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MAPpoly (v. 0.3.0) is an R package to construct genetic maps in autopolyploids with even ploidy levels. In its current version, MAPpoly can handle ploidy levels up to 8 when using hidden Markov models (HMM), and up to 12 when using the two-point simplification. When dealing with large numbers of markers (> 10,000), we strongly recommend using high-performance computation.

In its current version, MAPpoly can handle the following types of datasets:

  1. CSV files
  2. MAPpoly files
  3. fitPoly files
  4. VCF files

MAPpoly also is capable of importing objects generated by the following R packages

  1. updog
  2. polyRAD
  3. polymapR

The mapping strategy is based on using pairwise recombination fraction estimation as the first source of information to position allelic variants in specific homologues sequentially. For situations where pairwise analysis has limited power, the algorithm relies on the multilocus likelihood obtained through a hidden Markov model (HMM). The derivation of the HMM used in MAPpoly can be found in Mollinari and Garcia, 2019.


From CRAN (stable version)

To install MAPpoly from the The Comprehensive R Archive Network (CRAN) use


From GitHub (development version)

You can install the development version from Git Hub. Within R, you need to install devtools:


If you are using Windows, you must install the the latest recommended version of Rtools.

To install MAPpoly from Git Hub use

devtools::install_github("mmollina/mappoly", dependencies=TRUE)

For further QTL analysis, we recommend our QTLpoly package. QTLpoly is an under development software to map quantitative trait loci (QTL) in full-sib families of outcrossing autopolyploid species based on a random-effect multiple QTL model Pereira et al. 2020.



Related software

# Enable this universe
options(repos = c(
    polyploids = 'https://polyploids.r-universe.dev',
    CRAN = 'https://cloud.r-project.org'))

# Install some packages


Articles referencing MAPpoly

  1. Using probabilistic genotypes in linkage analysis of polyploids. (Liao et al., 2021)
  2. Discovery of a major QTL for root-knot nematode Meloidogyne incognita resistance in cultivated sweetpotato Ipomoea batatas. (Oloka, et al., 2021)
  3. Quantitative trait locus mapping for common scab resistance in a tetraploid potato full-sib population. (Pereira et al., 2021)
  4. The recombination landscape and multiple QTL mapping in a Solanum tuberosum cv.’Atlantic’-derived F1 population. (Pereira et al., 2021)
  5. High-Resolution Linkage Map and QTL Analyses of Fruit Firmness in Autotetraploid Blueberry (Cappai et al., 2020)
  6. When a phenotype is not the genotype: Implications of phenotype misclassification and pedigree errors in genomics-assisted breeding of sweetpotato Ipomoea batatas (L.) Lam.(Gemenet et al., 2020)
  7. Quantitative trait loci and differential gene expression analyses reveal the genetic basis for negatively associated beta-carotene and starch content in hexaploid sweetpotato [Ipomoea batatas (L.) Lam.] (Gemenet et al., 2020)
  8. Multiple QTL Mapping in Autopolyploids: A Random-Effect Model Approach with Application in a Hexaploid Sweetpotato Full-Sib Population. (Pereira et al., 2020)
  9. Unraveling the Hexaploid Sweetpotato Inheritance Using Ultra-Dense Multilocus Mapping. (Mollinari et al., 2020).


This package has been developed as part of the Genomic Tools for Sweetpotato Improvement project (GT4SP) and SweetGAINS, both funded by Bill & Melinda Gates Foundation. Its continuous improvement is made possible by Tools for polyploids, funded by USDA NIFA Specialty Crop Research Initiative Award.

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