Simulated data are crucial for evaluating epistasis detection tools in genome-wide association studies. Existing simulators are limited, as they do not account for linkage disequilibrium (LD), support limited interaction models of single nucleotide polymorphisms (SNPs) and only dichotomous phenotypes or depend on proprietary software. In contrast, EpiGEN supports SNP interactions of arbitrary order, produces realistic LD patterns and generates both categorical and quantitative phenotypes.EpiGEN is implemented in Python 3 and is freely available at https://github.com/baumbachlab/epigen.Supplementary data are available at Bioinformatics online.

EpiGEN: an epistasis simulation pipeline

Tieri;Paolo;
2020

Abstract

Simulated data are crucial for evaluating epistasis detection tools in genome-wide association studies. Existing simulators are limited, as they do not account for linkage disequilibrium (LD), support limited interaction models of single nucleotide polymorphisms (SNPs) and only dichotomous phenotypes or depend on proprietary software. In contrast, EpiGEN supports SNP interactions of arbitrary order, produces realistic LD patterns and generates both categorical and quantitative phenotypes.EpiGEN is implemented in Python 3 and is freely available at https://github.com/baumbachlab/epigen.Supplementary data are available at Bioinformatics online.
2020
Istituto Applicazioni del Calcolo ''Mauro Picone''
epistasis
simulated data
genome-wide association studies (GWAS)
linkage disequilibrium (LD)
SNP
categorical phenotypes
quantitative phenotypes
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/386741
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