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

Genetics Selection Evolution

Research

Herman A Mulder¹  Freddy Fikse²  Erling Strandberg²  Majbritt Felleki³  Lars Rönnegård³ 

[1] Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, PO Box 65, 8200, Lelystad, AB, The Netherlands;Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, SE-750 07, Uppsala, Sweden;Statistics Unit, Dalarna University, SE-781 70, Borlänge, Sweden;Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, SE-750 07, Uppsala, Sweden;

关键词: Variance Component;    Linear Mixed Model;    Residual Variance;    Generalize Linear Mixed Model;    Linear Predictor;   

DOI  :  10.1186/1297-9686-42-8

 received in 2009-11-06, accepted in 2010-03-19,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundThe 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.ResultsWe 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.ConclusionsWe 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.

【 授权许可】

CC BY   
© Rönnegård et al; licensee BioMed Central Ltd. 2010