Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/5011
Title: Estimating genetic covariance functions assuming a parametric correlation structure for environmental effects
Contributor(s): Meyer, Karin  (author)
Publication Date: 2001
DOI: 10.1051/gse:2001102
Handle Link: https://hdl.handle.net/1959.11/5011
Abstract: Arandom 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 coefcients for permanent environmental effects.
Publication Type: Journal Article
Source of Publication: Genetics Selection Evolution, 33(6), p. 557-585
Publisher: Elsevier
Place of Publication: Paris, France
ISSN: 0999-193X
1297-9686
Field of Research (FOR): 070201 Animal Breeding
060412 Quantitative Genetics (incl Disease and Trait Mapping Genetics)
Socio-Economic Outcome Codes: 970107 Expanding Knowledge in the Agricultural and Veterinary Sciences
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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Appears in Collections:Animal Genetics and Breeding Unit (AGBU)
Journal Article

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