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

Rasmus Waagepetersen1*, Noelia Ibánẽz-Escriche2 and Daniel Sorensen3

Author Affiliations

1 Department of Mathematical Sciences, Aalborg University, 9220 Aalborg, Denmark

2 IRTA, Avda. Rovira Roure, 25198 Lleida, Spain

3 Department of Genetics and Biotechnology, Danish Institute of Agricultural Sciences, P.O. Box 50, 8830 Tjele, Denmark

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Genetics Selection Evolution 2008, 40:161-176  doi:10.1186/1297-9686-40-2-161

The electronic version of this article is the complete one and can be found online at:

Received:14 February 2007
Accepted:7 September 2007
Published:15 March 2008

© 2008 INRA, EDP Sciences


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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