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Genetic analysis of weight, fat and muscle depth in growing lambs using random regression models

Published online by Cambridge University Press:  09 March 2007

T. M. Fischer*
Affiliation:
School of Rural Science and Agriculture, University of New England, Armidale, NSW 2351, Australia Australian Sheep IndustryCRC Australian Wool Innovation, 16-20 Barrack Street, Sydney, NSW 2000, Australia
J. H. J. van der Werf
Affiliation:
School of Rural Science and Agriculture, University of New England, Armidale, NSW 2351, Australia Australian Sheep IndustryCRC
R. G. Banks
Affiliation:
LAMBPLAN, MLA, c/o Animal Science, UNE, Armidale, NSW 2351, Australia
A. J. Ball
Affiliation:
LAMBPLAN, MLA, c/o Animal Science, UNE, Armidale, NSW 2351, Australia
A. R. Gilmour
Affiliation:
NSW Agriculture, Orange Agricultural Institute, Orange, NSW 2800, Australia Australian Sheep IndustryCRC
*
Australian Wool Innovation, 16-20 Barrack Street, Sydney, NSW, 2000, Australia. E-mail: troyfischer@woolinnovation.com.au
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Abstract

Genetic parameters were estimated using uni- and bi-variate random regression models for weight, eye-muscle depth and fat depth measures between 60 and 360 days of age. Each trait was measured up to five times in 50-day intervals following weaning on approximately 4000 Australian Poll Dorset Sheep. The model accounted for rearing type, dam age, management group and age of recording. The model used for analysing weight included quadratic, orthogonal polynomials for direct genetic and environmental effects, a linear polynomial for maternal genetic effects and heterogeneous error variance across ages. The fat and muscle analysis used linear orthogonal polynomials for direct genetic and environmental effects and heterogeneous error variance. Throughout the 300-day trajectory heritability for weight traits ranged from 0·20 to 0·31, while heritability for fat depth ranged from 0·24 to 0·34 and heritability for eye-muscle depth ranged from 0·24 to 0·40. Genetic correlations between repeated measures of the same trait at different ages were positive and declined as the age interval increased, to minimum values of 0·60, 0·31 and 0·50 for weight, fat and muscle respectively between 60 and 360 days of age. Genetic correlations between weight and fat and weight and eye muscle were moderate to high (0·6 to 0·8) and positive but decreased slightly with age. The genetic correlations between fat and muscle were moderate to high (0·5 to 0·7) throughout the 300-day trajectory. In all cases, the estimates produced in this study were reasonably consistent with the limited number of studies that exist in the reported literature. This study demonstrated the relationships that exist between repeated measures of weight, fat and muscle measures over time, which is of interest to prime lamb producers looking to select for specific breeding objectives or market end points requiring precise weight, fat and muscle combinations at certain ages.

Type
Research Article
Copyright
Copyright © British Society of Animal Science 2006

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References

Atkins, K. D., Murray, J. I., Gilmour, A. R. and Luff, A. L. 1991. Genetic variation in live weight and ultrasonic fat depth in Australian Poll Dorset sheep. Australian Journal of Agricultural Research 42: 629640.CrossRefGoogle Scholar
Banks, R. G. 1990. Lambplan: An integrated approach to genetic improvement for the Australian lamb industry. Proceedings of the Association for the Advancement of Animal Breeding and Genetics 8: 237240.Google Scholar
Banks, R. G. 1997. The Meat Elite Project: Establishment and achievements of an elite meat sheep nucleus. Proceedings of the Association for the Advancement of Animal Breeding and Genetics, Dubbo, Australia, 12: pp. 598601.Google Scholar
Banks, R. G. and Ross, I. S. 2003. Information flow in lamb supply chains-implications for terminal sire breeding. Proceedings of Association for the Advancement of Animal Breeding and Genetics, Melbourne, Australia, 15: pp. 334337.Google Scholar
Beatson, P. R. 1987. The inheritance of live weight-corrected fat-depth in Coopworth ram hoggets. Proceedings of Association for the Advancement of Animal Breeding and Genetics, Perth, Australia, 6: pp. 8790.Google Scholar
Berg, R. T. and Butterfield, R. M. 1976. New concepts of cattle growth. Macarthur Press, Sydney.Google Scholar
Brash, L. D., Fogarty, N. M., Gilmour, A. R. and Luff, A. F. 1992. Genetic parameters for liveweight and ultrasonic fat depth in Australian mean and dual-purpose sheep breeds. Australian Journal of Agricultural Research 43: 831841.CrossRefGoogle Scholar
Brown, D. J., Tier, B., Reverter, A., Banks, R. and Graser, H. U. 2000. OVIS: A multiple trait breeding value estimation program for genetic evaluation of sheep. Wool Technology and Sheep Breeding 48: 285297.Google Scholar
Fischer, T. M., Werf, J. H. J. van der, Banks, R. G. and Ball, A. J. 2004. Description of lamb growth using random regression on field data. Livestock Production Science 89: 175185.CrossRefGoogle Scholar
Fogarty, N. M. 1995. Genetic parameters for live weight, fat and muscle measurements, wool production and reproduction in sheep: a review. Animal Breeding Abstracts 63: 101143.Google Scholar
Gilmour, A. R., Cullis, B. R., Welham, S. J. and Thompson, R. 2002. ASREML reference manual. NSW Agriculture, Orange, Australia.Google Scholar
Gilmour, A. R., Luff, A. F., Fogarty, N. M. and Banks, R. G. 1994. Genetic parameters for ultrasound fat depth and eye muscle measurements in live Poll Dorset sheep. Australian Journal of Agricultural Research 45: 12811291.CrossRefGoogle Scholar
Gilmour, A. R., Thompson, R. and Cullis, B. R. 1995. AI, an efficient algorithm for REML estimation in linear mixed models. Biometrics 43: 277288.Google Scholar
Hassen, A., Wilson, D. E. and Rouse, G. H. 2003. Estimation of genetic parameters for ultrasound-predicted percentage of intramuscular fat in Angus cattle using random regression models. Animal Science 81: 3545.CrossRefGoogle ScholarPubMed
Huisman, A. E. 2002. Genetic analysis of growth and feed intake patterns in pigs. Ph.D. thesis, Wageningen Institute of Animal Sciences, Wageningen.Google Scholar
Huisman, A. E., Veerkamp, R. F. and Arendonk, J. A. M., van, . 2002. Genetic parameters for various random regression models to describe the weight data of pigs. Animal Science 80: 575582.CrossRefGoogle ScholarPubMed
Jopson, N. B., McEwan, J. C., Fennessey, P. F., Dodds, K. G., Nicoll, G. B. and Wade, C. M. 1997. Economic benefit of including computed tomography measurements in a large terminal sire breeding program. Proceedings of the Association for the Advancement of Animal Breeding and Genetics 12: 7275.Google Scholar
Kennedy, B. W., Werf, J. H. J., van der Meuwissen, T. H. E. 1993. Statistical and genetic properties of residual feed intake. Journal of Animal Science 71: 32393250.CrossRefGoogle ScholarPubMed
Kinghorn, B. P. 1997. Genetic improvement in sheep. In The genetics of sheep (ed. Ruvinsky, A.), pp. 565591, CAB International: Armidale, Australia.Google Scholar
Kirkpatrick, M., Lofsfold, D. and Bulmer, M. 1990. Analysis of the inheritance, selection and evolution of growth trajectories. Genetics 124: 979993.CrossRefGoogle ScholarPubMed
Lewis, R. M. and Brotherstone, S. 2002. A genetic evaluation of growth in sheep using random regression techniques. Animal Science 74: 6370.CrossRefGoogle Scholar
Lewis, R. M., Macfarlane, J. M., Simm, G. and Emmans, G. C. 2004. Effects of food quality on growth and carcass composition in lambs of two breeds and their cross. Animal Science 78: 355367.CrossRefGoogle Scholar
McEwan, J. C., Clarke, J. N., Hickey, S. M. and Knowler, K. J. 1993. Heritability of ultrasonic fat and muscle depths in Romney sheep. Proceedings of the New Zealand Society of Animal Production, Hamilton, New Zealand 53: pp. 347350.Google Scholar
McEwan, J. C., Dodds, K. G., Davis, G. H., Fennessey, P. F. and Hishon, M. 1991. Heritability of ultrasonic fat and muscle depths in sheep and their correlations with production traits. Proceedings of the Association for the Advancement of Animal Breeding and Genetics, Melbourne, Australia 9: pp. 276279.Google Scholar
Maniatis, N. and Pollott, G. E. 2002. Maternal effects on weight and ultrasonically measured traits of lambs in a small closed Suffolk flock. Small Ruminant Research 45: 235246.CrossRefGoogle Scholar
Meyer, K. 1992. Variance components due to direct and maternal effects for growth traits of Australian beef cattle. Livestock Production Science 31: 179204.CrossRefGoogle Scholar
Meyer, K. 2002. Estimates of covariance functions for growth of Australian beef cattle from a large set of field data. Proceedings of seventh world congress genetics applied to livestock production, Montpellier, France, CD-ROM communication no. 11–01.Google Scholar
Meyer, K. 2003. First estimates of covariance functions for lifetime growth of Angus cattle. Proceedings Association for Advancement of Animal Breeding and Genetics, Melbourne, Australia 15: pp. 395398.Google Scholar
Statistical Analysis Systems Institute, 1988. SAS/STAT user's guide, release 6.03 edition. SAS Institute, Cary, NC.Google Scholar
Thompson, J. M. and Butterfield, R. M. 1985. Food intake, growth and body composition in Australian Merino sheep selected for high and low weaning weight 2. Chemical and dissectible body composition. Animal Production 40: 7184.Google Scholar
Veerkamp, R. F. and Thompson, R. 1999. A covariance function for feed intake, live weight, and milk yield estimated using a random regression model. Journal of Dairy Science 82: 15651573.CrossRefGoogle ScholarPubMed
Werf, J. H. J., van der, and Wheaton, T. 1999. Estimation of genetic parameters for live weight, fat and muscle measurements in seven sheep breeds based on LAMBPLAN data. MLA Report, Sydney, Australia, 165 Walker St, North Sydney, Australia.Google Scholar