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Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học thế giới đề tài: A Bayesian approach for constructing genetic maps when markers are miscoded | 353 Genet Sei. Evol. 34 2002 353-369 INRA EDP Sciences 2002 DOI 10.1051 gse 2002012 Original article a BaYesian approach for constructing genetic maps when markers are miscoded Guilherme J.M. Rosaa Brian s. Yandellb Daniel Gianolac a Department of Biostatistics unESP Botucatu SP Brazil b Departments of Statistics and of Horticulture University of Winconsin Madison WI USA c Departments of Animal Science and of Biostatistics Medical Informatics University of Wisconsin Madison WI USA Received 10 September 2001 accepted 8 February 2002 Abstract - The advent of molecular markers has created opportunities for a better understanding of quantitative inheritance and for developing novel strategies for genetic improvement of agricultural species using information on quantitative trait loci QTL . A QTL analysis relies on accurate genetic marker maps. At present most statistical methods used for map construction ignore the fact that molecular data may be read with error. Often however there is ambiguity about some marker genotypes. A Bayesian MCMC approach for inferences about a genetic marker map when random miscoding of genotypes occurs is presented and simulated and real data sets are analyzed. The results suggest that unless there is strong reason to believe that genotypes are ascertained without error the proposed approach provides more reliable inference on the genetic map. genetic map construction miscoded genotypes Bayesian inference 1. introduction The advent of molecular markers has created opportunities for a better understanding of quantitative inheritance and for developing novel strategies for genetic improvement in agriculture. For example the location and the effects of quantitative trait loci QTL can be inferred by combining information from marker genotypes and phenotypic scores of individuals in a population in linkage disequilibrium such as in experiments with line crosses e.g. using backcross or F2 progenies. A QTL analysis relies on the availability of .
