GOplan: an R package for animal breeding program design via integrating Gene Flow and Bayesian optimization methods.

Area of Science:

• Animal breeding and genetics
• Computational biology
• Quantitative genetics

Background:

• Designing effective animal breeding programs is vital for economic success.
• Real-world breeding trials are expensive and time-consuming, necessitating efficient simulation tools.
• Existing simulators lack comprehensive crossbreeding frameworks and advanced optimization methods.

Purpose of the Study:

• To introduce GOplan, a novel R package for designing animal breeding programs.
• To provide a user-friendly tool for both pure-bred and crossbreeding systems.
• To enhance breeding program efficiency using Gene Flow and Bayesian optimization.

Main Methods:

• Development of the GOplan R package with three core functions: runCore(), runWhole(), and runOpt().
• Integration of mainstream crossbreeding frameworks for streamlined modeling.
• Application of Gene Flow and Bayesian optimization algorithms for enhanced efficiency.

Main Results:

• GOplan facilitates the evaluation of nucleus breeding programs and prediction of crossbreeding outcomes.
• The package optimizes crossbreeding structures for increased profitability.
• Bayesian optimization algorithms offer new avenues for developing future optimization strategies.

Conclusions:

• GOplan is a comprehensive tool that supports breeders in planning and accelerating breeding goals.
• The package enhances the efficiency of animal breeding programs through advanced simulation and optimization.
• The study highlights the potential of Bayesian optimization in animal breeding research.