SimpleMating: R-package for prediction and optimization of breeding crosses using genomic selection

Area of Science:

• Plant breeding
• Computational biology
• Quantitative genetics

Background:

• Parent and cross selection are crucial for successful breeding programs.
• Designing crosses that maximize genetic gain while preserving population diversity is essential for long-term breeding applications.

Purpose of the Study:

• To introduce SimpleMating, a novel, flexible, and open-source R package for computational mate allocation in breeding programs.
• To provide a tool for predicting and optimizing breeding crosses across diverse reproductive systems and designs.

Main Methods:

• SimpleMating estimates cross performance using criteria like mid-parental value and total genetic value.
• An optimization algorithm maximizes target criteria while minimizing next-generation inbreeding.
• Users can specify cross numbers, parent limits, and maximum relatedness for flexible mating plan generation.

Main Results:

• SimpleMating generates mating plans based on single or multi-trait criteria.
• Testing in a simulated maize breeding program demonstrated substantial genetic gain (up to 22% more than conventional genomic selection).
• The tool also showed a reduced loss of genetic diversity over time compared to conventional methods.

Conclusions:

• SimpleMating is a valuable tool for data-driven decision-making in breeding programs.
• The package effectively enhances genetic gain and maintains genetic diversity in plant breeding applications.