Genomic prediction of weight and wool traits in a multi-breed sheep population
N. Moghaddar A B D , A. A. Swan A C and J. H. J. van der Werf A BA Cooperative Research Centre for Sheep Industry Innovation, Armidale, NSW 2351, Australia.
B Schools of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia.
C Animal Genetics & Breeding Unit (AGBU), University of New England, Armidale, NSW 2351, Australia.
D Corresponding author. Email: n.moghaddar@une.edu.au
Animal Production Science 54(5) 544-549 https://doi.org/10.1071/AN13129
Submitted: 4 April 2013 Accepted: 16 July 2013 Published: 17 September 2013
Abstract
The objective of this study was to predict the accuracy of genomic prediction for 26 traits, including weight, muscle, fat, and wool quantity and quality traits, in Australian sheep based on a large, multi-breed reference population. The reference population consisted of two research flocks, with the main breeds being Merino, Border Leicester (BL), Poll Dorset (PD), and White Suffolk (WS). The genomic estimated breeding value (GEBV) was based on GBLUP (genomic best linear unbiased prediction), applying a genomic relationship matrix calculated from the 50K Ovine SNP chip marker genotypes. The accuracy of GEBV was evaluated as the Pearson correlation coefficient between GEBV and accurate estimated breeding value based on progeny records in a set of genotyped industry animals. The accuracies of weight traits were relatively low to moderate in PD and WS breeds (0.11–0.27) and moderate to relatively high in BL and Merino (0.25–0.63). The accuracy of muscle and fat traits was moderate to relatively high across all breeds (between 0.21 and 0.55). The accuracy of GEBV of yearling and adult wool traits in Merino was, on average, high (0.33–0.75). The results showed the accuracy of genomic prediction depends on trait heritability and the effective size of the reference population, whereas the observed GEBV accuracies were more related to the breed proportions in the multi-breed reference population. No extra gain in within-breed GEBV accuracy was observed based on across breed information. More investigations are required to determine the precise effect of across-breed information on within-breed genomic prediction.
References
Amer PR, Nieuwhof GJ, Pollott GE, Roughsedge T, Conington J, Simm G (2007) Industry benefits from recent genetic progress in sheep and beef populations. Animal 1, 1414–1426.| Industry benefits from recent genetic progress in sheep and beef populations.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC38vpt1Wrtw%3D%3D&md5=5cf47887c3e01e7e51c65febd5d99b09CAS | 22444915PubMed |
Banks RG, van der Werf JHJ (2009) Economic evaluation of whole genome selection, using meat sheep as a case study. In ‘Proceedings of the 18th Conference of the Association for the Advancement of Animal Breeding and Genetics, Adelaide, Australia’. pp. 430–433. (Association for the Advancement of Animal Breeding and Genetics)
Browning SR, Browning BL (2007) Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. American Journal of Human Genetics 81, 1084–1097.
| Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXht1KmsL3M&md5=27d443efa1beae518ffdcd981232d323CAS | 17924348PubMed |
Daetwyler HD, Hickey JM, Henshall J, Dominik S, Gredler B, van der Werf JHJ, Hayes JHJ (2010) Accuracy of estimated genomic breeding values for wool and meat traits in a multi-breed sheep population. Animal Production Science 50, 1004–1010.
| Accuracy of estimated genomic breeding values for wool and meat traits in a multi-breed sheep population.Crossref | GoogleScholarGoogle Scholar |
Daetwyler HD, Kemper KE, van der Werf JHJ, Hayes BJ (2012) Components of the accuracy of genomic prediction in a multi-breed sheep population. Journal of Animal Science 90, 3375–3384.
| Components of the accuracy of genomic prediction in a multi-breed sheep population.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XhsFKrs7jF&md5=0fcdd49fad3e04a5fb2d2daa1a0ebf47CAS | 23038744PubMed |
Fernando RL (1998) Genetic evaluation and selection using genotypic, phenotypic and pedigree information. In ‘Proceedings of the 6th World Congress on Genetics Applied to Livestock Production’. Vol. 26, pp. 329–336. (CSIRO Division of Animal Production, University of New England Animal Genetics and Breeding Unit: Armidale, NSW)
Gilmour AR, Gogel BG, Cullis BR, Thompson R (2009) ‘ASReml user guide. Release 3.0.’ (VSN International Ltd: Hemel Hempstead, UK)
Goddard ME (2009) Genomic selection: prediction of accuracy and maximization of long term response. Genetica 136, 245–257.
| Genomic selection: prediction of accuracy and maximization of long term response.Crossref | GoogleScholarGoogle Scholar |
Habier D, Fernando RL, Dekkers JCM (2007) The impact of genetic relationship information on genome-assisted breeding values. Genetics 177, 2389–2397.
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=556cfb60c02e085c8bc0915ae9464bbdCAS | 20172244PubMed |
Henderson CR (1984) ‘Application of linear models in animal breeding.’ (University of Guelph: Guelph, Ontario, Canada)
Kijas JW, Lenstra JA, Hayes B, Boitard S, Porto Neto LR, San Cristobal M, Servin B, McCulloch R, Whan V, Gietzen K, Paiva S, Barendse W, Ciani E, Raadsma H, McEwan JC, Dalrymple B, International Sheep Genomics Consortium (2012) Genome-wide analysis of the world’s sheep breeds reveals high levels of historic mixture and strong recent selection. PLoS Biology 10, e1001258
| Genome-wide analysis of the world’s sheep breeds reveals high levels of historic mixture and strong recent selection.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XislamtLo%3D&md5=4854f7f19b6461aaf079b1ee9853985fCAS | 22346734PubMed |
Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.
Nejati-Javaremi A, Smith C, Gibson JP (1997) Effect of total allelic relationship on accuracy and response to selection. Journal of Animal Science 75, 1738–1745.
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=fccfdc50435b24d2fead9387dea87968CAS | 16882088PubMed |
Swan AA, Brown DJ, Banks RG (2009) Genetic progress in Australian sheep industry. In ‘Proceedings of the 18th Conference of the Association for the Advancement of Animal Breeding and Genetics, Adelaide, Australia’. pp. 326–329. (Association for the Advancement of Animal Breeding and Genetics)
van der Werf JHJ (2009) Potential benefit of genomic selection in sheep. In ‘Proceedings of the 18th Conference of the Association for the Advancement of Animal Breeding and Genetics, Adelaide, Australia’. pp. 38–41. (Association for the Advancement of Animal Breeding and Genetics)
van der Werf JHJ, Kinghorn BP, Banks RG (2010) Design and role of an information nucleus in sheep breeding programs. Animal Production Science 50, 998–1003.
| Design and role of an information nucleus in sheep breeding programs.Crossref | GoogleScholarGoogle Scholar |
VanRaden PM (2008) Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414–4423.
| Efficient methods to compute genomic predictions.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXhtlajtLzO&md5=f50142bf057b601b01c215460c24dd7cCAS | 18946147PubMed |
White JD, Allingham PG, Gorman CM, Emery DL, Hynd P, Owens J, Bell A, Siddell J, Harper G, Hayes BJ, Daetwyler HD, Usmar J, Goddard ME, Henshall JM, Dominik S, Brewer H, van der Werf JHJ, Nicholas FW, Warner R, Hofmyer C, Longhurst T, Fisher T, Swan P, Forage R, Oddy VH (2012) Design and phenotyping procedures for recording wool, skin, parasite resistance, growth, carcass yield and quality traits of the Sheep GENOMICS mapping flock. Animal Production Science 52, 157–171.
| Design and phenotyping procedures for recording wool, skin, parasite resistance, growth, carcass yield and quality traits of the Sheep GENOMICS mapping flock.Crossref | GoogleScholarGoogle Scholar |
Wientjes YC, Veerkamp RF, Calus MP (2013) The effect of linkage disequilibrium and family relationships on the reliability of genomic prediction. Genetics 193, 621–631.
| The effect of linkage disequilibrium and family relationships on the reliability of genomic prediction.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXhtVWnu7zL&md5=52f43aa96111b5a1aaf4c4c8860a645dCAS | 23267052PubMed |
Yang J, Beben B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PF, Heath AC, Martin NG, Montgomery GW, Goddard ME, Visscher PM (2010) Missing heritability of human height explained by genomic relationships. Nature Genetics 42, 565–569.
| Missing heritability of human height explained by genomic relationships.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXns1GisL8%3D&md5=5126214cdad5fa515ace5770a35cae37CAS | 20562875PubMed |
