Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-27T19:00:50.807Z Has data issue: false hasContentIssue false
  • English
  • Français

Individual animal model estimates of genetic correlations between performance test and reproduction traits of landrace pigs performance tested in a commercial nucleus herd

Published online by Cambridge University Press:  02 September 2010

R. E. Crump ,
R. Thompson ,
C. S. Haley  and
J. Mercer
R. E. Crump
Affiliation:
Roslin Institute (Edinburgh), Roslin, Midlothian EH25 9PS
R. Thompson
Affiliation:
Roslin Institute (Edinburgh), Roslin, Midlothian EH25 9PS
C. S. Haley
Affiliation:
Roslin Institute (Edinburgh), Roslin, Midlothian EH25 9PS
J. Mercer
Affiliation:
Institute of Cell, Animal and Population Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT
Get access

Abstract

Bivariate individual animal model estimates of genetic and environmental correlations between reproduction traits (number born alive and average piglet weight) and performance test traits (ultrasonic backfat depth, average daily food intake, average daily gain and food conversion ratio) of Landrace pigs were calculated. The estimates were produced using a derivative-free restricted maximum likelihood algorithm to calculate likelihoods for different combinations of covariance parameters. A quadratic approximation to the likelihood surface was used to estimate the maximum likelihood values with respect to the covariance parameters. For all combinations of performance test traits with reproduction traits the resulting genetic and residual correlation estimates were low, with a maximum absolute value of 0·233 for the genetic correlation between food conversion ratio and number born alive. Standard errors of genetic correlation estimates were between 0·11 and 0·15. There is expected to have been little effect upon reproduction traits from the rigorous selection carried out upon performance test traits over the years. When incorporating reproduction data into best linear unbiased prediction analysis procedures it should be possible to analyse performance test and reproduction traits from this population separately, thereby making savings on computer resources and time required for the analysis of all traits.

Keywords

genetic correlationperformance testingpigsreproduction
Type
Research Article
Information
Animal Science , Volume 65 , Issue 2 , October 1997 , pp. 291 - 298
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

Avalos, E. and Smith, C. 1987. Genetic improvement of litter size in pigs. Animal Production 44:153–164.Google Scholar
Bichard, M., Bovey, M., Seidel, C. M., David, P. and Tomkins, C. 1983. New developments in scientific pig breeding, No. 3. Pig Improvement Company, UK.Google Scholar
Bereskin, B. 1984. Genetic correlations of pig performance and sow productivity traits. Journal of Animal Science 59: 1477–1487.CrossRefGoogle ScholarPubMed
Cameron, N. D., Curran, M. K. and Thompson, R. 1988. Estimation of sire with feeding regime interaction in pigs. Animal Production 46: 87–95.Google Scholar
Crump, R. E., Haley, C. S., Thompson, R. and Mercer, J. 1997a. Individual animal model estimates of genetic parameters for performance test traits of male and female Landrace pigs tested in a commercial nucleus herd. Animal Science 65:275–283.CrossRefGoogle Scholar
Crump, R. E., Haley, C. S., Thompson, R. and Mercer, J. 1997b. Individual animal model estimates of genetic parameters for reproduction traits of Landrace pigs performance tested in a commercial nucleus herd. Animal Science 65:285–290.CrossRefGoogle Scholar
De Nise, R. S. K., Irvin, K. M., Swiger, L. A. and Plimpton, R. F. 1983. Selection for increased leanness of Yorkshire swine. IV. Indirect responses of the carcass, breeding efficiency and preweaning litter traits. Journal of Animal Science 56: 551–559.CrossRefGoogle Scholar
Fredeen, H. T. and Mikami, H. 1986. Mass selection in a pig population: correlated responses in reproductive performance. Journal of Animal Science 62:1523–1532.CrossRefGoogle Scholar
Haley, C. S., Avalos, E. and Smith, C. 1988. Selection for litter size in the pig. Animal Breeding Abstracts 56: 317–332.Google Scholar
Hetzer, H. O. and Miller, R. H. 1970. Influence of selection for high and low fatness on reproductive performance of swine. Journal of Animal Science 30:481–495.CrossRefGoogle Scholar
Hill, W. G. and Webb, A. J. 1982. Genetics of reproduction in the pig. In Control of pig reproduction (ed. Cole, D. J. A. and Foxcroft, G. R.), pp. 541–564. Butterworths, London.CrossRefGoogle Scholar
Johansson, K. and Kennedy, B. W. 1983. Genetic and phenotypic relationships of performance test measurements with fertility in Swedish Landrace and Yorkshire sows. Ada Agriculturae Scandinavica 33:195–199.CrossRefGoogle Scholar
Juga, J. and Thompson, R. 1990. Estimation of bivariate variance components. Proceedings of the fourth world congress on genetics applied to livestock production, Edinburgh, vol. 13, pp. 496–499.Google Scholar
Kennedy, B. W. and Quinton, M. 1987. Interrelationships between health environment and genetic and phenotypic performance of pigs for growth and backfat. Canadian Jounal of Animal Science 67: 623–629.CrossRefGoogle Scholar
Legault, C. 1971. Correlations entre les performances d'engraissement et de carcasse et les performances d'elevage chez le pore Annales de Genetique et de Selection Animale 3:153–160.Google Scholar
Lobke, A., Willeke, H. and Pirchner, F. 1986. Relationship between reproductive performance and growth and backfat. European Association for Animal Production, Budapest, Hungary. GP3.14.Google Scholar
Meyer, K. 1988. DFREML a set of programs to estimate variance components under an individual animal model. Journal of Dairy Science 71: (suppl. 2) 33–34 (abstr.).CrossRefGoogle Scholar
Meyer, K. 1989. Restricted maximum likelihood to estimate variance components for animal models with several random effects using a derivative-free algorithm. Genetics, Selection, Evolution 21: 317–340.CrossRefGoogle Scholar
Morris, C. A. 1975. Genetic relationships of reproduction with growth and carcass traits in British pigs. Animal Production 20:31–44.Google Scholar
Schaeffer, L. R., Wilton, J. W. and Thompson, R. 1978. Simultaneous estimation of variance and covariance components from multitrait mixed model equations. Biometrics 34:199–208.CrossRefGoogle Scholar
Short, T. H., Wilson, E. R. and McLaren, D. G. 1994. Relationships between growth and litter traits in pig dam lines. Proceedings of the fifth world congress on genetics applied to livestock production, Guelph, vol. 17, pp. 413–416.Google Scholar
Thompson, R., Crump, R. E., Juga, J. and Visscher, P. 1995. Estimating variances and covariances for bivariate animal models using scaling and transformation. Genetics, Selection, Evolution 27: 33–42.CrossRefGoogle Scholar
Vangen, O. 1980. Studies on a two trait selection experiment in pigs. v. Correlated responses in reproductive performance. Ada Agriculturae Scandinavica 30:309–319.CrossRefGoogle Scholar
Visscher, P. and Thompson, R. 1992. Comparisons between genetic variances estimated from different types of relatives in dairy cattle. Animal Production 55: 315–320.Google Scholar
Vogt, D. W., Comstock, R. E. and Rempel, W. E. 1963. Genetic correlations between some economically important traits in swine. Journal of Animal Science 22:214–217.CrossRefGoogle Scholar