Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-28T04:57:21.876Z Has data issue: false hasContentIssue false
  • English
  • Français

Results of multivariate individual animal model genetic evaluations of british pedigree beef cattle

Published online by Cambridge University Press:  02 September 2010

R. E. Crumps ,
G. Simm ,
D. Nicholson ,
R. H. Findlay ,
J. G. E. Bryan  and
R. Thompson
R. E. Crumps
Affiliation:
Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG
G. Simm
Affiliation:
Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG
D. Nicholson
Affiliation:
Roslin Institute (Edinburgh), Roslin, Midlothian EH25 9PS
R. H. Findlay
Affiliation:
Roslin Institute (Edinburgh), Roslin, Midlothian EH25 9PS
J. G. E. Bryan
Affiliation:
Meat and Livestock Commission, PO Box 44, Winterkill House, Snowdon Drive, Milton Keynes MK6 1AX
R. Thompson
Affiliation:
Roslin Institute (Edinburgh), Roslin, Midlothian EH25 9PS
Get access

Abstract

This paper reports the procedures put into place in the UK for the genetic evaluation of pedigree beef cattle and estimation of genetic trends using a comprehensive model to allow critical analysis of progress made under previous data recording schemes. Live weights of Simmental, Limousin, Charolais, South Devon and Aberdeen Angus beef cattle, recorded by the Meat and Livestock Commission (MLC) from 1970 to 1992 were analysed, as part of a project to introduce best linear unbiased predictions (BLUP) of breeding value in the British beef industry. Birth weights were available from MLC or the relevant breed society, (4000 to 84000 records, depending on the breed) and 200- and 400-day weights were estimated by within-animal linear regression on all available weights (resulting in 8000 to 48000 records per breed). Animals were retrospectively assigned to contemporary groups within herds, separately for each trait, taking account of observed calving patterns. Records were adjusted to correct for heterogeneity of variance between herds. BLUP evaluations were then performed within breed, fitting a multivariate individual animal model. In addition to additive direct genetic effects, additive maternal genetic and dam permanent environmental effects were included for birth weight and 200-day weight. Unknown parents were assigned to genetic groups, based on estimated date of birth. The model included fixed effects for contemporary group, sex, month of birth, birth type (single or multiple), embryo transfer births, fostered calves, breed of dam, proportion purebred and age of dam. Genetic trends were estimated by regressing estimated breeding values for animals on their year of birth. Trends in birth weight, 200-day weight and 400-day weight between 1970 and 1992 were approximately 0·09, 0·73 and 1·38 kg per annum respectively for the Charolais breed; 0·08, 0·76 and 1·33 kg per annum for the Simmental; 0·06, 0·53 and 0·89 kg per annum for the Limousin; 0·12, 1·02 and 1·86 kg per annum for the Aberdeen Angus; and 0·03, 0·38 and 0·82 kg per annum for the South Devon breed.

Keywords

beef cattlebest linear unbiased predictiongenetic trendmultivariate analysis
Type
Research Article
Information
Animal Science , Volume 65 , Issue 2 , October 1997 , pp. 199 - 207
Copyright
Copyright © British Society of Animal Science 1997

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Allen, D. M. and Steane, D. E. 1985. Beef selection indices. British Cattle Breeders' Club Digest, no. 40, pp. 63–70.Google Scholar
Andersen, B. B., Baerdemaeker, A., de Bittante, G., Bonaiti, B., Colleau, J. J., Fimland, E., Jansen, J., Lewis, W. H. E., Politiek, R. D., Seeland, G., Teehan, T. J. and Werkmeister, F. 1981. Performance testing of bulls in AI: report of a working group of the Commission of Cattle Production. Livestock Production Science 8:101–119.CrossRefGoogle Scholar
Baker, R. L., Wickham, B. W. and Morris, C. A. 1982. The accuracy of central bull performance tests in New Zealand as evaluated by subsequent progeny testing. Proceedings of the second world congress on genetics applied to livestock production, Madrid, vol. VIII, pp. 300–304.Google Scholar
Benyshek, L. L. and Bertrand, J. K. 1990. National genetic improvement programmes in the United States beef industry. South African Journal of Animal Science 20:103–109.Google Scholar
Brotherstone, S. H. and Hill, W. G. 1986. Heterogeneity of variance amongst herds for milk production. Animal Production 42: 297–303.Google Scholar
Carabano, M. J. and Alenda, R. 1990. Serving several species with animal models. Proceedings of the fourth world congress on genetics applied to livestock production, Edinburgh, vol. XIII, pp. 394–399.Google Scholar
Crump, R. E., Wray, N. R., Thompson, R. and Simm, G. 1997. Assigning pedigree beef performance records to contemporary groups taking account of within-herd calving patterns. Animal Science 65:193–198.CrossRefGoogle Scholar
De Roo, G. and Fimland, E. A. 1983. A genetic analysis of performance and progeny test data for young bulls of Norwegian Red cattle and various Friesian crosses. Livestock Production Science 10:123–131.CrossRefGoogle Scholar
Graser, H.-U. and Hammond, K. 1985. Mixed model procedures for the Australian beef industry. I. Multiple-trait model for estimation of breeding values for 200-day and final weights of cattle. Australian Journal of Agricultural Research 36:527–535.CrossRefGoogle Scholar
Graser, H.-U., Hammond, K. and McClintock, A. E. 1984. Genetic trends in Australian Simmental. Proceedings of the fourth conference of the Australian Association of Animal Breeding and Genetics, pp. 86–87.Google Scholar
Henderson, C. R. 1973. Sire evaluation and genetic trends. Proceedings of the animal breeding and genetics symposium in honor ofDrJ. L. Lush. American Society of Animal Sciences and American Dairy Sciences Association, Champaign, Illinois, pp.10–40.Google Scholar
Henderson, C. R. and Quaas, R. L. 1976. Multiple trait evaluation using relatives' records, journal of Animal Science 43:1188–1197.CrossRefGoogle Scholar
Lewis, W. H. E. 1992. Beef index validation — national results from suckler cows. British Cattle Breeders' Club digest, no. 47, pp. 24–27.Google Scholar
Mohiuddin, G. 1993. Estimates of genetic and phenotypic parameters of some performance traits in beef cattle. Animal Breeding Abstracts 61:495–522.Google Scholar
Mrode, R. A. 1988. Selection experiments in beef cattle. 2. A review of responses and correlated responses. Animal Breeding Abstracts 56:155–167.Google Scholar
Quaas, R. L. 1988. Additive genetic model with groups and relationships, journal of Dairy Science 71: (suppl. 2) 91–98.CrossRefGoogle Scholar
Rose, E. P. de and Wilton, J. W. 1988. Estimation of genetic trends for Canadian station-tested beef bulls. Canadian Journal of Animal Science 68:49–56.CrossRefGoogle Scholar
Sasaki, Y. 1992. The effectiveness of the best linear unbiased prediction of beef sires using field data collected from small farms, journal of Animal Science 70: 3317–3321.CrossRefGoogle ScholarPubMed
Schaeffer, L. R. and Wilton, J. W. 1987. RAM computing strategies and multiple traits. Proceedings of the conference on genetic evaluation of beef cattle, Kansas City, pp. 25–49.Google Scholar
Simm, G., Smith, C. and Prescott, J. H. D. 1985. Environmental effects on bull performance test results. Animal Production 41:177–185.Google Scholar
Simm, G., Steane, D. E. and Wray, N. R. 1990. Developments in beef cattle breeding programmes in Europe. Proceedings of the fourth world congress on genetics applied to livestock production, Edinburgh, vol. XV, pp. 231–243.Google Scholar
Smith, C, Steane, D. E. and Jordan, C. 1979. Progeny test results on Hereford bulls weight-recorded on the farm. Animal Production 28:49–53.Google Scholar
Southgate, J. R., Cook, G. L. and Kempster, A. J. 1982. A comparison of different breeds and crosses from the suckler herd. 1. Live-weight growth and efficiency of food utilization. Animal Production 35: 87–98.Google Scholar
Thompson, R. 1986. Estimation of realized heritability in a selected population using mixed model methods. Genetique, Selection, Evolution 18:475–484.CrossRefGoogle Scholar
Thompson, R., Wray, N. R. and Crump, R. E. 1994. Calculation of prediction error variances using sparse matrix methods. Journal of Animal Breeding and Genetics 111: 102–109.CrossRefGoogle ScholarPubMed
Westell, R. A., Quaas, R. L. and Van Vleck, L. D. 1988. Genetic groups in an animal model. Journal of Dairy Science 71:1310–1318.CrossRefGoogle Scholar
Wiggans, G. R. 1990. Breeding value prediction with the animal model. Proceedings of the fourth world congress on genetics applied to livestock production, Edinburgh, vol. XIII, pp. 355–356.Google Scholar
Wilson, D. E., Willham, R. L. and Berger, P. J. 1985. Mixed model methodology for unifying within-herd and national beef sire evaluation. Journal of Animal Science 61: 814–824.CrossRefGoogle Scholar