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QMSim specifications


Unique identifier OMICS_15331
Name QMSim
Software type Package/Module
Interface Graphical user interface
Restrictions to use Academic or non-commercial use
Input data A parameter file, in which various parameters for the simulation are specified.
Output data Pedigree information, population structure, phenotypes, true genetic values, crossover positions, LD statistics, linkage map, phased genotypes and allele frequencies and effects.
Output format TXT
Operating system Unix/Linux, Mac OS, Windows
Programming languages C++
Computer skills Medium
Version 1.10
Stability Stable
Maintained Yes




No version available



  • person_outline Mehdi Sargolzaei <>

Publication for QMSim

QMSim citations


Inbreeding and runs of homozygosity before and after genomic selection in North American Holstein cattle

PMCID: 5787230
PMID: 29374456
DOI: 10.1186/s12864-018-4453-z

[…] per year has become steeper. therefore, the management of inbreeding has become a more urgent issue than in the past., a base population consisted of 500 females and 50 males was simulated using qmsim []. sixty overlapping generations were generated by mating 50 sires at random to 500 dams. each dam produced two progeny in each generation. sire and dam replacement rates were 0.5 and 0.3, […]


High density marker panels, SNPs prioritizing and accuracy of genomic selection

PMCID: 5756446
PMID: 29304753
DOI: 10.1186/s12863-017-0595-2

[…] fst scores as an external source of information to prioritize snp markers in the association models and to compare its performance with currently used approaches., simulation was carried out using qmsim software []. a historical population was generated based on random mating of 8000 animals for 300 generations followed by an additional 15 generations of random mating with population size […]


Which Individuals To Choose To Update the Reference Population? Minimizing the Loss of Genetic Diversity in Animal Genomic Selection Programs

PMCID: 5765340
PMID: 29133511
DOI: 10.1534/g3.117.1117

[…] one offspring per generation and the sex ratio in the offspring generation was 0.5 (). the simulated design is simpler than what occurs in a real breeding scheme. simulations were performed using qmsim (). details of the simulation process are provided in supplemental material (file s1)., to investigate the impact of an update to the reference population on gs in terms of subsequent predicted […]


Application of single step genomic BLUP under different uncertain paternity scenarios using simulated data

PMCID: 5619718
PMID: 28957330
DOI: 10.1371/journal.pone.0181752

[…] uncertainty with different strategies of scaling the g matrix to match the a22 matrix, using simulated data for beef cattle., phenotypes, pedigree, and genotypes were simulated using the software qmsim version 1.00 []. two traits assuming low and moderate heritabilities were simulated: age at first calving (afc; h2 = 0.12) and weight at 550 days (w550; h2 = 0.34). heritabilities were based […]


Incorporation of causative quantitative trait nucleotides in single step GBLUP

PMCID: 5530494
PMID: 28747171
DOI: 10.1186/s12711-017-0335-0

[…] \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{s}_{i}^{2} = s_{i}^{2}$$\end{document}s^i2=si2)., using the software qmsim [], we simulated a livestock population under selection for a single quantitative trait that has a heritability of 0.3. a historical population was generated by mutation and drift over 1000 […]


Prediction of the reliability of genomic breeding values for crossbred performance

PMCID: 5439167
PMID: 28499351
DOI: 10.1186/s12711-017-0318-1

[…] on phenotypes corrected with the best linear unbiased estimates of the fixed effects, and assuming the absence of selection []., first, historical and breed populations were simulated using the qmsim software []; second, a two-way crossbreeding program with five generations of random selection was simulated using a customized fortran program. for the historical population, 1000 discrete […]

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QMSim institution(s)
Department of Animal and Poultry Science, University of Guelph, Guelph, ON, Canada
QMSim funding source(s)
Supported by L’Alliance Boviteq Inc. (SEMEX Alliance, Canada); Natural Sciences and Engineering Research Council of Canada (Collaborative Research and Development grant); Ontario Centre for Agriculture Genomics (Challenge Fund).

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