Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Marker assisted selection unbiased prediction | Genet. Set. Evol. 1989 21 467-477 Elsevier INRA 467 Original article Marker assisted selection using best linear unbiased prediction . Fernando and M. Grossman University of Illinois Department of Animal Sciences 1207 West Gregory Drive Urbana IL 61801 USA received 12 May 1989 accepted 28 August 1989 Summary - Best linear unbiased prediction BLUP is applied to a mixed linear model with additive effects for alleles at a market quantitative trait locus MQTL and additive effects for alleles at the remaining quantitative trait loci QTL . A recursive algorithm is developed to obtain the covariance matrix of the effects of MQTL alleles. A simple method is presented to obtain its inverse. This approach allows simultaneous evaluation of fixed effects effects of MQTL alleles and effects of alleles at the remaining QTLs using known relationships and phenotypic and marker information. The approach is sufficiently general to accommodate individuals with partial or no marker information. Extension of the approach to BLUP with multiple markers is discussed. marker-assisted selection best linear unbasied prediction genetic marker Resume Selection assỉstée par un marqueur utilisation du meilleur prédicteur linéaire sans biais BLUP . La méthode du BLUP meilleure prediction lineaire sans biais est appliquée à un modèle linéaire mixte comprenant des effets additifs associé aux allèles d un locus quantitatif flanque d un gene marqueur et d effets additifs pour les autres locus quantitatifs. Un algorithme recursif permet d obtenir la matrice de covariances associée aux effets des alleles du locus marque. Une methode simple est aussi proposée pour calculer I inverse de cette matrice. Cette approche permet d evaluer simultanément les effets fixes les effets des alleles du locus marque et les effets génétiques additifs de I ensemble des autres locus d apres les relations de parenté les données phénotypiques et I information sur les marqueurs. Cette approche est assez generate pour .
