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quantiNemo specifications


Unique identifier OMICS_12968
Name quantiNemo
Software type Package/Module
Interface Graphical user interface
Restrictions to use None
Input data Quantinemo is launched using a settings file. The settings file is a text file with a flexible and easy to understand structure, can be edited with any text editor, and comments may be added for better readability.
Input format FSTAT
Output data Provides summary statistics for the differrent simulation components, including genetic variance estimates, quantitative trait analysis, and F-statistics for all types of loci. Most of the summary statistics are available for juveniles and adults. The statistics can be computed for any generation during the simulation.
Operating system Unix/Linux, Mac OS, Windows
Programming languages C++
License GNU General Public License version 3.0
Computer skills Medium
Version 1.0.4
Stability Stable
Maintained Yes




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  • person_outline Samuel Neuenschwander

Publication for quantiNemo

quantiNemo citations


Complex genetic patterns in human arise from a simple range expansion model over continental landmasses

PLoS One
PMCID: 5821356
PMID: 29466398
DOI: 10.1371/journal.pone.0192460

[…] the simulation outcome that best match the empirical data are selected to define a posterior probability distribution for each parameter. genetic data were generated using a modified version of quantinemo [] in a two-step process. first, individual-based forward-in-time simulations produce the demography of the expanding population. then a backward in time coalescent-based process simulates […]


Evaluation of the 2b RAD method for genomic selection in scallop breeding

Sci Rep
PMCID: 4709697
PMID: 26754638
DOI: 10.1038/srep19244

[…] snps located in the bsaxi tags were considered for subsequent simulation analysis., a breeding population was simulated for 1000 generations according to the fisher-wright population model using the quantinemo software. the detailed parameters for the generation of simulated populations/families can be found in . the first simulation was generated as follows: 100 male and 100 female candidates […]


Genetic architecture and genomic patterns of gene flow between hybridizing species of Picea

PMCID: 4815442
PMID: 25806545
DOI: 10.1038/hdy.2015.19

[…] in this study., to illustrate how the joint distribution of ancestry and interspecific heterozygosity change in generations following initial admixture of parental populations, we used the software quantinemo () to simulate data under simplified assumptions of non-overlapping generations and random mating between hermaphroditic adults within each resulting patch. each simulation consisted […]


Population genomic analyses of early phase Atlantic Salmon (Salmo salar) domestication/captive breeding

Evol Appl
PMCID: 4310584
PMID: 25667605
DOI: 10.1111/eva.12230

[…] were recorded every second generation, and fst and its standard deviation were used to estimate divergence in the neutral loci and qtls. all simulations were conducted as implemented in the software quantinemo (neuenschwander et al. )., after the filtering steps, 4733 (r. burrishoole), 4039 (r. vindel/umeå), and 2797 (r. saint john) snp loci were retained, corresponding to approximately one snp […]


How many marker loci are necessary? Analysis of dominant marker data sets using two popular population genetic algorithms

Ecol Evol
PMCID: 3797491
PMID: 24223282
DOI: 10.1002/ece3.725

[…] observe the effects of among-region migration over time, model c individuals were sampled from generations 1, 2, 50, 100, and 150., the above metapopulations were evolved using the forward-simulator quantinemo (neuenschwander et al. ). the breeding system was modeled with individuals acting as randomly mating hermaphrodites to approximate the breeding system of p. arundinacea, a highly […]


Influence of mutation rate on estimators of genetic differentiation lessons from Arabidopsis thaliana

BMC Genet
PMCID: 2888750
PMID: 20433762
DOI: 10.1186/1471-2156-11-33

[…] of parameter values. for each simulated dataset we calculated the estimators of genetic differentiation as described below., next we examined how mutation rate at underlying qtl affects qst. we used quantinemo [], with the same settings as described above for neutral markers with the following exceptions: the number of qtl underlying the variation in the quantitative trait was 10 […]

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quantiNemo institution(s)
Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; INRA, UMR Genetique et Diversite Animales, Jouy-en-Josas, France; Department of Zoology, University of British Columbia, Vancouver, CB, Canada
quantiNemo funding source(s)
This work has been financially supported by a Swiss NSF grant no. 3100A0-108194/1 and a Herbette Foundation grant (University of Lausanne).

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