Estimating genetic covariance functions assuming a parametric correlation structure for environmental effects.
A random regression model for the analysis of "repeated" records in animal breeding is described which combines a random regression approach for additive genetic and other random effects with the assumption of a parametric correlation structure for within animal covariances. Both stationary and non-stationary correlation models involving a small number of parameters are considered. Heterogeneity in within animal variances is modelled through polynomial variance functions. Estimation of parameters describing the dispersion structure of such model by restricted maximum likelihood via an "average information" algorithm is outlined. An application to mature weight records of beef cow is given, and results are contrasted to those from analyses fitting sets of random regression coefficients for permanent environmental effects.
Meyer
K
K
Animal Genetics and Breeding Unit, University of New England, Armidale NSW 2351, Australia. kmeyer@didgeridoo.une.edu.au
D017677:Age Distribution; D000465:Algorithms; D000704:Analysis of Variance; D000818:Animals; D001499:Bayes Theorem; D001835:Body Weight; D001947:Breeding / Q000706:statistics & numerical data*; D002417:Cattle / Q000235:genetics*; D004777:Environment; D014644:Genetic Variation; D016013:Likelihood Functions; D016014:Linear Models; D008957:Models, Genetic; D010641:Phenotype; D011897:Random Allocation; D013223:Statistics as Topic; D013997:Time Factors; D014730:Veterinary Medicine