Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học thế giới đề tài: Gibbs sampling in the mixed inheritance model using graph theory | Genet. Sei. Evol. 31 1999 3-24 c Inra Elsevier Paris 3 Original article Blocking Gibbs sampling in the mixed inheritance model using graph theory Mogens Sand0 Lunda Claus Skaanning Jensenb a DIAS Department of Breeding and Genetics Research Centre Foulum . Box 50 8830 Tjele Denmark b AUC Department of Computer Science Fredrik Bajers Vej 7E. 9220 Aalborg 0 Denmark Received 10 February 1998 accepted 18 November 1998 Abstract - For the mixed inheritance model MIM including both a single locus and a polygenic effect we present a Markov chain Monte Carlo MCMC algorithm in which discrete genotypes of the single locus are sampled in large blocks from their joint conditional distribution. This requires exact calculation of the joint distribution of a given block which can be very complicated. Calculations of the joint distributions were obtained using graph theoretic methods for Bayesian networks. An example of a simulated pedigree suggests that this algorithm is more efficient than algorithms with univariate updating or algorithms using blocking of sires with their final offspring. The algorithm can be extended to models utilising genetic marker information in which case it holds the potential to solve the critical reducibility problem of MCMC methods often associated with such models. Inra Elsevier Paris blocking Gibbs sampling mixed inheritance model graph theory Bayesian network Resume Echantillonnage de Gibbs par bloc dans le modèle à hérédité mixte en utilisant la théorie des graphes. Pour le cas de 1 hérédité mixte un seul locus avec un fond polygénique on présente un algorithme de Monte-Carlo par chaĩnes de Markov MCMC dans lequel les genotypes au locus unique sont échantillonnés en blocs importants à partir de leur distribution jointe conditionnelle. Ceci exige le calcul exact de distribution conjointe d un bloc donné qui peut être très compliquée. Le calcul des distributions jointes est obtenu en utilisant des méthodes graphiques théoriques pour les réseaux .
