PyToxo: a Python tool for calculating penetrance tables of high-order epistasis models

• Published in: BMC bioinformatics Vol. 23; no. 1; pp. 117 - 13
• Main Authors: González-Seoane, Borja, Ponte-Fernández, Christian, González-Domínguez, Jorge, Martín, María J.
• Format: Journal Article
• Language: English
• Published: London BioMed Central 02.04.2022; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Batch processing; Bioinformatics; Biomedical and Life Sciences; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Epistasis; Epistasis model; Epistasis, Genetic; Gene interaction; Genetic epistasis; Genetic research; Genomes; Genotype & phenotype; Heritability; Interfaces; Life Sciences; Medical genetics; Microarrays; Models, Genetic; Open source software; Penetrance; Phenotype; Phenotypes; Prevalence; Public software; Python (Programming language); Simulation; Software; Spain; Prevalence; Simulation; Epistasis model; Gene interaction; Penetrance; Heritability; SymPy; Python
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-022-04645-7

Background Epistasis is the interaction between different genes when expressing a certain phenotype. If epistasis involves more than two loci it is called high-order epistasis. High-order epistasis is an area under active research because it could be the cause of many complex traits. The most common way to specify an epistasis interaction is through a penetrance table. Results This paper presents PyToxo, a Python tool for generating penetrance tables from any-order epistasis models. Unlike other tools available in the bibliography, PyToxo is able to work with high-order models and realistic penetrance and heritability values, achieving high-precision results in a short time. In addition, PyToxo is distributed as open-source software and includes several interfaces to ease its use. Conclusions PyToxo provides the scientific community with a useful tool to evaluate algorithms and methods that can detect high-order epistasis to continue advancing in the discovery of the causes behind complex diseases.