Strategies to improve on selection based on estimated breeding values

Area of Science:

• Animal breeding and genetics
• Quantitative genetics
• Population genetics

Background:

• Traditional selection maximizes short-term genetic gain using estimated breeding values (EBV).
• Long-term genetic gain and diversity maintenance require more complex strategies.
• This study compares selection strategies using the MoBPS simulation software.

Purpose of the Study:

• To compare existing and novel selection strategies for long-term genetic gain and diversity.
• To evaluate the impact of different genetic diversity management techniques.
• To identify optimal combinations of strategies for breeding programs.

Main Methods:

• Utilized the MoBPS simulation software for a unified testing pipeline.
• Compared truncation selection based on EBV with allele frequency weighting.
• Incorporated average kinship into the selection index.
• Employed evolutionary algorithms to optimize weights for combined strategies.

Main Results:

• Weighting SNP effects by allele frequency increased long-term gain by 1.6% and reduced inbreeding by 16.2%, with short-term losses.
• Including average kinship in the selection index yielded 4.3% greater long-term gain and 15.8% less inbreeding without short-term losses.
• A combined strategy (optimum contribution selection, allele frequency weighting, average kinship, avoiding related matings, lower selection proportion) achieved 5.1% greater gain and 37.3% lower inbreeding.

Conclusions:

• Combined strategies for genetic diversity management significantly outperform single methods.
• Implementing these strategies does not necessitate short-term genetic gain losses.
• Integrating diversity management is crucial for the long-term success of breeding companies.