GEPSi: A Python Library to Simulate GWAS Phenotype Data

Abstract

MotivationMany computational methods aim to identify genetic variants associated with diseases and complex traits. Due to the absence of ground truth data, simulated genotype and phenotype data is needed to benchmark these methods. However, phenotypes are frequently simulated as an additive function of randomly selected variants, neglecting biological complexity such as non-random occurrence of causal SNPs, epistatic effects, heritability and dominance. Including such features would improve benchmarking studies and accelerate the development of methods for genetic analysis. ResultsHere, we describe GEPSi (GWAS Epistatic Phenotype Simulator), a user-friendly python package to simulate phenotype data based on user-supplied genotype data for a population. GEPSi incorporates diverse biological parameters such as heritability, dominance, population stratification and epistatic interactions between SNPs. We demonstrate the use of this package to compare machine learning methods for GWAS analysis. Availability and ImplementationGEPSi is freely available under an Apache 2.0 license, and can be downloaded from https://github.com/clara-parabricks/GEPSi. Supplementary informationSupplementary data are available online.