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
   • Dosage based
   • Probability based
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
   • Datasets
   • Maps

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).

Installation

From CRAN (stable version)

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

install.packages("mappoly")

From GitHub (development version)

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

install.packages("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.

Vignettes

• Building a genetic map using potato genotype data from SolCAP
• Building a genetic map in an hexaploid full-sib population using MAPpoly
• Dataset examples
  • Hexaploid sweetpotato VCF dataset (Beauregard x Tanzania) obtained using VCF2SM
  • Hexaploid simulation with dosage call in MAPpoly format
  • Hexaploid simulation with dosage probabilities in MAPpoly format
  • Tetraploid potato with dosage call in MAPpoly format
  • Tetraploid potato with dosage call in CSV format
  • Tetraploid potato with dosage probabilities in MAPpoly format

Related software

• Simulations

  • PedigreeSim: Simulation of genetic marker data in diploid and polyploid pedigreed populations.

• Genotype calling

  • ClusterCall: Automated tetraploid genotype calling by hierarchical clustering
  • fitPoly: Genotype Calling for Bi-Allelic Marker Assays
  • polyRAD: Genotype Calling with Uncertainty from Sequencing Data in Polyploids and Diploids
  • SuperMASSA: Graphical Bayesian inference tool for genotyping polyploids
  • updog: Flexible Genotyping for Polyploids
  • VCF2SM: Python script that integrates VCF files and SuperMASSA

• Genetic mapping in polyploids

  • MDSMap: High Density Genetic Linkage Mapping using Multidimensional Scaling

  • polymapR: Linkage Analysis in Outcrossing Polyploids

  • TetraploidSNPMap: Linkage maps and mapping QTLs for autotetraploid species, using SNP dosage data.

• Haplotype reconstruction

  • TetraOrigin:haplotype reconstruction in a full-sib tetraploid family
  • PolyOriginR:haplotype reconstruction in polyploid multiparental populations

• QTL mapping

  • QTLpoly: QTL mapping in full-sib families of outcrossing autopolyploid species based on a random-effect multiple QTL model
  • diaQTL: QTL analysis of diploid and autotetraploid diallel populations

Miscellaneous

• Supplementary scripts for Mollinari and Garcia (2019)
• Workshop: Polyploid Genetic Data Analysis: From Dosage Calling to Linkage and QTL Analysis
• Miscellaneous scripts

Acknowledgment

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.