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Genomic prediction with parallel computing for slaughter traits in Chinese Simmental beef cattle using high-density

Peng Guo1,2, Bo Zhu1, Lingyang Xu1

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Genomic selection using parallel Bayesian models shows improved prediction accuracy for beef cattle slaughter traits. These advanced methods enhance breeding value estimation for traits like carcass weight in Chinese Simmental cattle.

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Area of Science:

  • Animal Genetics and Breeding
  • Quantitative Genetics
  • Genomic Prediction

Background:

  • Genomic selection is crucial for complex traits in livestock.
  • Accurate breeding values for slaughter traits are vital for the beef cattle industry.
  • Investigating genomic prediction accuracies for these traits is essential.

Purpose of the Study:

  • To assess the genomic predictive abilities for key slaughter traits in Chinese Simmental cattle.
  • To compare the performance of genomic BLUP (GBLUP) with parallel Bayesian models (PBayesA, PBayesB, PBayesCπ).
  • To evaluate the feasibility of advanced genomic selection methods for improving breeding programs.

Main Methods:

  • Utilized Illumina Bovine 770K SNP Beadchip data.
  • Estimated marker effects using GBLUP and three parallel Bayesian models.
  • Employed 5-fold cross-validation for predictive accuracy assessment.

Main Results:

  • Genomic prediction accuracies varied across traits and models, ranging from 0.195±0.084 (GBLUP for LMP) to 0.424±0.147 (PBayesB for CW).
  • Average accuracies across traits were GBLUP: 0.327, PBayesA: 0.335, PBayesB: 0.347, and PBayesCπ: 0.334.
  • Parallel Bayesian models consistently outperformed GBLUP for the studied slaughter traits.

Conclusions:

  • Parallel Bayesian models offer superior prediction accuracy for slaughter traits in Chinese Simmental cattle compared to GBLUP.
  • Genomic selection using these models is feasible and beneficial for improving breeding programs.
  • Direct genomic breeding value estimation via parallel Bayesian methods can enhance accuracy and aid in identifying superior young candidates.