title: Bayesian and maximum likelihood estimation of genetic maps.
abstract: There has recently been increased interest in the use of Markov Chain Monte Carlo (MCMC)-based Bayesian methods for estimating genetic maps. The advantage of these methods is that they can deal accurately with missing data and genotyping errors. Here we present an extension of the previous methods that makes the Bayesian method applicable to large data sets. We present an extensive simulation study examining the statistical properties of the method and comparing it with the likelihood method implemented in Mapmaker. We show that the Maximum A Posteriori (MAP) estimator of the genetic distances, corresponding to the maximum likelihood estimator, performs better than estimators based on the posterior expectation. We also show that while the performance is similar between Mapmaker and the MCMC-based method in the absence of genotyping errors, the MCMC-based method has a distinct advantage in the presence of genotyping errors. A similar advantage of the Bayesian method was not observed for missing data. We also re-analyse a recently published set of data from the eggplant and show that the use of the MCMC-based method leads to smaller estimates of genetic distances.
authors.affiliation: Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14850, USA.
authors.forename: Thomas L
authors.initials: TL
authors.lastname: York
authors.forename: Richard T
authors.initials: RT
authors.lastname: Durrett
authors.forename: Steven
authors.initials: S
authors.lastname: Tanksley
authors.forename: Rasmus
authors.initials: R
authors.lastname: Nielsen
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D008040:Genetic Linkage; D016013:Likelihood Functions; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D032323:Solanum melongena / Q000235:genetics