Open Access

A comparison of strategies for Markov chain Monte Carlo computation in quantitative genetics

  • Rasmus Waagepetersen1Email author,
  • Noelia Ibánẽz-Escriche2 and
  • Daniel Sorensen3
Genetics Selection Evolution200840:161

Received: 14 February 2007

Accepted: 7 September 2007

Published: 15 March 2008


In quantitative genetics, Markov chain Monte Carlo (MCMC) methods are indispensable for statistical inference in non-standard models like generalized linear models with genetic random effects or models with genetically structured variance heterogeneity. A particular challenge for MCMC applications in quantitative genetics is to obtain efficient updates of the high-dimensional vectors of genetic random effects and the associated covariance parameters. We discuss various strategies to approach this problem including reparameterization, Langevin-Hastings updates, and updates based on normal approximations. The methods are compared in applications to Bayesian inference for three data sets using a model with genetically structured variance heterogeneity.


Langevin-Hastings Markov chain Monte Carlo normal approximation proposal distributions reparameterization

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Authors’ Affiliations

Department of Mathematical Sciences, Aalborg University
Department of Genetics and Biotechnology, Danish Institute of Agricultural Sciences


© INRA, EDP Sciences 2008