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Open Access

Irreducibility and efficiency of ESIP to sample marker genotypes in large pedigrees with loops

  • Soledad A. Fernández1,
  • Rohan L. Fernando,
  • Bernt Guldbrandtsen4,
  • Christian Stricker3,
  • Matthias Schelling5 and
  • Alicia L. Carriquiry3
Genetics Selection Evolution200234:537

Received: 21 August 2001

Accepted: 6 May 2002

Published: 15 September 2002


Markov chain Monte Carlo (MCMC) methods have been proposed to overcome computational problems in linkage and segregation analyses. This approach involves sampling genotypes at the marker and trait loci. Among MCMC methods, scalar-Gibbs is the easiest to implement, and it is used in genetics. However, the Markov chain that corresponds to scalar-Gibbs may not be irreducible when the marker locus has more than two alleles, and even when the chain is irreducible, mixing has been observed to be slow. Joint sampling of genotypes has been proposed as a strategy to overcome these problems. An algorithm that combines the Elston-Stewart algorithm and iterative peeling (ESIP sampler) to sample genotypes jointly from the entire pedigree is used in this study. Here, it is shown that the ESIP sampler yields an irreducible Markov chain, regardless of the number of alleles at a locus. Further, results obtained by ESIP sampler are compared with other methods in the literature. Of the methods that are guaranteed to be irreducible, ESIP was the most efficient.


Metropolis-HastingsirreducibilityElston-Stewart algorithmiterative peeling

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Authors’ Affiliations

Department of Statistics, Ohio State University, Columbus, USA
Department of Animal Science, Iowa State University, Ames, USA
Department of Statistics, Iowa State University, Ames, USA
Danish Institute of Animal Science, Foulum, Denmark
Institute of Animal Sciences, Swiss Federal Institute of Technology, ETH-Zentrum CLU, Zürich, Switzerland


© INRA, EDP Sciences 2002