Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học thế giới đề tài: Effects of data structure on the estimation of covariance functions to describe genotype by environment interactions in a reaction norm model | Genet. Sel. Evol. 36 2004 489-507 INRA EDP Sciences 2004 DOI gse 2004013 489 Original article Effects of data structure on the estimation of covariance functions to describe genotype by environment interactions in a reaction norm model Mario . CALUSa Piter BlJMAb Roel F. VEERKAMPa a Animal Sciences Group Division Animal Resources Development PO Box 65 8200 AB Lelystad The Netherlands b Animal Breeding and Genetics Group Department of Animal Sciences Wageningen University PO Box 338 6700 AH Wageningen The Netherlands Received 7 October 2003 accepted 12 May 2004 Abstract - Covariance functions have been proposed to predict breeding values and genetic co variances as a function of phenotypic within herd-year averages environmental parameters to include genotype by environment interaction. The objective of this paper was to investigate the influence of definition of environmental parameters and non-random use of sires on expected breeding values and estimated genetic variances across environments. Breeding values were simulated as a linear function of simulated herd effects. The definition of environmental parameters hardly influenced the results. In situations with random use of sires estimated genetic correlations between the trait expressed in different environments were and while simulated at and estimated genetic variances deviated up to 30 from the simulated values. Non random use of sires poor genetic connectedness and small herd size had a large impact on the estimated covariance functions expected breeding values and calculated environmental parameters. Estimated genetic correlations between a trait expressed in different environments were biased upwards and breeding values were more biased when genetic connectedness became poorer and herd composition more diverse. The best possible solution at this stage is to use environmental parameters combining large numbers of animals per herd while losing some information on genotype by .
