TAILIEUCHUNG - Báo cáo sinh học: " Prediction of selection response for Poisson distributed traits"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Prediction of selection response for Poisson distributed traits | 297 Genet Sei Evol 1993 25 297-303 Elsevier INRA Note Prediction of selection response for Poisson distributed traits JL Foulley INRA Station de Génétique Quantitative et Appliquée Centre de Recherches de Jouy-en-Josas 78352 Jouy-en-Josas Cedex France Received 18 November 1992 accepted 19 February 1993 Summary - This paper presents a formula to predict expected response to one generation of truncation selection for a Poisson distributed trait under polygenic additive inheritance. The derivation relies of the Poisson-lognormal model and its analysis via quasi-likelihood. The formula derived accounts for asymmetry of response. The relationship with the classical formula R ipơQ is explained. Properties are illustrated with an example of sire selection based on progeny test performance. discrete variable Poisson distribution prediction selection response asymmetry of response Resume Prediction de la réponse à la selection pour des caractères distribués suivant une loi de Poisson. Get article présente une formule de prediction de la réponse à une generation de selection par troncature pour un caractère distribué suivant une loi de Poisson sous 1 hypothèse d un déterminisme polygénique additif. Le raisonnement est base sur le modèle Poisson log normal et son traitement par quasi-vraisemblance. La formule presentee rend compte d une asymétrie de la réponse. Sa relation avec la formule classique R ipơG est expliquée. Ses propriétés sont illustrées par un exemple de selection de males en contrôle de descendance. variables discretes distribution de Poisson prediction réponse à la selection asymétrie de la réponse The theory of response of selection under polygenic inheritance relies basically on the normality assumption and kindred linear model methodology. Discrete traits cannot be properly analyzed using classical procedures Gianola 1982 and nonlinear statistical methods have recently been proposed for genetic evaluation of such traits see eg review by Ducrocq 1990 . .

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