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Advances in methodology for utilizing sequential records

Published online by Cambridge University Press:  27 February 2018

W. G. Hill
Affiliation:
Institute of Cell, Animal and Population Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT
S. Brotherstone
Affiliation:
Institute of Cell, Animal and Population Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT
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Abstract

There have been substantial advances in recent years in methods for genetic analysis of traits that are expressed repeatedly over time, for example milk yield on successive test days during lactation. The background to the methods, notably random regression, covariance functions and splines, are outlined. The utility of these methods for analysing functional data on which individual records on cows are few but sire family records that span the lactation, is reviewed. Methods of analyses for measures of herd life are discussed and that being adopted in the UK is outlined.

Type
Research Article
Copyright
Copyright © British Society of Animal Science 1999

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References

Ali, T. E. and Schaeffer, L. R. 1987. Accounting for covariance among test day milk yields in dairy cows. Canadian Journal of Animal Science 67:637644.CrossRefGoogle Scholar
Brotherstone, S., Veerkamp, R. F. and Hill, W. G. 1997. Genetic parameters for a simple predictor of the lifespan of Holstein-Friesian dairy cattle and its relation to production. Animal Science 65: 3137.CrossRefGoogle Scholar
Brotherstone, S., Veerkamp, R. F. and Hill, W. G. 1998. Predicting breeding values for herd life of Holstein-Friesian dairy cattle from lifespan and type. Animal Science 67: 405411.CrossRefGoogle Scholar
Ducrocq, V., Quaas, R. L., Poliak, E. J. and Casella, G. 1988a. Length of productive life of dairy cows. 1. Justification of a Weibull model. Journal of Dairy Science 71: 30613070.CrossRefGoogle Scholar
Ducrocq, V., Quaas, R. L., Poliak, E. J. and Casella, G. 1988b. Length of productive life of dairy cows. 2. Variance component estimation and sire evaluation. Journal of Dairy Science 71:30713079.CrossRefGoogle Scholar
Ducrocq, V. and Sölkner, J. 1998. Implementation of a routine breeding value evaluation for longevity of dairy cows using survival analysis techniques. Proceedings of the sixth world congress on genetics applied to livestock production, Annidale, vol. 23, pp. 359362.Google Scholar
Jamrozic, 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: 762770.CrossRefGoogle Scholar
Jamrozic, J., Schaeffer, L. R. and Dekkers, J. C. M. 1997. Genetic evaluation of dairy cattle using test day yields and random regression model. Journal of Dairy Science 80: 12171226.CrossRefGoogle Scholar
Jones, H. E., White, I. M. S. and Brotherstone, S. 1999. Genetic evaluation of Holstein Friesian sires for daughter condition score changes using a random regression model. Animal Science 68:467475.CrossRefGoogle Scholar
Kirkpatrick, M. and Heekman, N. 1989. A quantitative genetic model for growth, shape and other infinite-dimensional characters. Journal of Mathematical Biology 27: 429450.CrossRefGoogle ScholarPubMed
Kirkpatrick, M., Hill, W. G. and Thompson, R. 1994. Estimating the covariance structure of traits during growth and ageing: illustrated with lactation in dairy cattle. Genetical Research 64:5769.CrossRefGoogle ScholarPubMed
Kirkpatrick, M., Lofsvold, D. and Bulmer, M. G. 1990. Analysis of the inheritance, selection and evolution of growth trajectories. Genetics 124: 979993.CrossRefGoogle ScholarPubMed
Meyer, K. and Hill, W. G. 1997. Estimation of genetic and phenotypic covariance functions for longitudinal or ‘repeated’ records by restricted maximum likelihood. Livestock Production Science 47:185200.CrossRefGoogle Scholar
Olori, V. E., Brotherstone, S., Hill, W. G. and McGuirk, B. J. 1999. Fit of standard models of the lactation curve to weekly records of milk production of cows in a single herd. Livestock Production Science In press.Google Scholar
Rekaya, R., Rodriguez-Zas, S. L., Gianola, D. and Shook, G. E. 1998. Test-day models for longitudinal binary responses: an application to mastitis in Holsteins. Abstracts of the 49th annual meeting of the European Association for Animal Production, Warsaw, p. 44.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. 443446.Google Scholar
Veerkamp, R. F. and Thompson, R. 1998. Multi-trait covariance functions to model genetic variation in the dynamic relation between feed intake, live weight and milk yield during lactation. Abstracts of the 49th annual meeting of the European Association for Animal Production, Warsaw, p. 45.Google Scholar
White, I. M. S., Thompson, R. and Brotherstone, S. 1998. Genetic and environmental smoothing of lactation curves with cubic splines. Journal of Dairy Science 632638.Google Scholar
Wilmink, J. B. M. 1987. Adjustment of test day milk, fat and protein yield for age, season and stage of lactation. Livestock Production Science 16: 335348.CrossRefGoogle Scholar
Wood, P. D. P. 1967. Algebraic model of the lactation curve in dairy cattle. Nature 216: 164165.CrossRefGoogle Scholar
Woolliams, J. A. and Waddington, D. 1998. The effectiveness of mixed-model smoothing for prediction of lactation yields. Proceedings of the British Society of Animal Science, 1998, p. 10.Google Scholar