Issue |
Genet. Sel. Evol.
Volume 40, Number 2, March-April 2008
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Page(s) | 161 - 176 | |
DOI | https://doi.org/10.1051/gse:2007042 | |
Published online | 27 February 2008 |
DOI: 10.1051/gse:2007042
A comparison of strategies for Markov chain Monte Carlo computation in quantitative genetics
Rasmus Waagepetersen1, Noelia Ibánez-Escriche2 and Daniel Sorensen31 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
(Received 14 February 2007; accepted 7 September 2007; published online 27 February 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
Key words: Langevin-Hastings / Markov chain Monte Carlo / normal approximation / proposal distributions / reparameterization
Correspondence and reprints: rw@math.aau.dk
© INRA, EDP Sciences 2008