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Estimation of breeding values for mean and dispersion, their variance and correlation using double hierarchical generalized linear models
Dalarna University, School of Technology and Business Studies, Statistics. (Komplexa system - mikrodataanalys)
Department of Statistics, Seoul National University, Seoul 151-747, Korea .
Department of Statistics, Seoul National University, Seoul 151-747, Korea .
School of Mathematics and Applied Statistics, Faculty of Informatics, University of Wollongong, Wollongong, NSW 2522, Australia.
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2012 (English)In: Genetics Research, ISSN 0016-6723, Vol. 94, no 6, 307-317 p.Article in journal (Refereed) Published
Abstract [en]

The possibility of breeding for uniform individuals by selecting animals expressing a small response to environment has been studied extensively in animal breeding. Bayesian methods for fitting models with genetic components in the residual variance have been developed for this purpose, but have limitations due to the computational demands. We use the hierarchical (h)-likelihood from the theory of double hierarchical generalized linear models (DHGLM) to derive an estimation algorithm that is computationally feasible for large datasets. Random effects for both the mean and residual variance parts of the model are estimated together with their variance/covariance components. An important feature of the algorithm is that it can fit a correlation between the random effects for mean and variance. An h-likelihood estimator is implemented in the R software and an iterative reweighted least square (IRWLS) approximation of the h-likelihood is implemented using ASReml. The difference in variance component estimates between the two implementations is investigated, as well as the potential bias of the methods, using simulations. IRWLS gives the same results as h-likelihood in simple cases with no severe indication of bias. For more complex cases, only IRWLS could be used, and bias did appear. The IRWLS is applied on the pig litter size data previously analysed by Sorensen & Waagepetersen (2003) using Bayesian methodology. The estimates we obtained by using IRWLS are similar to theirs, with the estimated correlation between the random genetic effects being −0·52 for IRWLS and −0·62 in Sorensen & Waagepetersen (2003).

Place, publisher, year, edition, pages
Cambridge University Press, 2012. Vol. 94, no 6, 307-317 p.
Keyword [en]
genetic heterogeneity, environmental variation, hierarchical likelihood, DHGLM
National Category
Animal and Dairy Science Probability Theory and Statistics
Research subject
Komplexa system - mikrodataanalys
Identifiers
URN: urn:nbn:se:du-11811DOI: 10.1017/S0016672312000766ISI: 000314425400002OAI: oai:DiVA.org:du-11811DiVA: diva2:603904
Available from: 2013-02-07 Created: 2013-02-07 Last updated: 2015-06-22Bibliographically approved
In thesis
1. Genetic Heteroscedasticity for Domestic Animal Traits
Open this publication in new window or tab >>Genetic Heteroscedasticity for Domestic Animal Traits
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Animal traits differ not only in mean, but also in variation around the mean. For instance, one sire’s daughter group may be very homogeneous, while another sire’s daughters are much more heterogeneous in performance. The difference in residual variance can partially be explained by genetic differences. Models for such genetic heterogeneity of environmental variance include genetic effects for the mean and residual variance, and a correlation between the genetic effects for the mean and residual variance to measure how the residual variance might vary with the mean.

The aim of this thesis was to develop a method based on double hierarchical generalized linear models for estimating genetic heteroscedasticity, and to apply it on four traits in two domestic animal species; teat count and litter size in pigs, and milk production and somatic cell count in dairy cows.

The method developed is fast and has been implemented in software that is widely used in animal breeding, which makes it convenient to use. It is based on an approximation of double hierarchical generalized linear models by normal distributions. When having repeated observations on individuals or genetic groups, the estimates were found to be unbiased.

For the traits studied, the estimated heritability values for the mean and the residual variance, and the genetic coefficients of variation, were found in the usual ranges reported. The genetic correlation between mean and residual variance was estimated for the pig traits only, and was found to be favorable for litter size, but unfavorable for teat count.

Place, publisher, year, edition, pages
Uppsala: Sveriges Lantbruksuniversitet, 2014. 54 p.
Series
Acta Universitatis agriculturae Sueciae, ISSN 1652-6880 ; 2014:43
Keyword
Quantitative genetics, genetic heteroscedasticity of residuals, genetic heterogeneity of environmental variation, genetic heterogeneity of residual variance, double hierarchical generalized linear models, teat count in pigs, litter size in pigs, milk yield in cows, somatic cell count in cows
National Category
Animal and Dairy Science Probability Theory and Statistics
Research subject
Komplexa system - mikrodataanalys
Identifiers
urn:nbn:se:du-14310 (URN)978-91-576-8035-8 (ISBN)978-91-576-8034-1 (ISBN)
Public defence
2014-06-11, Room L, Undervisningsplan 8, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2014-06-16 Created: 2014-06-16 Last updated: 2015-06-08Bibliographically approved

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Citation style
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