![]() Beide Anwendungen beruhen auf einer vorherigen Untersuchung, in der der Verlauf der Residualvarianz durch die Verwendung von 30 Intervallen geschatzt wurde. Testtagsgemelke von Spanischen Holstein-Kuhen wurden mittels zweier zufalliger Regressionsmodelle, basierend auf Legendre Polynomen, unter zwei unterschiedlichen Voraussetzungen von Heterogenitat der Residualvarianz, untersucht, um die Variabilitat der Restvarianz der Milchleistung der Testtage durch so wenig Parameter wie moglich beschreiben zu konnen: 1) dem Verfahren des Wechsel-Identifikationspunktes mit zwei unbekannten Anderungspunkten und 2) der Verwendung von 10 frei gewahlten Intervallen der Residualvarianz. The 10 intervals modelling showed a slightly better performance probably because the change point function overestimates the Residual Variance values at the very early lactation. Both specifications for the Residual Variance were close to each other. The two model-selecting tools revealed a strong consistency between them. The Bayes factor and the cross-validation predictive densities were employed for the model assessment. This study aims to compare the change point technique identification versus the use of arbitrary intervals as two possible techniques to deal with the characterization of the Residual Variance in random regression test-day models. The change point technique has been previously implemented in the analysis of the heterogeneity of the Residual Variance in the Spanish population, yet no comparisons with other methods have been reported so far. Both implementations were based on a previous study where the trajectory of the Residual Variance was estimated using 30 intervals. Designs or datasets should have at least 100 sires each with 100 offspring.Test-day milk yields from Spanish Holstein cows were analysed with two random regression models based on Legendre polynomials under two different assumptions of heterogeneity of Residual Variance which aim to describe the variability of temporary measurement errors along days in milk with a reduced number of parameters, such as (i) the change point identification technique with two unknown change points and (ii) using 10 arbitrary intervals of Residual Variance. Conclusion The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed. ![]() Using Akaike's information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Practically, no bias was observed for estimates of any of the parameters. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Results Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. ![]() Akaike's information criterion was constructed as model selection criterion using approximated h-likelihood. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Methods We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike's information criterion using h-likelihood to select the best fitting model. temperature) and called macro-environmental or unknown and called micro-environmental. Environmental factors are either identifiable (e.g. Abstract : Background Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |