L.GEN.2000 - Development of genomic multi-breed eating quality trait estimates using shared global data

Summary

This project tested whether global consumer eating quality data can be shared to accurately predict beef eating quality using genomic, multi‑breed values. Consumer scores from Australia, the US and Ireland showed the approach is feasible across diverse cattle populations.

Objectives

• Build a global dataset: Create a shared, multi‑breed eating quality reference dataset with international partners to support genomic breeding value estimation.
• Strengthen international linkages: Link Australian cattle data with overseas populations to improve genetic comparison, ranking and imports.
• Validate genomic predictions: Test the accuracy of multi‑breed genomic predictions for eating quality and assess their suitability for integration into BREEDPLAN to drive long‑term herd improvement.

Key findings

Eating quality traits showed moderate heritability across all countries, confirming a genetic basis. Importantly, combining international data substantially increased genomic prediction accuracy, demonstrating clear benefits of a shared global reference for predicting beef eating quality.

Benefits to industry

Eating quality traits are heritable and can be used as a genetic selection tool, enabling long‑term improvement across beef herds. Using shared international data greatly improves prediction accuracy, supporting the development of a reliable eating quality EBV for industry use and reducing the cost and difficulty of measuring these traits, in Australia and globally.

MLA action

Progress development of a commercial multi‑breed eating quality EBV, delivered through BREEDPLAN.

Future research

Genomic predictions for eating quality are now accurate enough to support commercial multi‑breed products, with strong potential for delivery through Beef Genetic Evaluation systems such as BREEDPLAN. Future work should focus on well‑designed international reference populations, continued global collaboration, and routine sensory data collection to maintain and improve prediction accuracy.

 Combining data across countries is essential for cost‑effective improvement of hard‑to‑measure traits, alongside better data infrastructure to support long‑term genomic evaluation.