Introduction to genetic simulation for animal breeding and genetics
Abstract
This article introduces genetic simulation for animal breeding and genetics. We will discuss some of the reasons to perform simulations in science, terms used to describe different types of genetic simulations, examples of common simulation approaches and tools that implement them, and how to get started with simulation in your research. The three simulation programs discussed are the population genetic coalescent simulator msprime, the evolutionary forward-in-time simulator SLiM and the quantitative genetic simulator AlphaSimR, with an emphasis on the latter as it is the most important for animal breeding and genetics. An example is also given about how to write simple population genetic simulations from scratch in a scripting language like R. Small code examples are provided.
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BibTeX
@misc{johnsson2026introduction,
title = {{Introduction to genetic simulation for animal breeding and genetics}},
author = {Martin Johnsson},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.12094},
eprint = {2611.12094},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.12094},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Introduction to genetic simulation for animal breeding and genetics
AU - Martin Johnsson
PY - 2026
DA - 2026/11/09
PB - arXiv
DO - 10.48550/arxiv.2611.12094
UR - https://arxiv.org/abs/2611.12094
AB - This article introduces genetic simulation for animal breeding and genetics. We will discuss some of the reasons to perform simulations in science, terms used to describe different types of genetic simulations, examples of common simulation approaches and tools that implement them, and how to get started with simulation in your research. The three simulation programs discussed are the population genetic coalescent simulator msprime, the evolutionary forward-in-time simulator SLiM and the quantitative genetic simulator AlphaSimR, with an emphasis on the latter as it is the most important for animal breeding and genetics. An example is also given about how to write simple population genetic simulations from scratch in a scripting language like R. Small code examples are provided.
ER -