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A genetic evaluation of growth in sheep using random regression techniques

Published online by Cambridge University Press:  18 August 2016

R.M. Lewis  and
S. Brotherstone
R.M. Lewis*
Affiliation:
Animal Biology Division, Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG, UK
S. Brotherstone
Affiliation:
Institute of Cell, Animal and Population Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, UK
*
†Present addess: Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24060-0306, USA. E-mail:[email protected]
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Abstract

Repeated measures of live weight in growing animals are used to describe the path by which they travel from birth to maturity. A family of growth functions-the Gompertz is one in particular-has been used successfully to describe this journey with relatively few parameters (most importantly mature size and a rate parameter). However, using these functions to differentiate the genetic merit of individual animals to grow is problematic since the estimates of these parameters are highly correlated and are obtained with varying precision among animals. An alternative is random regression (RR) methodology. It allows environmental effects specific to the time of recording to be accounted for and can accommodate genetic differences in the shape of each animal’s growth curve. At present, though, only linear models (polynomials) can pragmatically be fitted with RR. This may be limiting since a priori beliefs about the appropriate form of a growth function, such as the non-linear Gompertz equation, cannot be accommodated. This paper describes the application of RR techniques to describe growth on a population of Suffolk sheep and compares the genetic evaluation predicted from a RR model with that obtained from a more traditional method based on a Gompertz form.

The RR model chosen as providing the best fit (P < 0·01) included additive genetic and permanent environmental (between repeat records of an individual) effects fitted to a fifth order polynomial, and dam effects fitted to a third order polynomial. Measurement error was modelled as six classes. The heritability varied at different points along the growth trajectory (from 0·09 at 15 days to 0·33 at 150 days), suggesting that live weight early in a lamb’s life is a different trait to live weight later in life. There was genetic variation in the growth curves of individual animals, which was accounted for by fitting a RR model. Breeding values obtained by RR and a Gompertz approach were moderately to highly correlated (0·81 at 56 days, 0·91 at 150 days). If breeding value for live weight at 150 days of age were the selection criterion, similar individuals would be chosen with both methodologies. The ‘better’ properties and greater flexibility of the RR approach are discussed.

Keywords

genetic factorsgrowthrandom regressionsheep
Type
Breeding and genetics
Information
Animal Science , Volume 74 , Issue 1 , February 2002 , pp. 63 - 70
Copyright
Copyright © British Society of Animal Science 2002

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References

Andersen, S. and Pedersen, B. 1996. Growth and food intake curves for group-housed gilts and castrated male pigs. Animal Science 63: 457–464.CrossRefGoogle Scholar
Cullis, B. R. and McGilchrist, B. R. 1990. A model of the analysis of growth data from designed experiments. Biometrics 46: 131–142.Google Scholar
Detorre, G. L., Candotti, J. J., Reverter, A., Bellido, M. M., Vasco, P., Garcia, L. J. and Brinks, J. S. 1992. Effects of growth curve parameters on cow efficiency. Journal of Animal Science 70: 2668–2672.Google Scholar
Emmans, G. C. 1997. A method to predict the food intake of domestic animals from birth to maturity as a function of time. Journal of Theoretical Biology 186: 189–199.CrossRefGoogle Scholar
Genstat 5 Committee. 1998. Genstat 5, release 4·1 (PC/ Windows NT). Rothamsted Experimental Station, Harpenden, UK.Google Scholar
Gilmour, A. R., Cullis, B. R., Welham, S. J. and Thompson, R. 1998. ASReml user guide. NSW Agriculture, Orange, NSW, 2800, Australia.Google Scholar
Gilmour, A. R., Thompson, R. and Cullis, B. R. 1995. Average information REML, an efficient algorithm for variance estimation in linear mixed models. Biometrics 51: 1440–1450.Google Scholar
Jamrozik, J. and Schaeffer, L. R. 1997. Estimates of genetic parameters for a test day model with random regressions for yield traits of first lactation Holsteins. Journal of Dairy Science 80: 762–770.Google Scholar
Johnson, D. L. and Thompson, R. 1995. Restricted maximum-likelihood estimation of variance components for univariate animal models using sparse-matrix techniques and average information. Journal of Dairy Science 78: 449–456.CrossRefGoogle Scholar
Kirkpatrick, M., Lofsvold, D. and Bulmer, M. 1990. Analysis of the inheritance, selection and evolution of growth trajectories. Genetics 124: 973–993.Google Scholar
Lewis, R. M., Emmans, G. C., Dingwall, W. S. and Simm, G. 2002. A description of the growth of sheep and its genetic analysis. Animal Science 74: 51–62.Google Scholar
Meyer, K. 1997. An ‘average information’ restricted maximum likelihood algorithm for estimating reduced rank genetic covariance matrices or covariance functions for animal models with equal design matrices. Genetics, Selection, Evolution 29: 97–116.Google Scholar
Meyer, K. 1998. “DxMrr” — a program to estimate covariance functions for longitudinal data by restricted maximum likelihood. Proceedings of the sixth world congress on genetics applied to livestock production, Armidale, vol. 27, pp. 465–466.Google Scholar
Meyer, K. 1999. Estimates of genetic and phenotypic covariance functions for postweaning growth and mature weight of beef cows. Journal of Animal Breeding and Genetics 116: 181–205.CrossRefGoogle Scholar
Meyer, K. 2000. Random regressions to model the phenotypic variation in monthly weights of Australian beef cows. Livestock Production Science 65: 19–38.Google Scholar
Parks, J. R. 1982. A theory of feeding and growth of animals. Springer-Verlag, Berlin.CrossRefGoogle Scholar
Portolano, B. and Todaro, M. 1997. Curves and biological efficiency of growth for 100- and 180-day-old lambs of different genetic types. Annales de Zootechnie 46: 245–253.Google Scholar
Schaeffer, L. R. and Dekkers, J. C. M. 1994. Random regressions in animal models for test-day production in dairy cattle. Proceedings of the fifth world congress on genetics applied to livestock production, Guelph, vol. 18, pp. 443–446.Google Scholar
Simm, G. and Dingwall, W. S. 1989. Selection indices for lean meat production in sheep. Livestock Production Science 21: 223–233.Google Scholar
Simm, G., Lewis, R. M., Grundy, B. and Dingwall, W. S. 2002. Responses to selection for lean growth in sheep. Animal Science 74: 39–50.Google Scholar
Simm, G. and Murphy, S. V. 1996. The effects of selection for lean growth in Suffolk sires on the saleable meat yield of their crossbred progeny. Animal Science 62: 255–263.Google Scholar
Taylor, St C. S. 1980. Genetic size-scaling rules in animal growth. Animal Production 30: 161–165.Google Scholar
Verbyla, A., Cullis, B. R., Kenwood, M. G. and Welham, S. J. 1999. The analysis of designed experiments and longitudinal data by using smoothing splines (with discussion). Journal of the Royal Statistical Society, C 48: 269–311.Google Scholar
White, I. M. S., Thompson, R. and Brotherstone, S. 1999. Genetic and environmental smoothing of lactation curves with cubic splines. Journal of Dairy Science 82: 632–638.Google Scholar
Winsor, C. P. 1932. The Gompertz curve as a growth curve. Proceedings of the National Academy of Sciences of the United States of America 18: 1–8.Google Scholar