Genomic selection for meat quality traits in Nelore cattle

Area of Science:

• Animal Genetics
• Quantitative Genetics
• Livestock Science

Background:

• Meat quality traits are crucial for beef cattle production and consumer satisfaction.
• Genomic selection offers a powerful tool for improving complex traits like meat quality.
• Understanding heritability and prediction accuracy is essential for effective genetic improvement programs.

Purpose of the Study:

• To estimate heritability for meat quality traits in Nelore cattle.
• To evaluate the accuracy of genomic prediction using various statistical methods.
• To compare the performance of GBLUP, Improved Bayesian Lasso, and Bayes Cπ for predicting meat quality.

Main Methods:

• Phenotypic and genotypic data from approximately 5000 Nelore cattle were analyzed.
• A single-trait animal model was used for heritability estimation and phenotype adjustment.
• Genomic prediction methods including GBLUP, Improved Bayesian Lasso, and Bayes Cπ were applied to estimate SNP effects.

Main Results:

• Heritability estimates for meat quality traits ranged from 0.03 to 0.19.
• Genomic prediction accuracies varied from 0.23 to 0.73.
• Highest accuracies were observed for meat color and tenderness; lowest for fat content traits.
• Minimal differences in prediction accuracy were found among the tested genomic prediction methods.

Conclusions:

• Genomic prediction is a viable tool for improving meat quality traits in Nelore cattle.
• Meat color and tenderness exhibit higher genetic determinism and predictability compared to fat content traits.
• The evaluated genomic prediction methods demonstrated comparable performance in this context.