title: Improved techniques for sampling complex pedigrees with the Gibbs sampler.
abstract: Markov chain Monte Carlo (MCMC) methods have been widely used to overcome computational problems in linkage and segregation analyses. Many variants of this approach exist and are practiced; among the most popular is the Gibbs sampler. The Gibbs sampler is simple to implement but has (in its simplest form) mixing and reducibility problems; furthermore in order to initiate a Gibbs sampling chain we need a starting genotypic or allelic configuration which is consistent with the marker data in the pedigree and which has suitable weight in the joint distribution. We outline a procedure for finding such a configuration in pedigrees which have too many loci to allow for exact peeling. We also explain how this technique could be used to implement a blocking Gibbs sampler.
authors.affiliation: Iowa State University, Ames, IA 50011, USA. abraham@iastate.edu
authors.forename: K Joseph
authors.initials: KJ
authors.lastname: Abraham
authors.forename: Liviu R
authors.initials: LR
authors.lastname: Totir
authors.forename: Rohan L
authors.initials: RL
authors.lastname: Fernando
mesh_terms: D000465:Algorithms; D000818:Animals; D005787:Gene Frequency; D008040:Genetic Linkage; D006801:Humans; D010375:Pedigree; D011336:Probability; D013223:Statistics as Topic / Q000379:methods*