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Báo cáo sinh học: "Computing approximate standard errors for genetic parameters derived from random regression models fitted by average information REML"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học thế giới đề tài: Computing approximate standard errors for genetic parameters derived from random regression models fitted by average information REML | Genet. Sel. Evol. 36 2004 363-369 363 INRA EDP Sciences 2004 DOI 10.1051 gse 2004006 Note Computing approximate standard errors for genetic parameters derived from random regression models fitted by average information REML Troy M. FiscHERa c Arthur R. GiLMOURb c Julius H.J. van der WERFa c a School of Rural Science and Agriculture University of New England Armidale NSW 2351 Australia b NSW Agriculture Orange Agricultural Institute Orange NSW 2800 Australia c Australian Sheep Industry CRC Received 21 October 2003 accepted 9 January 2004 Abstract - Approximate standard errors ASE of variance components for random regression coefficients are calculated from the average information matrix obtained in a residual maximum likelihood procedure. Linear combinations of those coefficients define variance components for the additive genetic variance at given points of the trajectory. Therefore ASE of these components and heritabilities derived from them can be calculated. In our example the ASE were larger near the ends of the trajectory. random regression heritability approximate standard error genetic parameter residual maximum likelihood 1. INTRODUCTION Random regression RR has been widely used for genetic analysis of longitudinal data from many of the major animal breeding industries world wide and has also been implemented into routine large scale animal breeding applications 4 . Estimates of derived genetic parameters such as heritability at given points along the trajectory are commonly published from such studies and comment is often made about the accuracy and robustness of RR models. However there have been no attempts to quantify the accuracy of such estimates for different parts of the trajectory from RR analyses using residual Corresponding author tfischer@une.edu.au 364 T.M. Fischer et al. maximum likelihood REML methods. In contrast Meyer 9 published confidence intervals of genetic parameter estimates derived from Bayesian analyses using Gibbs sampling. With .