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Improved techniques for sampling complex pedigrees with the Gibbs sampler.

K Joseph Abraham·Liviu R Totir·Rohan L Fernando
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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.

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AlgorithmsAnimalsGene FrequencyGenetic LinkageHumansPedigreeProbabilityStatistics as TopicJournal Article

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K Joseph Abraham, Liviu R Totir, Rohan L Fernando. 2007-01-11. Improved techniques for sampling complex pedigrees with the Gibbs sampler.. https://doi.org/10.1186/1297-9686-39-1-27

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