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

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.

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Correspondence to Rasmus Waagepetersen.

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Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Waagepetersen, R., Ibánẽz-Escriche, N. & Sorensen, D. A comparison of strategies for Markov chain Monte Carlo computation in quantitative genetics. Genet Sel Evol 40, 161 (2008). https://doi.org/10.1186/1297-9686-40-2-161

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

Keywords

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