MoBPS - Modular Breeding Program Simulator

Area of Science:

• Animal Breeding and Genetics
• Computational Biology
• Bioinformatics

Background:

• Complex breeding programs require sophisticated simulation tools to assess genetic and economic impacts.
• Existing frameworks may lack the efficiency or flexibility needed for large-scale, modern breeding scenarios.

Purpose of the Study:

• To introduce the R-package MoBPS as a computationally efficient and flexible framework for simulating complex breeding programs.
• To enable comparison of economic and genetic impacts of various breeding strategies.

Main Methods:

• Simulations based on individual-level data.
• Utilizes bit-wise data storage and matrix multiplications via the R-package miraculix for high performance.
• Haplotypes are dynamically derived, avoiding storage of large haplotype data.

Main Results:

• MoBPS can handle large-scale populations efficiently.
• The modular structure allows integration of coarse founder population simulations with detailed breeding program modeling.
• Supports common breeding practices like optimum genetic contributions and single-step GBLUP, with user customization options.

Conclusions:

• MoBPS provides a powerful and adaptable tool for researchers and breeders.
• Facilitates advanced simulation and analysis of complex breeding programs, aiding in strategic decision-making.