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Improved techniques for sampling complex pedigrees with the Gibbs sampler

Genetics Selection Evolution volume 39, Article number: 27 (2007) Cite this article

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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Authors and Affiliations

  1. 1301 Agronomy Hall, Iowa State University, Ames, IA, 50011, USA

    K. Joseph Abraham

  2. Department of Animal Science, Iowa State University, Ames, IA, 50011, USA

    Liviu R. Totir & Rohan L. Fernando

  3. Lawrence H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA, 50011, USA

    Rohan L. Fernando

Authors
  1. K. Joseph Abraham
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  2. Liviu R. Totir
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  3. Rohan L. Fernando
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Correspondence to K. Joseph Abraham.

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Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abraham, K.J., Totir, L.R. & Fernando, R.L. Improved techniques for sampling complex pedigrees with the Gibbs sampler. Genet Sel Evol 39, 27 (2007). https://doi.org/10.1186/1297-9686-39-1-27

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  • DOI: https://doi.org/10.1186/1297-9686-39-1-27

Keywords

Genetics Selection Evolution

ISSN: 1297-9686