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genomicSimulation: fast R functions for stochastic simulation of breeding programs.

Kira Villiers1, Eric Dinglasan1, Ben J Hayes1

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Summary

Genomic simulation software helps optimize crop and animal breeding programs by modeling genetic diversity and gains. This new tool, genomicSimulation, uses real genotypes for faster, more accurate predictions in breeding decisions.

Keywords:
C languageR packagebreeding program designbreeding program simulationgenomic selectionmeiosis simulation

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Area of Science:

  • Quantitative genetics
  • Bioinformatics
  • Computational biology

Background:

  • Designing effective breeding programs requires sophisticated simulation tools.
  • Optimizing for genetic gain and diversity is crucial for long-term success.
  • Existing tools may lack flexibility or integration capabilities.

Purpose of the Study:

  • Introduce genomicSimulation, a novel tool for stochastic simulation of breeding.
  • Enable accurate predictions based on real genotypes.
  • Facilitate integration with existing bioinformatics workflows.

Main Methods:

  • Developed genomicSimulation in C for high performance.
  • Created an R package for enhanced usability and integration.
  • Validated the tool by simulating a known breeding program.

Main Results:

  • The tool accurately simulates key population genetic features, including genomic relationships.
  • Simulated linkage disequilibrium patterns closely matched real data.
  • genomicSimulation demonstrated high execution speed and minimal dependencies.

Conclusions:

  • genomicSimulation provides a fast and flexible solution for breeding program design.
  • The tool aids in balancing genetic gain and diversity for sustainable breeding.
  • Freely available C library and R package enhance accessibility for researchers.