The role of reference populations in genetic evaluation for Australian beef cattle.

Genomic information is playing an increasingly important role in genetic evaluation for beef cattle breeders. As genotyping becomes more common, it is the quality and quantity of phenotypes for economically important traits which determines the effectiveness of these evaluations.

In this webinar recording, join Dr Matt Wolcott (Animal Genetics Breeding Unit Scientist, University of New England) when he examines the role of genomic information in modern genetic evaluation and discusses how breeds and breeders can identify traits for which intensive recording strategies will yield the greatest benefit.

Matt also presents a case study which examines the establishment of an effective reference population in a commercial Australian Brahman stud herd, and the contributions this work made to EBV accuracy at the breed level.

You can watch the full recording or use the playlist below to jump to the start of a particular section within the presentation. 23:29; published 23 April 2021 by FutureBeefAu.

Download a copy of the presentation – Role of reference populations in beef genetic evaluation (PDF; 5.62 MB)

Additional resources:

Breedplan information (University of New England)

Repronomics project (FutureBeef)

Southern multi-breed project (NSW Department of Primary Industries)

Kaiuroo project – MDC final report (Meat & Livestock Australia)

Full recording


  1. The role of reference populations in beef genetic evaluation
  2. Outline
  3. Background – reference populations and genomic selection
  4. Genomic information in the BREEDPLAN SS analysis
  5. Number of records for reference traits
  6. What to record in the reference population
  7. Industry funded and managed reference populations (Research projects)
  8. The Repronomics Project
  9. Southern Multi-Breed Project
  10. Collecting reference population data – The Kaiuroo MDC project
  11. i. Aims – Kaiuroo MDC project
  12. ii. Snapshot – Kaiuroo MDC project
  13. iii. The gap and opportunity
  14. iv. Experimental design
  15. v. Results and outcomes for industry
  16. vi. Reference population in seedstock herd
  17. Conclusions