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dc.contributor.authorVarona Aguado, Luís
dc.contributor.authorGarcía Cortés, Luis Alberto
dc.contributor.authorPerez Enciso, Miguel
dc.description.abstractA fundamental issue in quantitative trait locus (QTL) mapping is to determine the plausibility of the presence of a QTL at a given genome location. Bayesian analysis offers an attractive way of testing alternative models (here, QTL vs. no-QTL) via the Bayes factor. There have been several numerical approaches to computing the Bayes factor, mostly based on Markov Chain Monte Carlo (MCMC), but these strategies are subject to numerical or stability problems. We propose a simple and stable approach to calculating the Bayes factor between nested models. The procedure is based on a reparameterization of a variance component model in terms of intra-class correlation. The Bayes factor can then be easily calculated from the output of a MCMC scheme by averaging conditional densities at the null intra-class correlation. We studied the performance of the method using simulation. We applied this approach to QTL analysis in an outbred population. We also compared it with the Likelihood Ratio Test and we analyzed its stability. Simulation results were very similar to the simulated parameters. The posterior probability of the QTL model increases as the QTL effect does. The location of the QTL was also correctly obtained. The use of meta-analysis is suggested from the properties of the Bayes factor.ca_ES
dc.publisherBioMed Centralca_ES
dc.relation.isformatofReproducció del document publicat a
dc.relation.ispartofGenetics Selection Evolution, 2001, vol. 33, p. 133-152ca_ES
dc.rightscc-by (c) Varona et al., 2001ca_ES
dc.subjectBayes factorca_ES
dc.subjectQuantitative Trait Locica_ES
dc.subjectHypothesis testingca_ES
dc.titleBayes factors for detection of quantitative trait locica_ES

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