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Genetic principal components for live ultrasound scan traits of Angus cattle

Published online by Cambridge University Press:  09 March 2007

K. Meyer
K. Meyer*
Affiliation:
Animal Genetics and Breeding Unit, University of New England, Armidale NSW 2351, Australia
*
†E-mail: kmeyer@didgeridoo.une.edu.au
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Abstract

Multivariate restricted maximum likelihood analyses were carried out for a large data set comprising records for eye-muscle area, fat depth at the 12/13th rib and the rump P8 site, and percentage intramuscular fat, recorded via live ultrasound scanning of Australian Angus cattle. Records on heifers or steers were treated as separate traits from those on bulls. Reduced rank estimates of the genetic covariance matrix were obtained by restricted maximum likelihood, estimating the leading three, four, five, six, seven and all eight principal components and these were contrasted with estimates from pooled bivariate analyses.

Results from analyses fitting five or six genetic principal components agreed closely with estimates from bivariate and eight-variate analyses and literature results. Heritabilities and variances for ‘fatness’ traits measured on heifers or steers were higher than those recorded for bulls, and genetic correlations were less than unity for the same trait measured in different sexes. Eye-muscle area showed little association with the other traits.

Reduced rank estimation decreased computational requirements of multivariate analyses dramatically, in essence corresponding to those of an m-variate analysis for m principal components considered. Five or six principal components appeared to be necessary to model genetic covariances adequately. The first three of these components then explained about 97% of the genetic variation among the eight traits.

A simulation study showed that errors in reduced rank estimates of the genetic covariance matrix were small, once three or more principal components from analyses fitting five or more components were used in constructing the estimates. Similarly, accuracy of genetic evaluation for the eight traits using the first four components was only slightly less than that using all principal components.

Results suggest that reduced rank estimation and prediction is applicable for the eight scan traits considered. The leading three to four principal components sufficed to describe the bulk of genetic variation between animals. However, five or more principal components needed to be considered in estimating covariance matrices and the ‘loadings’ of the original traits to the principal components.

Keywords

beef cattlegenetic parametersprincipal component analysisreduced rank estimation.
Type
Research Article
Information
Animal Science , Volume 81 , Issue 3 , December 2005 , pp. 337 - 345
Copyright
Copyright © British Society of Animal Science 2005

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References

Cattell, R. B. 1966. The scree test for the number of factors. Multivariate Behavioural Research 1: 245–276.CrossRefGoogle ScholarPubMed
Crews, D. H., Pollak, E. J., Weaber, R. L., Quaas, R. L. and Lipsey, R. J. 2003. Genetic parameters for carcass traits and their live animal indicators in Simmental cattle. Journal of Animal Science 81: 1427–1433.CrossRefGoogle ScholarPubMed
Gilmour, A. R., Thompson, R. and Cullis, B. R. 1995. Average Information REML, an efficient algorithm for variance parameter estimation in linear mixed models. Biometrics 51: 1440–1450.CrossRefGoogle Scholar
Graser, H. U., Tier, B., Johnston, D. J. and Barwick, S.A. 2005. Genetic evaluation for the beef industry in Australia. Australian Journal of Experimental Agriculture 45: 913–921.CrossRefGoogle Scholar
Harville, D. A. 1997. Matrix algebra from a statistician's perspective. Springer Verlag, Berlin.CrossRefGoogle Scholar
Hayes, J. F. and Hill, W. G. 1981. Modifications of estimates of parameters in the construction of genetic selection indices (‘bending’). Biometrics 37: 483–493.CrossRefGoogle Scholar
Henshall, J. M. and Meyer, K. 2002. “PDMATRIX” – Programs to make matrices positive definite. Proceedings of the seventh world congress on genetics applied to livestock production, Montpellier, communication no. 28–12.Google Scholar
Johnston, D. J., Reverter, A., Burrow, H. M., Oddy, H. M. and Robinson, D. L. 2003. Genetic and phenotypic characterisation of animal, carcass, and meat quality traits from temperate and tropically adapted beef breeds. 1. Animal measures. Australian Journal of Agricultural Research 54: 107–118.CrossRefGoogle Scholar
Kirkpatrick, M. and Meyer, K. 2004. Simplified analysis of complex phenotypes: direct estimation of genetic principal components. Genetics 168: 2295–2306.CrossRefGoogle ScholarPubMed
Koivula, M., Negussie, E. and Mäntysaari, E. A. 2004. Genetic parameters for test-day somatic cell count at different lactation stages of Finnish dairy cattle. Livestock Production Science 90: 145–157.CrossRefGoogle Scholar
Lynch, M. and Walsh, B. 2000. The geometry of vectors and matrices: eigenvalues and eigenvectors. In Evolution and selection of quantitative traits, volume 2. URL http: //nitro. biosci. arizona. edu/zbook/volume_2/ chapters/vol2_A1. html.Google Scholar
Mäntysaari, E. A. 1999. Derivation of multiple trait reduced random regression (RR) model for the first lactation test day records of milk, protein and fat. Proceedings of the 50th annual meeting of the European Association for Animal Production.Google Scholar
Meyer, K. and Graser, H. U. 1999. Estimates of parameters for scan records of Australian beef cattle treating records on males and females as different traits. Proceedings of the Association for the Advancement of Animal Breeding Genetics 13: 385–388.Google Scholar
Meyer, K. and Kirkpatrick, M. 2005a. Restricted maximum likelihood estimation of genetic principal components and smoothed covariance matrices. Genetics, Selection, Evolution 37: 1–30.CrossRefGoogle ScholarPubMed
Meyer, K. and Kirkpatrick, M. 2005b. Up hill, down dale: quantitative genetics of curvaceous traits. Philosophical Transactions of the Royal Society, B 360: 1443–1455.CrossRefGoogle ScholarPubMed
Meyer, K. and Smith, S. P. 1996. Restricted maximum likelihod estimation for animal models using derivatives of the likelihood. Genetics, Selection, Evolution 28: 23–49.CrossRefGoogle Scholar
Reverter, A., Johnston, D. J., Ferguson, D. M., Perry, D., Goddard, M. E., Burrow, H. M., Oddy, H. M., Thompson, J. M. and Bindon, B. M. 2003. Genetic and phenotypic characterisation of animal, carcass, and meat quality traits from temperate and tropically adapted beef breeds. 4. Correlations among animal, carcass, and meat quality traits. Australian Journal of Agricultural Research 54: 149–158.CrossRefGoogle Scholar
Reverter, A., Johnston, D. J., Graser, H. -U., Wolcott, M. L. and Upton, W. H. 2000. Genetic analyses of live-animal ultrasound and abattoir carcass traits in Australian Angus and Hereford cattle. Journal of Animal Science 78: 1786–1795.CrossRefGoogle ScholarPubMed
Thompson, R. 1976. The estimation of maternal genetic variance. Biometrics 32: 903–917.CrossRefGoogle Scholar
Wolfinger, R. D. 1993. Covariance structure selection in general mixed models. Communications in Statistics–Simulation and Computing 22: 1079–1106.CrossRefGoogle Scholar