title: A survey of current Bayesian gene mapping methods.
abstract: Recently, there has been much interest in the use of Bayesian statistical methods for performing genetic analyses. Many of the computational difficulties previously associated with Bayesian analysis, such as multidimensional integration, can now be easily overcome using modern high-speed computers and Markov chain Monte Carlo (MCMC) methods. Much of this new technology has been used to perform gene mapping, especially through the use of multi-locus linkage disequilibrium techniques. This review attempts to summarise some of the currently available methods and the software available to implement these methods.
authors.affiliation: Department of Preventive Medicine, University of Southern California, 1540 Alcazar Street, CHP-220, Los Angeles, CA 90089-9011, USA. jmolitor@usc.edu
authors.forename: John
authors.initials: J
authors.lastname: Molitor
authors.forename: Paul
authors.initials: P
authors.lastname: Marjoram
authors.forename: David
authors.initials: D
authors.lastname: Conti
authors.forename: Duncan
authors.initials: D
authors.lastname: Thomas
mesh_terms: D001499:Bayes Theorem; D002874:Chromosome Mapping; D008040:Genetic Linkage; D006801:Humans; D008957:Models, Genetic; D009010:Monte Carlo Method