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Estimating the covariance structure of traits during growth and ageing, illustrated with lactation in dairy cattle

Published online by Cambridge University Press:  14 April 2009

Mark Kirkpatrick*
Affiliation:
Department of Zoology, University of Texas, Austin TX 78712, USA
William G. Hill
Affiliation:
Institute of Cell, Animal and Population Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, U.K.
Robin Thompson
Affiliation:
BBSRC Roslin Institute (Edinburgh), Roslin, Midlothian EH25 9PS, U.K.
*
Corresponding author
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Summary

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Quantitative variation in traits that change with age is important to both evolutionary biologists and breeders. We present three new methods for estimating the phenotypic and additive genetic covariance functions of a trait that changes with age, and illustrate them using data on daily lactation records from British Holstein—Friesian dairy cattle. First, a new technique is developed to fit a continuous covariance function to a covariance matrix. Secondly, this technique is used to estimate and correct for a bias that inflates estimates of phenotypic variances. Thirdly, we offer a numerical method for estimating the eigenvalues and eigenfunctions of covariance functions. Although the algorithms are moderately complex, they have been implemented in a software package that is made freely available.

Analysis of lactation shows the advantages of the new methods over earlier ones. Results suggest that phenotypic variances are inflated by as much as 39 % above the underlying covariance structure by measurement error and short term environmental effects. Analysis of additive genetic variation indicates that about 90 % of the additive genetic variation for lactation during the first 10 months is associated with an eigenfunction that corresponds to increased (or decreased) production at all ages. Genetic tradeoffs between early and late milk yield are seen in the second eigenfunction, but it accounts for less than 8 % of the additive variance. This illustrates that selection is expected to increase production throughout lactation.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1994

References

Danell, B., (1990). Genetic aspects of different parts of the lactation. Proceedings of the 4th World Congress on 69 Genetic Applications to Livestock Production, Edinburgh, 14, 114117.Google Scholar
Falconer, D., (1989). Introduction to Quantitative Genetics, 3rd edition. New York: Longman.Google Scholar
Gomulkiewicz, R., & Kirkpatrick, M., (1992). Quantitative genetics and the evolution of reaction norms. Evolution 46, 390411.CrossRefGoogle ScholarPubMed
Kirkpatrick, M., & Heckman, N., (1989). A quantitative genetic model for growth, shape and other infinitedimensional characters. Journal of Mathematical Biology 27, 429450.CrossRefGoogle ScholarPubMed
Kirkpatrick, M., Lofsvold, D., & Bulmer, M., (1990). Analysis of the inheritance, selection and evolution of growth trajectories. Genetics 124, 979993.CrossRefGoogle ScholarPubMed
Kirkpatrick, M., & Lofsvold, D., (1992). Measuring selection and constraint in the evolution of growth. Evolution 46, 954971.CrossRefGoogle ScholarPubMed
Lancaster, P., & Salkauskas, K., (1986). Curve and Surface Fitting: An Introduction. London: Academic Press.Google Scholar
Meyer, K., (1985). Maximum likelihood estimation of variance components for a multivariate mixed model with equal design matrices. Biometrics 41, 153165.CrossRefGoogle ScholarPubMed
Pander, B. L., Hill, W. G., & Thompson, R., (1992). Genetic parameters of test day records of British Holstein-Friesian heifers. Animal Production 55, 1121.Google Scholar
Pander, B. L., Thompson, R., & Hill, W. G., (1993). The effect of increasing the interval between recordings on genetic parameters of test day yields of British Holstein-Friesian heifers. Animal Production 56, 159164.Google Scholar
Pander, B. L., Thompson, R., & Hill, W. G., (1993). Phenotypic correlations among daily records of milk yields. Indian Journal of Animal Sciences 63, 12821286.Google Scholar
Patterson, H. D., & Thompson, R., (1971). Recovery of interblock information when block sizes are unequal. Biometrika 58, 545554.CrossRefGoogle Scholar
Soong, T. T., (1973). Random Differential Equations in Science and Engineering. New York: Academic Press.Google Scholar
VanRaden, P. M., Wiggans, G. R., & Ernst, C. A., (1991). Expansion of projected lactation yield to stabilize genetic variance. Journal of Dairy Science 74, 43444349.CrossRefGoogle ScholarPubMed
Wolfram, S., (1991). Mathematica: A System for Doing Mathematics by Computer, Second edition. Redwood City: Addison-Wesley.Google Scholar