an archive of simulated datasets for given genetic models. We highlight the utility and limitations of GAMETES with an example simulation study using MDR, an algorithm designed to detect epistasis. GAMETES is a fast, flexible, and precise tool for generating complex n-locus models with random architectures. While GAMETES has a limited ability to generate models with higher heritabilities, it is proficient at generating the lower heritability models typically used in simulation studies evaluating new algorithms. In addition, the GAMETES modeling strategy may be flexibly combined with any dataset simulation strategy. Beyond dataset simulation, GAMETES could be employed to pursue theoretical characterization of genetic models and epistasis.

Subfields: Genetics

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Citations in the corpus

Automating Biomedical Data Science Through Tree-Based Pipeline Optimization (2016, methodology): “We simulated two-locus epistatic genetic models with heritabilities of (0.1, 0.2, or 0.4) and attribute minor allele frequencies of 0.2 in GAMETES and selected the model with median diﬃculty from all those generated [15].”
TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning (2016, methodology): “We compiled 150 supervised classification benchmarks1 from a wide variety of sources, including the UCI machine learning repository [11], a large preexisting benchmark repository from [17], and simulated genetic analysis datasets from [19].”
Discovering causal interactions using Bayesian network scoring and information gain (2016, methodology): “Urbanowicz et al. [54] created GAMETES, which is a software package for generating pure, strict epistatic models with random architectures.”
Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection (2012, methodology): “n-loci, h2, MAFs, and K) we begin by generating a population of pure, strict, epistatic models of random architecture using the strategy outlined in [13].”
Detecting gene-gene interactions using a permutation-based random forest method (2016, methodology): “Genetic Architecture Model Emulator for Testing and Evaluating Software (GAMETES) is a user-friendly software designed by Urbanowicz et al. for simulation studies [31].”
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science (2016, methodology): “We simulate two-locus epistatic genetic models with heritabilities of (0.1, 0.2, or 0.4) and attribute minor allele frequencies of 0.2 in GAMETES and select the model with median diﬃculty from all those generated [18].”