# Outcrossing Populations

#### Statistical Genetics Lab
Department of Genetics
Luiz de Queiroz College of Agriculture
University of São Paulo

#### 2026-01-12

Access the complete version of this vignette here

To have access to all example datasets. Install `onemap` from GitHub and run the following command:

    #install.packages("devtools") ## if you do not have it installed
    devtools::install_github("cristianetaniguti/onemap")

The following example is intended to show the usage of `OneMap` functions for linkage mapping in outcrossing (non-inbred) populations. With basic knowledge of R syntax, one should have no problems using it. If you are not familiar with R, we recommend reading the vignette Introduction to R.

Hopefully, these examples will be clear enough to help any user to understand its functionality and start using it. You do not need to be an expert in R to build your linkage map, but some concepts are necessary and will help you through the process.

The software has been available on CRAN since 2007 (OneMap on CRAN) and has undergone several updates, adding new features and optimization up to version 3.2.0 in 2024. Future updates will focus solely on maintaining accessibility and functionality. New feature development and optimization efforts are now being directed toward the MAPpoly and MAPpoly2 packages.

MAPpoly is a more robust package designed for constructing linkage maps in polyploid species. Its optimized algorithms also provide improved efficiency for diploid species compared to OneMap. Therefore, we recommend using MAPpoly instead of OneMap in the following scenarios for diploid species:

- When working with only biallelic markers (e.g., SNPs).
- For outcrossing full-sib (F1), F2, or backcross populations.
- For datasets with a large number of markers (>5,000).
- For multi-population datasets (e.g., progeny from multiple parents; see MAPpoly2).

However, OneMap remains the best choice if you have:

- Populations derived from recombinant inbred lines (RILs).
- Datasets with multiallelic or dominant markers.

For guidance on best practices in building linkage maps while accounting for genotyping errors, please refer to Taniguti et. al 2023.