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Single and multitrait estimates of breeding values for survival using sire and animal models

Published online by Cambridge University Press:  18 August 2016

T. H. E. Meuwissen ,
R. F. Veerkamp ,
B. Engel  and
S. Brotherstone
T. H. E. Meuwissen
Affiliation:
Institute for Animal Science and Health, ID-Lelystad, Box 65, 8200 AB Lelystad, The Netherlands
R. F. Veerkamp
Affiliation:
Institute for Animal Science and Health, ID-Lelystad, Box 65, 8200 AB Lelystad, The Netherlands
B. Engel
Affiliation:
Institute for Animal Science and Health, ID-Lelystad, Box 65, 8200 AB Lelystad, The Netherlands
S. Brotherstone
Affiliation:
Institute of Cell, Animal and Population Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, UK
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Abstract

Survival data were simulated under the Weibull model in a half-sib family design, and about 50% of the records were censored. The data were analysed using the proportional hazard model (PHM) and, after transformation to survival scores, using a linear and a binary (logit) model (LIN and BIN, respectively), where the survival scores are indicators of survival during time period t given survival up to period t – 1. Correlations between estimated and true breeding values of sires (accuracies of selection) were very similar for all three models (differences were smaller than 0·3%). Daughter effects were however less accurately predicted by the LIN model, i.e.taking proper account of the distribution of the survival data yields more accurate predictions of daughter effects. The estimated variance components and regressions of true on estimated breeding values were difficult to compare for the LIN models, because estimated breeding values were expressed as additive effects on survival scores while the simulated true breeding values were expressed on the underlying scale. Also the differences in accuracy of selection between sire and animal model breeding value estimates were small, probably due to the half-sib family design of the data. To estimate breeding values for functional survival, i.e. the component of survival that is genetically independent of production (here milk yield), two methods were compared: (i) breeding values were predicted by a single-trait linear model with a phenotypic regression on milk yield; and (ii) breeding values were predicted by a two-trait linear model for survival and milk yield, and breeding values for survival corrected for milk yield were obtained by a genetic regression on the milk yield breeding value estimates. Both methods yielded very similar accuracies of selection for functional survival, and are expected to be equivalent.

Keywords

animal modelsbreeding valuesurvival
Type
Research Article
Information
Animal Science , Volume 75 , Issue 1 , August 2002 , pp. 15 - 24
Copyright
Copyright © British Society of Animal Science 2002

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References

Ducrocq, V. 1987. An analysis of length of productive life in dairy cattle. Ph.D. thesis, Cornell University. Google Scholar
Ducrocq, V. 1999. Topics that deserve further attention in survival analysis applied to dairy cattle breeding — some suggestions. INTERBULL Bulletin 21: 181–189.Google Scholar
Ducrocq, V. and Sölkner, J. 1998a. Implementation of a routine breeding value evaluation of dairy cows using survival analysis techniques. Proceedings of the sixth world congress on genetics applied to livestock production, Armidale, vol. 23 pp. 359–362.Google Scholar
Ducrocq, V. and Sölkner, J. 1998b. The Survival Kit — a Fortran package for the analysis of survival data. Proceedings of the sixth world congress on genetics applied to livestock production, Armidale, vol. 27, pp. 447–448.Google Scholar
Engel, B. and Buist, W. 1998. Bias reduction of approximate maximum likelihood estimates for heritability in threshold models. Biometrics 54: 1155–1164.Google Scholar
Gilmour, A. R., Cullis, B. R., Welham, S. J. and Thompson, R. 2000. ASREML reference manual version 2000. New South Wales Agriculture, Orange, Australia.Google Scholar
Kennedy, B. W., Werf, J. H. J. van der and Meuwissen, T. H. E. 1993. Genetic and statistical properties of residual feed intake. Journal of Animal Science 71: 3239–3250.Google Scholar
Korsgaard, I. R., Andersen, A. H. and Jensen, J. 2000. On different models, on heritability, reliability and related quantities of survival analysis. Proceedings of the European Association for Animal Production, The Hague, vol. 51, p. 80.Google Scholar
Louis, T. 1991. Assessing, accommodating and interpreting the influences of heterogeneity. Environment and Health Perspectives 80: 215–222.Google Scholar
Madgwick, P. A. and Goddard, M. E. 1989. Genetic and phenotypic parameters of longevity in Australian dairy cattle. Journal of Dairy Science 72: 2624–2632.Google Scholar
Meuwissen, T. H. E., Engel, B., Veerkamp, R. F. and Brotherstone, S. 2000. A linear approximation to proportional hazard models for the analysis of survival data. Proceedings of the European Association for Animal Production, The Hague, vol. 51, p. 81.Google Scholar
Tempelman, R. J. and Gianola, D. 1996. A mixed model for overdispersed count data in animal breeding. Biometrics 52: 265–279.Google Scholar
Veerkamp, R. F., Brotherstone, S., Engel, B. and Meuwissen, T. H. E. 2001. Analysis of censored survival data using random regression models. Animal Science 72: 1–10.Google Scholar
Visscher, P. M., Thompson, R., Yadzi, H., Hill, W. G. and Brotherstone, S. 1999. Genetic analysis of longevity in the UK: present practice and considerations for the future. INTERBULL Bulletin 21: 16–22.Google Scholar
Yadzi, M. H., Thompson, R., Ducrocq, V. and Visscher, P. M. 2000. Genetic parameters and response to selection in proportional hazard models. Proceedings of the European Association for Animal Production, The Hague, vol. 51, p. 81.Google Scholar