Beef cattle breeding in Australia with genomics: opportunities and needs
D. J. Johnston A B , B. Tier A and H.-U. Graser A
+ Author Affiliations
- Author Affiliations
A Animal Genetics and Breeding Unit1, University of New England, Armidale, NSW 2351, Australia.
B Corresponding author. Email: djohnsto@une.edu.au
Animal Production Science 52(3) 100-106 https://doi.org/10.1071/AN11116
Submitted: 20 June 2011 Accepted: 9 December 2011 Published: 6 March 2012
Journal Compilation © CSIRO Publishing 2012 Open Access CC BY-NC-ND
Abstract
Opportunities exist in beef cattle breeding to significantly increase the rates of genetic gain by increasing the accuracy of selection at earlier ages. Currently, selection of young beef bulls incorporates several economically important traits but estimated breeding values for these traits have a large range in accuracies. While there is potential to increase accuracy through increased levels of performance recording, several traits cannot be recorded on the young bull. Increasing the accuracy of these traits is where genomic selection can offer substantial improvements in current rates of genetic gain for beef. The immediate challenge for beef is to increase the genetic variation explained by the genomic predictions for those traits of high economic value that have low accuracies at the time of selection. Currently, the accuracies of genomic predictions are low in beef, compared with those in dairy cattle. This is likely to be due to the relatively low number of animals with genotypes and phenotypes that have been used in developing genomic prediction equations. Improving the accuracy of genomic predictions will require the collection of genotypes and phenotypes on many more animals, with even greater numbers needed for lowly heritable traits, such as female reproduction and other fitness traits. Further challenges exist in beef to have genomic predictions for the large number of important breeds and also for multi-breed populations. Results suggest that single-nucleotide polymorphism (SNP) chips that are denser than 50 000 SNPs in the current use will be required to achieve this goal. For genomic selection to contribute to genetic progress, the information needs to be correctly combined with traditional pedigree and performance data. Several methods have emerged for combining the two sources of data into current genetic evaluation systems; however, challenges exist for the beef industry to implement these effectively. Changes will also be needed to the structure of the breeding sector to allow optimal use of genomic information for the benefit of the industry. Genomic information will need to be cost effective and a major driver of this will be increasing the accuracy of the predictions, which requires the collection of much more phenotypic data than are currently available.
References
Barwick SA, Graser H-U, Swan AA, Hermesch S (2011) Experience in breeding objectives for beef cattle, sheep and pigs, new developments and future needs. Proceedings of the Association for the Advancement of Animal Breeding and Genetics 19, 23–30.Beef CRC (2009) Australian marker panel evaluation. Available at http://www.beefcrc.com.au/Assets/572/1/DJ_Pfizer_MVP_Report-3toCRC.pdf [Verified 1 May 2011]
Chen CY, Misztal I, Aguilar I, Tsuruta S, Meuwissen THE, Aggrey SE, Wing T, Muir WM (2011) Genome-wide marker-assisted selection combining all pedigree phenotypic information with genotypic data in one step: an example using broiler chickens. Journal of Animal Science 89, 23–28.
| Genome-wide marker-assisted selection combining all pedigree phenotypic information with genotypic data in one step: an example using broiler chickens.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXotl2lsg%3D%3D&md5=c031b0d13581b2262848206e16b79808CAS |
de Roos APW, Hayes BJ, Spelman RJ, Goddard ME (2008) Linkage disequilibrium and persistence of phase in Holstein-Friesian, Jersey and Angus cattle. Genetics 179, 1503–1512.
| Linkage disequilibrium and persistence of phase in Holstein-Friesian, Jersey and Angus cattle.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD1cvmsFeltw%3D%3D&md5=a6c49348245b1efce69bcdfe46d3a613CAS |
de Roos APW, Hayes BJ, Goddard ME (2009) Reliability of genomic predictions across multiple populations. Genetics 183, 1545–1553.
| Reliability of genomic predictions across multiple populations.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD1MfhtFSqsg%3D%3D&md5=b9d5bd700e46018d2915502cf78fc7daCAS |
Garrick DJ (2010) The nature, scope and impact of some whole genome analyses in beef cattle. In ‘Proceedings of the 9th world congress on genetics applied to livestock production’. Communication 23.
Goddard ME (2009) Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136, 245–257.
| Genomic selection: prediction of accuracy and maximisation of long term response.Crossref | GoogleScholarGoogle Scholar |
Goddard ME (2012) Uses of genomics in livestock agriculture. Animal Production Science 52, 73–77.
| Uses of genomics in livestock agriculture.Crossref | GoogleScholarGoogle Scholar |
Goddard ME, Hayes BJ (2009) Mapping genes for complex traits in domestic animals and their use in breeding programs. Nature Reviews. Genetics 10, 381–391.
| Mapping genes for complex traits in domestic animals and their use in breeding programs.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXmtVKrsLw%3D&md5=5318e6beaf602f17794e33ee2b300564CAS |
Goddard ME, Hayes BJ, Meuwissen THE (2010a) Genomic selection in farm animal species – lessons learnt and future perspectives. In ‘Proceedings of the 9th world congress on genetics applied to livestock production’. Communication 701.
Goddard ME, Hayes BJ, Meuwissen THE (2010b) Genomic selection in livestock. Genetic Research Cambridge 92, 413–421.
| Genomic selection in livestock.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXjs1Clt7k%3D&md5=eeda12b1ce683152888107d3874bb5f0CAS |
Graser H-U, Tier B, Johnston DJ, Barwick SA (2005) Genetic evaluation for the beef industry in Australia. Australian Journal of Experimental Agriculture 45, 913–921.
| Genetic evaluation for the beef industry in Australia.Crossref | GoogleScholarGoogle Scholar |
Habier D, Tetens J, Seefied F-R, Lichtner P, Thaller G (2010) The impact of genetic relationship information on genomic breeding values in German Holsteins. Genetics, Selection, Evolution 42, 5–17.
| The impact of genetic relationship information on genomic breeding values in German Holsteins.Crossref | GoogleScholarGoogle Scholar |
Harris BL, Johnson DL (2010) Genomic predictions for New Zealand dairy bulls and integration with national genetic evaluation. Journal of Dairy Science 93, 1243–1252.
| Genomic predictions for New Zealand dairy bulls and integration with national genetic evaluation.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXitlOis78%3D&md5=22c05949294bfed382460384b5f73857CAS |
Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME (2009a) Invited review. Genomic selection in dairy: progress and challenges. Journal of Dairy Science 92, 433–443.
| Invited review. Genomic selection in dairy: progress and challenges.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXit1Kju7s%3D&md5=23939241311e2729579adc8fdcbcfdabCAS |
Hayes BJ, Visscher PM, Goddard ME (2009b) Increased accuracy of artificial selection by using the realised relationship matrix. Genetic Research Cambridge 91, 47–60.
| Increased accuracy of artificial selection by using the realised relationship matrix.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXit1aisbc%3D&md5=bd9e1502e7793d6b8df1216149b86bfaCAS |
Johnston DJ, Graser H-U (2010) Estimated gene frequencies and GeneSTAR markers and their size of effects on meat tenderness, marbling, and feed efficiency in temperate and tropical beef cattle breeds across a range of production environments. Journal of Animal Science 88, 1917–1935.
| Estimated gene frequencies and GeneSTAR markers and their size of effects on meat tenderness, marbling, and feed efficiency in temperate and tropical beef cattle breeds across a range of production environments.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXmvVeqtrk%3D&md5=7de295e469abf66a1b5df8ec99e3b192CAS |
Johnston DJ, Tier B, Graser H-U (2009) Integration of DNA markers into BREEDPLAN EBVs. Proceedings of the Association for the Advancement of Animal Breeding and Genetics 18, 30–33.
Johnston DJ, Jeyaruban GJ, Graser H-U (2010) Evaluation of Pfizer Animal Genetics HD 50K MVP calibration. Available at http://agbu.une.edu.au/pdf/Pfizer_50K_ September%202010.pdf [Verified 1 June 2011]
Kachman SD (2008) Incorporation of marker scores into national genetic evaluations. Available at http://www.beefimprovement.org/PDFs/Kansas%20City%20Missouri%202008.pdf [Verified 21 January 2011]
Kinghorn BP (2012) The use of genomics in the management of livestock. Animal Production Science 52, 78–91.
| The use of genomics in the management of livestock.Crossref | GoogleScholarGoogle Scholar |
Kizilkaya K, Fernando RL, Garrick DJ (2010) Genomic prediction of simulated multibreed and purebred performance using fifty thousand single nucleotide polymorphism genotypes. Journal of Animal Science 88, 544–551.
| Genomic prediction of simulated multibreed and purebred performance using fifty thousand single nucleotide polymorphism genotypes.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXktVOqt74%3D&md5=fd9a487bbe918237da46d74065069b64CAS |
Legarra A, Aguilar I, Misztal I (2009) A relationship matrix including full pedigree and genomic information. Journal of Dairy Science 92, 4656–4663.
| A relationship matrix including full pedigree and genomic information.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXhtVKqtr3E&md5=52dad86381f223dab5059bdac03e2e99CAS |
Lewis J, Abas Z, Dadousis C, Lykidis D, Paschou P, Drineas P (2011) Tracing cattle breeds with principal components analysis ancestry informative SNPs. PLoS ONE 6, e18007
| Tracing cattle breeds with principal components analysis ancestry informative SNPs.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXltVWlsLs%3D&md5=98f67eb3e0e78be0d48388f7cabbab08CAS |
MacNeil MD, Northcutt SL, Schnabel RD, Garrick DJ, Woodward BW, Taylor JF (2010) Genetic correlations between carcass traits and molecular breeding values in Angus cattle. In ‘Proceedings of the 9th world congress on genetics applied to livestock production’. Communication 482, Leipzig.
Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.
Misztal I, Legarra A, Aguilar I (2009) Computing procedures for genetic evaluation including phenotypic records, full pedigree, and genomic information. Journal of Dairy Science 92, 4648–4655.
| Computing procedures for genetic evaluation including phenotypic records, full pedigree, and genomic information.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXhtVKqtr3L&md5=5949d33b867e99a0c757ecbf6e73c3dcCAS |
Northcutt SL (2010) Pulling it all together: genomic-enhanced EPDs. Available at http://www.angus.org/AGI/GenomicEnhancedEPDs.pdf [Verified 13 January 2011]
Northcutt SL (2011) Genomic choices. Available at http://www.angus.org/AGI/GenomicChoiceApril2011.pdf [Verified 10 September 2011]
Pérez-Enciso M, Ferretti L (2010) Massive parallel sequencing in animal genetics: where froms and wheretos. Animal Genetics 41, 561–569.
| Massive parallel sequencing in animal genetics: where froms and wheretos.Crossref | GoogleScholarGoogle Scholar |
Schaeffer LR (2006) Strategy for applying genome-wide selection in dairy cattle. Journal of Animal Breeding and Genetics 123, 218–223.
| Strategy for applying genome-wide selection in dairy cattle.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD28vlvFCitA%3D%3D&md5=f2323721cc79f7ab1ccb3d6057ddefe7CAS |
Swan AA, Brown DJ, Tier B, van der Werf JH (2011) Use of genomic information to estimate breeding values for carcass traits in sheep. Proceedings of the Association for the Advancement of Animal Breeding and Genetics. 19, 331–334.
Swan AA, Johnston DJ, Brown DJ, Tier B, Graser H-U (2012) Integration of genomic information into beef cattle and sheep genetic evaluations in Australia. Animal Production Science 52, 126–132.
| Integration of genomic information into beef cattle and sheep genetic evaluations in Australia.Crossref | GoogleScholarGoogle Scholar |
The Bovine HapMap Consortium (2009) Genome-wide survey of SNP variation uncovers the genetic structure of cattle breeds. Science 324, 528–532.
| Genome-wide survey of SNP variation uncovers the genetic structure of cattle breeds.Crossref | GoogleScholarGoogle Scholar |
Van Eenennaam AL, van der Werf JH, Goddard ME (2011) The value of using DNA markers for beef bull selection in the seedstock sector. Journal of Animal Science 89, 307–320.
| The value of using DNA markers for beef bull selection in the seedstock sector.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXhvFSntb0%3D&md5=e5e80496b989f4adbda2021a961016aeCAS |
VanRaden PM, Van Tassel CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF, Schenkel FS (2009) Invited review: reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science 92, 16–24.
| Invited review: reliability of genomic predictions for North American Holstein bulls.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXlsVOrsw%3D%3D&md5=e20b7d5db6872bfe41fe1c7cba312240CAS |
Weber K, Bennett G, Keele J, Snelling W, Thallman RM, Van Eeennaam AL, Kuehn L (2011) Genomic selection in beef cattle: training and validation in multibreed populations. In ‘Proceedings plant and animal genome conference XIX’, San Diego, 2011. Poster P514.
Weigel KA, Van Tassell CP, O’Connell JR, VanRaden PM, Wiggans GR (2010) Prediction of unobserved single nucleotide polymorphisms genotypes of Jersey cattle using reference panels and population-based imputation algorithms. Journal of Dairy Science 93, 2229–2238.
| Prediction of unobserved single nucleotide polymorphisms genotypes of Jersey cattle using reference panels and population-based imputation algorithms.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXnvVans7Y%3D&md5=175dbe8742bc1ea65d367ddf18da76f9CAS |
