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Genetic heterogeneity of residual variance - estimation of variance components using double hierarchical generalized linear models

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 ca...

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Autores principales: Rönnegård, Lars, Felleki, Majbritt, Fikse, Freddy, Mulder, Herman A, Strandberg, Erling
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2859745/
https://www.ncbi.nlm.nih.gov/pubmed/20302616
http://dx.doi.org/10.1186/1297-9686-42-8
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author Rönnegård, Lars
Felleki, Majbritt
Fikse, Freddy
Mulder, Herman A
Strandberg, Erling
author_facet Rönnegård, Lars
Felleki, Majbritt
Fikse, Freddy
Mulder, Herman A
Strandberg, Erling
author_sort Rönnegård, Lars
collection PubMed
description 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.
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spelling pubmed-28597452010-04-27 Genetic heterogeneity of residual variance - estimation of variance components using double hierarchical generalized linear models Rönnegård, Lars Felleki, Majbritt Fikse, Freddy Mulder, Herman A Strandberg, Erling Genet Sel Evol Research 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. BioMed Central 2010-03-19 /pmc/articles/PMC2859745/ /pubmed/20302616 http://dx.doi.org/10.1186/1297-9686-42-8 Text en Copyright ©2010 Rönnegård et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Rönnegård, Lars
Felleki, Majbritt
Fikse, Freddy
Mulder, Herman A
Strandberg, Erling
Genetic heterogeneity of residual variance - estimation of variance components using double hierarchical generalized linear models
title Genetic heterogeneity of residual variance - estimation of variance components using double hierarchical generalized linear models
title_full Genetic heterogeneity of residual variance - estimation of variance components using double hierarchical generalized linear models
title_fullStr Genetic heterogeneity of residual variance - estimation of variance components using double hierarchical generalized linear models
title_full_unstemmed Genetic heterogeneity of residual variance - estimation of variance components using double hierarchical generalized linear models
title_short Genetic heterogeneity of residual variance - estimation of variance components using double hierarchical generalized linear models
title_sort genetic heterogeneity of residual variance - estimation of variance components using double hierarchical generalized linear models
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2859745/
https://www.ncbi.nlm.nih.gov/pubmed/20302616
http://dx.doi.org/10.1186/1297-9686-42-8
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