Estimating genetic covariance functions assuming a parametric correlation structure for environmental effects

Free access article

Issue		Genet. Sel. Evol. Volume 33, Number 6, November-December 2001


Page(s)		557 - 585
DOI		10.1051/gse:2001102

DOI: 10.1051/gse:2001102

Genet. Sel. Evol. 33 (2001) 557-585

Estimating genetic covariance functions assuming a parametric correlation structure for environmental effects

Karin Meyer

Animal Genetics and Breeding Unit, University of New England, Armidale NSW 2351, Australia

(Received 3 November 2000; accepted 23 April 2001)

Abstract
A random regression model for the analysis of "repeated " records in animal breeding is described which combines a random regression approach for additive genetic and other random effects with the assumption of a parametric correlation structure for within animal covariances. Both stationary and non-stationary correlation models involving a small number of parameters are considered. Heterogeneity in within animal variances is modelled through polynomial variance functions. Estimation of parameters describing the dispersion structure of such model by restricted maximum likelihood via an "average information" algorithm is outlined. An application to mature weight records of beef cow is given, and results are contrasted to those from analyses fitting sets of random regression coefficients for permanent environmental effects.

Key words: repeated records / random regression model / correlation function / estimation / REML

Correspondence and reprints: Karin Meyer
e-mail: kmeyer@didgeridoo.une.edu.au

© INRA, EDP Sciences 2001

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