Accuracies of genomically estimated breeding values from pure-breed and across-breed predictions in Australian beef cattle
Background: The major obstacles for the implementation of genomic selection in Australian beef cattle are the variety of breeds and in general, small numbers of genotyped and phenotyped individuals per breed. The Australian Beef Cooperative Research Center (Beef CRC) investigated these issues by deriving genomic prediction equations (PE) from a training set of animals that covers a range of breeds and crosses including Angus, Murray Grey, Shorthorn, Hereford, Brahman, Belmont Red, Santa Gertrudis and Tropical Composite. This paper presents accuracies of genomically estimated breeding values (GEBV) that were calculated from these PE in the commercial pure-breed beef cattle seed stock sector. Methods: PE derived by the Beef CRC from multi-breed and pure-breed training populations were applied to genotyped Angus, Limousin and Brahman sires and young animals, but with no pure-breed Limousin in the training population. The accuracy of the resulting GEBV was assessed by their genetic correlation to their phenotypic target trait in a bi-variate REML approach that models GEBV as trait observations. Results: Accuracies of most GEBV for Angus and Brahman were between 0.1 and 0.4, with accuracies for abattoir carcass traits generally greater than for live animal body composition traits and reproduction traits. Estimated accuracies greater than 0.5 were only observed for Brahman abattoir carcass traits and for Angus carcass rib fat. Averaged across traits within breeds, accuracies of GEBV were highest when PE from the pooled across-breed training population were used. However, for the Angus and Brahman breeds the difference in accuracy from using pure-breed PE was small. For the Limousin breed no reasonable results could be achieved for any trait. Conclusion: Although accuracies were generally low compared to published accuracies estimated within breeds, they are in line with those derived in other multi-breed populations. Thus PE developed by the Beef CRC can contribute to the implementation of genomic selection in Australian beef cattle breeding.
Accuracy of Igenity Direct Genomic Values in Australian Angus
The quality of Igenity² direct genomic values (GEBVs) derived by two different prediction procedures for 12 traits of 1032 Angus bulls was estimated as the genetic correlation to their phenotypic target traits. In addition, the effect of a decreasing genetic relationship between validation and training population was inferred by subdividing the set of 1032 GEBVs accordingly. Genetic correlations estimated were medium to high even when all training individuals were excluded from the analysis, and well in line with those already published. Thus blending Australian Angus breeding values with Igenity GEBVs can be beneficial for breeders.
Genomic Breeding values from Across Breed Prediction in Practice: Accuracy of Beef-CRC Genomic Breeding Values in Australian Angus and Australian Brahman beef cattle
Genomic selection in the Australian beef cattle sector is challenged by the variety of small breeds and a low number of phenotyped and genotyped individuals in each breed. The Beef Cooperative Research Center (Beef CRC) derived prediction equations (PE) on mixed-breed and pure-breed training sets. This paper presents the accuracy of the resulting genomically estimated breeding values (GEBV) assessed by their genetic correlation to their phenotypic target trait recorded in the seed-stock cattle populations of Australian Angus and Brahman. Accuracies of the majority of GEBVs was between 0.1 and 0.4, and were highest when the PE of the pooled across-breed training population were used. The difference in accuracies from using pure-breed PEs were small. Results were generally low compared to accuracies estimated within breeds, but in line with those derived in other across-breed populations. Thus prediction equations derived by the Beef CRC can contribute to the implementation of genomic selection in Australian beef cattle breeding.