Beef CRC Chief Scientist Professor Mike Goddard will today announce the results of the CRC’s key beef cattle genomics research program – the delivery of genomic breeding values for key beef production and market traits across Australian beef cattle breeds.
Professor Goddard said the prediction of genomic breeding values across cattle breeds was a world first innovation, and would be integrated into the existing estimated breeding values (EBVs) in BREEDPLAN, the beef cattle industry’s genetic database.
“We’ve developed genomic across-breed predictions for all the existing traits in BREEDPLAN, except birth and weaning traits, including traits such as feed efficiency, carcase and beef quality and female and male reproductive performance,” Professor Goddard said.
He said these genomic predictions will provide the foundation for ongoing development of genomics in the Australian beef cattle industry.
“The critical thing is that these genomic predictions have been developed and tested on Australian cattle under Australian beef production systems,” Professor Goddard said.
The prediction equations have been developed to work in all breeds. However they will be most accurate for the breeds that were available in the training data (i.e. Angus, Hereford, Shorthorn in the temperate breed and Brahman and Brahman-derived composites such as the Santa Gertrudis, Belmont Red and pastoral company Tropical Composites in the tropically adapted breeds).
Professor Goddard said the accuracy of the CRC’s predictions varied across traits.
“For feed conversion efficiency the average accuracy is around 40% (0.4), for marbling and age at puberty in heifers and bulls it is around 30% but it is only around 20% (0.2) for eye muscle area,” Professor Goddard said. The accuracy also varies across the cattle breeds, with the accuracy being highest in those breeds with the largest number of trait records.
Professor Goddard, who is acknowledged as the inventor of ‘genomic selection’ and who was recently elected to the Australian Academy of Science in recognition for his distinguished work in quantitative genetics and complex genetic traits for agriculture, said the key advantage of the Beef CRC’s genomic research was for breeders to gain a genetic insight into the qualities of young animals that do not have any performance data recorded.
“You can’t at present, for example, measure days to calving on a young bull. But if you could use BREEDPLAN genomic EBVs to predict days to calving with improved accuracy, that would really improve the decision-making process for breeders and producers in selecting the top animals,” Professor Goddard said.
The Beef CRC’s genomic predictions draw upon the ‘genotype’ (derived from the animals’ DNA) and ‘phenotype’ (physical measurements on hard-to-measure traits) records of 10,000 beef cattle and used the latest 700K SNP chips that were released in late 2010.
Beef CRC CEO Dr Heather Burrow said the Beef CRC’s research represented a key milestone of the CRC’s genomic research program.
“Overall, the greatest value to the industry will be identifying animals that are genetically superior for traits that up to now have been very difficult or sometimes impossible for industry to measure,” Dr Burrow said.
“This will allow industry to select to improve those economically important traits for the very first time” she said.
The Beef CRC will now provide the genomic predictions to the Animal Genetics and Breeding Unit (AGBU), which will calibrate the predictions to allow their inclusion in BREEDPLAN, Australia’s national beef genetic evaluation scheme.
“Australian cattle breeders should be able to access the genomic information for most cattle breeds by July, when the information has been blended with the existing pedigree and breeding records in BREEDPLAN,” Dr Burrow said.
“The predictions will help commercial bull-buyers identify which bulls are most suited for their production systems with greater confidence. They will also improve the accuracy of all the BREEDPLAN traits.”
“The research will provide the foundation for integrating genomics into on-farm breeding decisions,” Professor Goddard said.
“But it only represents a single point in time and the predictions need to be updated regularly over time to be useful to industry. The more recording that is done, the more accurate the EBVs will become.”