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Maximizing genetic response in crossbreds using both purebred and crossbred information

Published online by Cambridge University Press:  02 September 2010

Ming Wei  and
J. H. J. van der Werf
Ming Wei
Affiliation:
Department of Animal Breeding, Wageningen Agricultural University, PO Box 338, 6700 AH Wageningen, The Netherlands
J. H. J. van der Werf
Affiliation:
Department of Animal Breeding, Wageningen Agricultural University, PO Box 338, 6700 AH Wageningen, The Netherlands
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Abstract

A combined crossbred and purebred selection (CCPS) method, i.e. using crossbred and purebred information, was proposed to achieve genetic response in crossbred animals. Selection index theory was applied to establish a CCPS index. The CCPS was compared with pure-line selection (PLS) and crossbred selection (CS) methods. The genetic correlation between purebred and crossbred performance (rpc) and crossbred heritability (hc2) are crucial factors in the comparison. The CCPS is always better than PLS or CS when a fixed number of purebred progeny is tested. With a fixed total number of purebred and crossbred tested progeny, CCPS is only worse than PLS for very high values of rpc (>0·8). Superiority of CCPS over PLS increases and over CS decreases with decreasing rpc. The larger hc2 is, relative to purebred heritability (hc2 the more response CS and CCPS will achieve. The robustness of CCPS against inappropriate assumptions on rpc and hc2 values was investigated. The expected response is always an overestimate, and the actual response is smaller than the optimal response when rpc is assumed one but the true rpc is smaller. The difference between actual and optimal response increases as rpc decreases but it is small for large rpc values (e.g. <3% for rpc >0·7). The expected response is smaller than the actual response when rpc is large and hc2> hp2 Finally, the actual response to CCPS is larger than the optimal response to PLS for positive values for rpc. The main conclusions are: (1) CCPS method is optimal for obtaining genetic response in crossbreds; and (2) CCPS with inappropriate assumptions on rpc and hc2 values (e.g. recognizing crossbreds as purebreds) achieves more genetic response than PLS for common values of rpc and crossbred heritability.

Keywords

breeding programmescrossbreedingselection indexselection methods
Type
Research Article
Information
Animal Production , Volume 59 , Issue 3 , December 1994 , pp. 401 - 413
Copyright
Copyright © British Society of Animal Science 1994

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References

Arthur, J. A. 1986. An evaluation of industry breeding programs for egg type chickens. Proceedings of the third world congress on genetics applied to livestock production, vol. 10, pp. 325–335.Google Scholar
Bell, A. E. 1982. Selection for heterosis — results with laboratory and domestic animals. Proceedings of the second world congress on genetics applied to livestock production, vol. 6, pp. 206–227.Google Scholar
Bichard, M., David, P. J. and Bovey, M. 1986. Selection between and within lines and cross-breeding strategies for worldwide production of hybrids. Proceedings of the third world congress on genetics applied to livestock production, vol. 10, pp. 130–142.Google Scholar
Biswas, D. K., Chapman, A. B., First, N. L. and Self, H. L. 1971. Intrapopulation versus reciprocal recurrent selection in swine. Journal of Animal Science 32: 840–848.CrossRefGoogle ScholarPubMed
Brascamp, E. W. 1978. Methods on economic optimization of animal breeding plans — notes for Scandinavian post-graduate course in economic breeding planning. University of Helsinki, Department of Animal Breeding, Helsinki. IVO report B-134. Zeist, the Netherlands.Google Scholar
Comstock, R. E. 1961. Reciprocal recurrent selection with reference to swine breeding. Twenty-second annual report, Regional Swine Breeding Laboratory. US Department of Agriculture, Ames, Iowa.Google Scholar
Comstock, R. E. and Robinson, H. F. 1957. Findings relative to reciprocal recurrent selection. Proceedings of international genetics symposium, Scientific Council of japan, Tokyo, pp. 461–464.Google Scholar
Comstock, R. E., Robinson, H. F. and Harvey, P. H. 1949. A breeding procedure designed to make maximum use of both general and specific combining ability. Agronomy journal 41: 360–367.CrossRefGoogle Scholar
Gama, L. T. and Smith, C. 1993. The role of inbreedng depression in livestock production systems. Livestock Production Science 36: 203–211.CrossRefGoogle Scholar
Harris, D. L. 1963. The influence of errors of parameter estimation upon index selection. In Proceedings of statistical genetics and plant breeding (ed. Hanson, W. D. and Robinson, H. F.), pp. 491–500. NAS-NRC publication 982. Washington, DC.Google Scholar
Hartmann, W. 1992. Evaluation of the potentials of new scientific developments for commercial poultry breeding. World's Poultry Science Journal 48: 17–27.CrossRefGoogle Scholar
Hazel, L. N. 1943. Genetics basis for selection indices. Genetics 28: 476–490.CrossRefGoogle Scholar
Henderson, C. R. 1963. Selection index and expected genetic advance. In Proceedings of statistical genetics and plant breeding (ed. Hanson, W. D. and Robinson, H. F.), pp. 141–163. NAS-NRC publication 982. Washington, DC.Google Scholar
Hill, W. G. 1970. Theory of limits to selection with line crossing. In Mathematical topics in population genetics (ed. Kojima, K.), pp. 210–245. Springer-Verlag, Berlin.CrossRefGoogle Scholar
Hill, W. G. 1971. Theoretical aspects of crossbreeding. Annales de Génétique el de Sélection Animale 3: 23–34.CrossRefGoogle ScholarPubMed
Hill, W. G. 1974. Predication and evaluation of response to selection with overlapping generations. Animal Production 18: 117–139.Google Scholar
Hill, W. G. 1977. Order statistics of correlated variables and implications in genetic selection programs. II. Response to selection. Biometrics 33: 703–712.CrossRefGoogle Scholar
Hull, F. H. 1945. Recurrent selection for specific combining ability in corn. journal of American Society of Agronomy 45: 989–1009.Google Scholar
Jakubec, V., Podebradsky, Z., Pichova, J. and Hyanek, J. 1974. Construction and use of selection indices for populations in broiler production. Proceedings of working symposium on breed evaluation and crossing experiment with farm animal, Zeist pp. 55–66.Google Scholar
Meuwissen, T. H. E. 1991. Reduction of selection differentials in finite populations with a nested full-half sib family structure. Biometrics 47: 195–203.CrossRefGoogle ScholarPubMed
McNew, R. W. and Bell, A. E. 1976. Comparison of crossbred and purebred selection for a heterotic trait in highly selected populations of Tribolium. journal of Heredity 67: 275–283.CrossRefGoogle ScholarPubMed
Orozco, F. 1986. Crossbreeding and heterosis. US-Spain joint seminar on sheep breeding, Zaragoza.Google Scholar
Pirchner, F. and Krosigk, C. M. von. 1973. Genetic parameters of cross- and pure-bred poultry. British Poultry Science 14: 193–202.CrossRefGoogle Scholar
Pirchner, F. and Mergl, R. 1977. Overdominance as cause for heterosis in poultry. journal of Animal Breeding and Genetics 94: 151–158.Google Scholar
Quaas, R. L. and Pollak, E. J. 1980. Mixed model methodology for farm and ranch beef cattle testing programs. journal of Animal Science 51: 1277–1287.CrossRefGoogle Scholar
Robertson, A. and Hill, W. G. 1983. Population and quantitative genetics of many linked loci in finite populations. Proceedings of the Royal Society of London B 219: 253–264.Google Scholar
Sales, J. and Hill, W. G. 1976. Effect of sampling errors on efficiency of selection indices. 1. Use of information from relatives for single trait improvement. Animal Production 22: 1–17.Google Scholar
Sellier, P. 1982. Selecting populations for use in crossbreeding. Proceedings of the second world congress on genetics applied to livestock production, vol. 6, pp. 15–49.Google Scholar
Singh, R. P. and Dempfle, A. L. 1989. Optimising multistage selection in a modified reciprocal recurrent selection programme considering cost and rate of genetic progress. Proceedings of the fortieth annual meeting of the European Association for Animal Production, Dublin, vol. 1 (summaries), p. 167.Google Scholar
Steen, H. A. M. van der and Wei, M. 1991. Reciprocal recurrent versus pure line selection. British Poultry Breeders' Round Table, Edinburgh.Google Scholar
Swan, A. A. 1992. Multibreed evaluation procedures. Ph.D. thesis, University of New England, Australia.Google Scholar
Wei, M. 1992. Combined crossbred and purebred selection in animal breeding. Ph.D. thesis, Wageningen Agricultural University, The Netherlands.Google Scholar
Wei, M. and Steen, H. A. M. van der. 1991. Comparison of pure-line selection with reciprocal recurrent selection systems in animal breeding (a review). Animal Breeding Abstracts 59: 281–298.Google Scholar
Wei, M., Steen, H. A. M. van der, Werf, J. H. J. van der and Brascamp, E. W. 1991a. Relationship between purebred and crossbred parameters. 1. Variances and covariances under the one-locus model. journal of Animal Breeding and Genetics 108: 253–261.CrossRefGoogle Scholar
Wei, M., Werf, J. H. J. van der and Brascamp, E. W. 1991b. Relationship between purebred and crossbred parameters. 2. Genetic correlation between purebred and crossbred performance under the model with two-loci. Journal of Animal Breeding and Genetics 108: 262–269.CrossRefGoogle Scholar
Wei, M. and Werf, J. H. J. van der. 1993. Genetic correlation between crossbreds and purebreds and its relationship with dominance variation in egg production traits. journal of Animal Science (suppl.), 71: p. 106 (abstr.).Google Scholar