A Bayesian approach for constructing genetic maps when markers are miscoded

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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.



Bayesian inference, Genetic map construction, Miscoded genotypes, molecular marker, Bayesian analysis, genetics, selective breeding, Bayes theorem, biological model, brassica napus, chromosome map, computer simulation, data analysis, gene mapping, genetic database, genetic marker, genetic recombination, genotype, methodology, multifactorial inheritance, nonhuman, phenotype, quantitative trait, quantitative trait locus, reliability, statistical analysis, rapeseed, Brassica, Brassica napus, Bayes Theorem, Chromosome Mapping, Computer Simulation, Databases, Genetic, Genetic Markers, Genotype, Models, Genetic, Quantitative Trait, Heritable, Recombination, Genetic

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Genetics Selection Evolution, v. 34, n. 3, p. 353-369, 2002.