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https://doi.org/10.5281/zenodo.3688461
08 December 2025, 19:22:31 UTC
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    README.md
    ## R/qtl: A QTL mapping environment
    
    [![R-CMD-check](https://github.com/kbroman/qtl/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/kbroman/qtl/actions/workflows/R-CMD-check.yaml)
    [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/qtl)](https://cran.r-project.org/package=qtl)
    [![zenodo DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3688461.svg)](https://doi.org/10.5281/zenodo.3688461)
    
    **Authors**: Karl W Broman and Hao Wu, with ideas from Gary Churchill
    and Śaunak Sen and contributions from Danny Arends,
    Robert Corty, Timothée Flutre, Ritsert Jansen, Pjotr Prins, Lars
    Rönnegård, Rohan Shah, Laura Shannon, Quoc Tran, Aaron
    Wolen, and Brian Yandell
    
    [R/qtl](https://rqtl.org) is an extensible, interactive environment for mapping
    quantitative trait loci (QTL) in experimental crosses. It is
    implemented as an add-on package for the freely available and widely
    used statistical language/software [R](https://www.r-project.org).
    The development of this software as an add-on to R allows us to take
    advantage of the basic mathematical and statistical functions, and
    powerful graphics capabilities, that are provided with R. Further, the
    user will benefit by the seamless integration of the QTL mapping
    software into a general statistical analysis program. Our goal is to
    make complex QTL mapping methods widely accessible and allow users to
    focus on modeling rather than computing.
    
    A key component of computational methods for QTL mapping is the hidden
    Markov model (HMM) technology for dealing with missing genotype
    data. We have implemented the main HMM algorithms, with allowance for
    the presence of genotyping errors, for backcrosses, intercrosses, and
    phase-known four-way crosses.
    
    The current version of R/qtl includes facilities for estimating
    genetic maps, identifying genotyping errors, and performing single-QTL
    genome scans and two-QTL, two-dimensional genome scans, by interval
    mapping (with the EM algorithm), Haley-Knott regression, and multiple
    imputation. All of this may be done in the presence of covariates
    (such as sex, age or treatment). One may also fit higher-order QTL
    models by multiple imputation and Haley-Knott regression.
    
    ### License
    
    The R/qtl package is free software; you can redistribute it and/or
    modify it under the terms of the GNU General Public License,
    version 3, as published by the Free Software Foundation.
    
    This program is distributed in the hope that it will be useful, but
    without any warranty; without even the implied warranty of
    merchantability or fitness for a particular purpose.  See the GNU
    General Public License for more details.
    
    A copy of the GNU General Public License, version 3, is available at
    <https://www.r-project.org/Licenses/GPL-3>
    

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