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

Abstract

The design of breeding programs is crucial for maximizing economic gains. Simulation provides the most efficient measures to test these programs, as real-world trials are often costly and time-consuming. We developed GOplan, a comprehensive and user-friendly R package designed to develop animal breeding programs considering pure-bred populations and crossbreeding systems. Compared to other traditional simulators, it has mainstream crossbreeding frameworks, which streamline modeling, and use Gene Flow and Bayesian optimization methods to enhance breeding program efficiency. GOplan includes three key functions: runCore() to evaluate the effects of nucleus breeding programs, runWhole() to predict economic outcomes and the production performance of crossbreeding systems, and runOpt() to optimize crossbreeding structures for greater profitability. These functions support breeders in better planning and accelerating breeding goals. Additionally, the application of Bayesian Optimization algorithms in this study provides valuable insights for developing new optimization algorithms in the future. The software is available at https://github.com/CAU-TeamLiuJF/GOplan.

Keywords: Bayesian optimization; breeding program evaluation; gene flow.