Predicting phenotypes of beef eating quality traits.

Area of Science:

• Animal genetics and breeding
• Food science and technology
• Quantitative genetics

Background:

• Accurate prediction of beef eating quality is crucial for optimizing animal management and carcass value.
• Phenotype predictions can incorporate genetic and fixed effects, but their accuracy needs validation.
• Leveraging genetic markers offers potential for early and precise quality assessments.

Purpose of the Study:

• To assess the accuracy of phenotype predictions for key beef eating quality traits.
• To evaluate the impact of different modeling approaches (SNP effects vs. fixed effects) on prediction accuracy.
• To determine the utility of these predictions for supply chain management and value extraction.

Main Methods:

• Genomic data from 1701 animals (including Bos indicus and Bos taurus breeds) were analyzed.
• BayesR model utilizing 709k single nucleotide polymorphisms (SNPs) was employed for genetic component prediction.
• Fixed effects such as days aged and carcass weight were included, alongside genomic relationship matrix principal components.

Main Results:

• Models capturing breed effects within SNP effects showed slightly higher prediction accuracies (0.43-0.50) than those fitting breed effects explicitly (0.42-0.49).
• Incorporating days aged and carcass weight did not significantly improve prediction accuracy in this study.
• Phenotype prediction accuracy for beef eating quality was deemed sufficiently high for practical application.

Conclusions:

• Genomic predictions offer a viable tool for assessing beef eating quality from DNA samples.
• These predictions can enable optimal sorting of beef products within the supply chain for market value maximization.
• The study identified novel genes potentially influencing beef eating quality traits.