TAILIEUCHUNG - Báo cáo sinh học: " Genetic heterogeneity of residual variance estimation of variance components using double hierarchical generalized linear models"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: Genetic heterogeneity of residual variance estimation of variance components using double hierarchical generalized linear models | Rõnnegảrd et al. Genetics Selection Evolution 2010 42 8 http content 42 1 8 GSE Ge n et i cs Selection Evolution RESEARCH Open Access Genetic heterogeneity of residual variance -estimation of variance components using double hierarchical generalized linear models 2 3 2 Lars Ronnegard Majbritt Felleki Freddy Fikse Herman A Mulder Erling Strandberg Abstract 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

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