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

  • Rasmus Waagepetersen1Email author,

    Affiliated with

    • Noelia Ibánẽz-Escriche2 and

      Affiliated with

      • Daniel Sorensen3

        Affiliated with

        Genetics Selection Evolution200840:161

        DOI: 10.1186/1297-9686-40-2-161

        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
        IRTA, Avda. Rovira Roure
        Department of Genetics and Biotechnology, Danish Institute of Agricultural Sciences


        © INRA, EDP Sciences 2008