Skip to content

Advertisement

Genetics Selection Evolution

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

https://doi.org/10.1186/1297-9686-40-2-161

Received: 14 February 2007

Accepted: 7 September 2007

Published: 15 March 2008

Abstract

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.

Keywords

Langevin-HastingsMarkov chain Monte Carlonormal approximationproposal distributionsreparameterization

(To access the full article, please see PDF)

Authors’ Affiliations

(1)
Department of Mathematical Sciences, Aalborg University, Aalborg, Denmark
(2)
IRTA, Avda. Rovira RoureLleida, Spain
(3)
Department of Genetics and Biotechnology, Danish Institute of Agricultural Sciences, Tjele, Denmark

Copyright

© INRA, EDP Sciences 2008

Advertisement