Evaluation of Linear Programming and Optimal Contribution Selection Approaches for Long-Term Selection on Beef Cattle
Xu Zheng1, Tianzhen Wang1, Qunhao Niu1
1State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
Linear programming (LP) and optimal contribution selection (OCS) strategies improve genetic gain in beef cattle. OCS effectively controls inbreeding, making it ideal for sustainable long-term genetic improvement.
Area of Science:
- Animal Breeding and Genetics
- Quantitative Genetics
- Genomic Selection
Background:
- Genomic selection significantly enhances genetic gain in livestock.
- Optimal selection methods for long-term beef cattle breeding require further investigation.
Purpose of the Study:
- To compare the effectiveness of linear programming (LP) and optimal contribution selection (OCS) strategies in beef cattle breeding.
- To assess their impact on genetic gain and inbreeding levels over 15 generations.
Main Methods:
- Simulated a beef cattle population over 15 generations.
- Implemented LP and OCS breeding strategies, with truncation selection (TS-I, TS-II) as controls.
- Monitored genetic gain, average kinship, QTL variance, and heterozygosity.
Main Results:
- LP significantly increased genetic gain, particularly for high-heritability traits, but elevated inbreeding.
- OCS yielded lower genetic gain than TS-II but effectively managed inbreeding.
- Both LP and OCS improved genetic gain, with OCS offering sustainable long-term gains.
Conclusions:
- LP and OCS are effective for enhancing genetic gain in beef cattle.
- OCS is preferable for sustainable genetic improvement due to superior inbreeding control.
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