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Genetic heterogeneity of residual variance: estimation of variance components using double hierarchical generalized linear models
Dalarna University, School of Technology and Business Studies, Statistics.ORCID iD: 0000-0002-1057-5401
Dalarna University, School of Technology and Business Studies, Statistics.
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2010 (English)In: Genetics Selection Evolution, ISSN 0999-193X, E-ISSN 1297-9686, Vol. 42, 8Article in journal (Refereed) Published
Abstract [en]

Background: The sensitivity to microenvironmental changes varies among animals and may be under genetic control. It is essential to take this element into account when aiming at breeding robust farm animals. Here, linear mixed models with genetic effects in the residual variance part of the model can be used. Such models have previously been fitted using EM and MCMC algorithms.

Results: We propose the use of double hierarchical generalized linear models (DHGLM), where the squared residuals are assumed to be gamma distributed and the residual variance is fitted using a generalized linear model. The algorithm iterates between two sets of mixed model equations, one on the level of observations and one on the level of variances. The method was validated using simulations and also by re-analyzing a data set on pig litter size that was previously analyzed using a Bayesian approach. The pig litter size data contained 10,060 records from 4,149 sows. The DHGLM was implemented using the ASReml software and the algorithm converged within three minutes on a Linux server. The estimates were similar to those previously obtained using Bayesian methodology, especially the variance components in the residual variance part of the model.

Conclusions: We have shown that variance components in the residual variance part of a linear mixed model can be estimated using a DHGLM approach. The method enables analyses of animal models with large numbers of observations. An important future development of the DHGLM methodology is to include the genetic correlation between the random effects in the mean and residual variance parts of the model as a parameter of the DHGLM.

Place, publisher, year, edition, pages
2010. Vol. 42, 8
National Category
Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis, Statistisk modellering är grunden till en ökad förståelse inom genetik!
Identifiers
URN: urn:nbn:se:du-10497DOI: 10.1186/1297-9686-42-8ISI: 000277118500001PubMedID: 20302616OAI: oai:DiVA.org:du-10497DiVA: diva2:542773
Available from: 2012-08-03 Created: 2012-08-03 Last updated: 2015-12-11Bibliographically 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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