Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-27T11:21:51.940Z Has data issue: false hasContentIssue false
  • English
  • Français

Expectation and variance of genetic gain in open and closed nucleus and progeny testing schemes

Published online by Cambridge University Press:  02 September 2010

T. H. E. Meuwissen
T. H. E. Meuwissen
Affiliation:
Research Institute for Animal Production ‘Schoonoord’, PO Box 501, 3700 Am Zeist, The Netherlands
Get access

Abstract

Open and closed nucleus and conventional and modern progeny testing schemes were compared for expectation and variance of genetic gain. Generation intervals were optimized, with minimum values of 2 and 6 years (progeny test results available) for males in nucleus and progeny testing schemes, respectively. Females had a minimum generation interval of 2 years, except in the conventional progeny testing schemes, which had a minimum of 4 years (one individual record available). Apart from the generation intervals and the progeny test, open nucleus and progeny testing schemes were identical, since ‘nucleus females’ are also born in progeny testing schemes, being full-sibs of the young bulls and dispersed over commercial herds. The number of nucleus sires (bull sires) selected was varied between four and 32. Selection was for milk production.

A deterministic model was used, that accounted for variance reduction due to selection and the effects of finite size and family structure on the selection differentials. Prediction of the variance of the selection response accounted for selection of full- and paternal half-sibs.

Closed nucleus schemes gave a factor 0·03, 0·13 and 0·19 larger response rates than open nucleus and modern and conventional progeny testing schemes, respectively. Reduction of genetic variance of open nucleus schemes was larger than that of closed nucleus schemes, which caused the slightly higher response rates of closed nucleus schemes. Standard deviations of selection responses of closed nucleus schemes were a factor 0·46, 0·79 and 0·86 larger, respectively.

Preference for the schemes was assessed using a quadratic utility function expressing risk and inbreeding aversion. The increase in genetic gain due to shortening of generation intervals more than compensated for its increased variance. Whether the increased genetic gain due to closing the nucleus compensated for its increased variance depended on the amount of risk aversion. Selection of four sires and eight to 16 sires had the highest utility in progeny testing and nucleus schemes, respectively.

Keywords

genetic gainprogeny testing
Type
Research Article
Information
Animal Production , Volume 53 , Issue 2 , October 1991 , pp. 133 - 141
Copyright
Copyright © British Society of Animal Science 1991

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

Anderson, J. R., Dillon, J. L. and Hardacker, J. B. 1977. Agricultural decision analysis. Iowa State University Press, Ames, Ia.Google Scholar
Colleau, J. J. 1989. The genetics of dairy MOETs. In New selection schemes in dairy cattle: nucleus programmes (ed. Kalm, E. and Liboriussen, T.), European Association for Animal Production Publication no. 44, pp. 55–63. Pudoc, Wageningen, The Netherlands.Google Scholar
David, F. N. and Johnson, N. L. 1954. Statistical treatment of censored data. Part I. Fundamental formulae. Biometrika 41: 228–240.Google Scholar
Henderson, C. R. 1982. Best linear unbiased prediction in populations that have undergone selection. Proceedings of the world congress on sheep and beef cattle breeding, University, NZ, Vol 1 (ed. Barton, R. A. and Smith, W. C.), pp. 191–200.Google Scholar
Hill, W. G. 1976. Order statistics of correlated variables and implications in genetic selection programmes. Biometrics 32: 889–902.CrossRefGoogle ScholarPubMed
Hill, W. G. 1977. Variation in response to selection. Proceedings of the international conference on genetics, (ed. Pollak, E., Kempthorne, O. and Bailey, T. B.), pp. 511–519. Iowa State University Press, Ames la.Google Scholar
James, J. W. 1987. Determination of optimal selection policies. Journal of Animal Breeding and Genetics 104: 23–27CrossRefGoogle Scholar
Johnson, D. L. 1977. Variance-covariance structure of group means with overlapping generations. Proceedings of the international conference on quantitative genetics, (ed. Kempthorne, E. O. and Bailey, T. B.), pp. 511–519. Iowa State University Press, Ames, la.Google Scholar
Meuwissen, T. H. E. 1989. A deterministic model for the optimization of dairy cattle breeding based on BLUP breeding value estimates. Animal Production 49: 193–202.Google Scholar
Meuwissen, T. H. E. 1991a. Reduction of selection differentials in finite populations with a nested full-half sib family structure. Biometrics. 47: 195–204.CrossRefGoogle ScholarPubMed
Meuwissen, T. H. E. 1991b. The use of increased female reproductive rates in dairy cattle breeding schemes. Animal Production 52: 21–31.Google Scholar
Nicholas, F. W. and Smith, C. 1983. Increased rates of genetic change in dairy cattle by embryo transfer and splitting. Animal Production 36: 341–353.Google Scholar
Ruane, J. 1988. Review of the use of embryo transfer in the genetic improvement of dairy cattle. Animal Breeding Abstracts 56: 437–446.Google Scholar
Ruane, J. 1990. Evaluation of genetic improvement programmes using multiple ovulation and embryo transfer in dairy cattle. Ph.D. Thesis, University of Edinburgh.Google Scholar
Ruane, J. and Thompson. R. 1989. Simulation of an adult multiple ovulation and embryo transfer (MOET) nucleus breeding scheme in dairy cattle. In New selection schemes in dairy cattle: nucleus programmes (ed. Kalm, E. and Liboriussen, T.), European Association for Animal Production publication, no. 44, 72–80. Pudoc, Wageningen, The Netherlands.Google Scholar
Wray, N. R. and Thompson, R. 1990. Prediction of rates of inbreeding in selected populations. Genetical Research 55: 41–54.CrossRefGoogle ScholarPubMed