TAILIEUCHUNG - Báo cáo khoa hoc:" Estimating genetic covariance functions assuming a parametric correlation structure for environmental effects"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học thế giới đề tài: Estimating genetic covariance functions assuming a parametric correlation structure for environmental effects | 557 Genet Sei. Evol. 33 2001 557-585 INRA EDP Sciences 2001 Original article Estimating genetic covariance functions assuming a parametric correlation structure for environmental effects Karin Meyer Animal Genetics and Breeding Unit UniversitY of New EnGland Armidale NSW 2351 Australia Received 3 November 2000 accepted 23 April 2001 Abstract - 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. repeated records random regression model correlation function estimation REML 1. introduction Random regression RR models have become a preferred choice in the analysis of longitudinal data in animal breeding applications. Typical applications have been the analysis of test day records in dairy cattle and growth or feed intake records in pigs and beef cattle see for instance Meyer 25 for references. RR models are particularly useful when we are interested in differences between individuals as we obtain a complete description of the trajectory . growth curve over the range of ages considered. A popular model involves regression on orthogonal polynomials of time. This does not require prior assumptions about the shape of the trajectory. Such RR have proven to be well capable of modelling changes in variation due

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