Travis Build Status AppVeyor Build Status Development License: GPL v3 codecov CRAN_Status_Badge

MAPpoly (v. 0.2.0) 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. Recently, we used MAPpoly to build an ultra-dense multilocus integrated genetic map containing ~30k SNPs and characterized the inheritance system in a sweetpotato full-sib family (Mollinari et al., 2020).


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



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.