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Responses of multi-trait selection in open nucleus schemes for dairy cattle breeding

Published online by Cambridge University Press:  02 September 2010

T. H. E. Meuwissen  and
J. A. Woolliams
T. H. E. Meuwissen
Affiliation:
Research Institute for Animal Production ‘Schoonoord’, PO Box 501, 3700 AM Zeist, The Netherlands
J. A. Woolliams
Affiliation:
AFRC Roslin Institute (Edinburgh)†, Roslin, Midlothian EH25 9PS
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Abstract

Responses of selection for milk production and secondary traits were predicted in open nucleus schemes using a deterministic model. Secondary traits considered were: traits recorded during lactation (e.g. mastitis resistance; calving ease); traits recorded in the nucleus only (e.g. food intake); traits recorded early in life (e.g. growth rate); and traits recorded late in life (e.g. longevity). Also, genotype × environment interactions between nucleus and commercial herds and predictors of merit in juveniles were considered.

Extension of the breeding goal to include an uncorrelated secondary trait, which was recorded at each lactation, had the same heritability as milk production (assumed throughout to be 0·25) and half its economic value, increased total economic gain by a factor of 0·12. This increase was only 0·04, if the heritability of the secondary trait was 0·1. The situation for traits of low heritability was not improved by progeny testing of young bulls due to the short optimized generation intervals. Gain increased only by a factor of 0·04, if the economic value was 0·25.

Including a secondary trait of heritability 0·25 and a genetic correlation with yield of 0·5 in the index, only increased economic response rates by a factor of 0·04. However, when the genetic correlation was –0·5 the benefits were greater with increases of 0·09, 0·10 and 0·22 for heritabilities of 0·05, 0·10 and 0·25, respectively. Hence, including traits with low heritability but with strong negative correlations with yield, which might apply to fertility and disease resistance, increased rates of gain moderately.

If an uncorrelated secondary trait was recorded in the nucleus only, e.g. food intake, and had half the economic value of milk production, total gains increased by a factor of 0·10. Hence, recording of secondary traits can be restricted to the nucleus with only minor loss of gain. The extra economic benefit was greatest from secondary traits measured early in life compared with late in life, e.g. longevity, with benefits increased by factors of 0·24 and 0·06, respectively.

Open nucleus schemes are robust in the presence of genotype × environment interactions between nucleus and commercial herds, if the breeding value estimation method accounts for these interactions, juvenile indicator traits of milk production may increase rates of gain by a factor of 0·11, if the heritability of the indicator trait is 0·25 and the correlation with milk production is 0·5.

Keywords

breeding programmesdairy cattledeterministic modelsselection
Type
Research Article
Information
Animal Production , Volume 56 , Issue 3 , June 1993 , pp. 293 - 299
Copyright
Copyright © British Society of Animal Science 1993

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References

Bovehuis, H., Niebel, E. and Fewson, D. 1989. Implications of selection for secondary traits on MOET-nucleus cattle breeding programs for dairy and dual-purpose breeds. Livestock Production Science 22: 237–254.CrossRefGoogle Scholar
Bulmer, M. G. 1971. The effect of selection on genetic variability. American Naturalist 105: 201–211.CrossRefGoogle Scholar
Colleau, J. J. 1985. [Genetic improvement by embryo transfer within selection nuclei in dairy cattle.] Génétique Sélection et Evolution 17: 499–537.CrossRefGoogle Scholar
Hazel, L. N. 1943. The genetic basis for constructing selection indexes. Genetics, USA 28: 476–490.CrossRefGoogle ScholarPubMed
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, Massey 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 programs. Biometrics 32: 889–902.CrossRefGoogle Scholar
Merks, J. W. M. 1989. Genotype × environment interactions in pig breeding programmes. VI. Genetic relations between performances in central test, on-farm test and commercial fattening. Livestock Production Science 22: 325–339.CrossRefGoogle 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: 195–203.CrossRefGoogle ScholarPubMed
Meuwissen, T. H. E. 1991b. Expectation and variance of genetic gain in open and closed nucleus and progeny testing schemes. Animal Production 53: 133–141.Google Scholar
Neimann-Sörensen, A. 1991. Views on various strategies for development in dairy cattle production. In On the eve of the third inillennium, the European challenge for animal production (ed. Rossier, E.), European Association for Animal Production publication 48, Pudoc, Wageningen, The Netherlands.Google Scholar
Nicholas, F. W. 1989. Incorporation of new reproductive technology in genetic improvement programs. In Evolution and animal breeding (ed. Hill, W. G. and Mackay, T. F. C.). Commonwealth Agricultural Bureaux International, Oxon.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
Villanueva, B., Wray, N. and Thompson, R. 1993. Prediction of asymptotic rates of response from selection on multiple traits using uni-variate and multi-variate best linear unbiased predictors. Animal Production In press.Google Scholar
Woolliams, J. A. and Smith, C. 1988. The value of indicator traits in the genetic improvement of dairy cattle. Animal Production 46: 333–345.CrossRefGoogle Scholar