Genetics Selection Evolution (Mar 2008)

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

  • Sorensen Daniel,
  • Ibánẽz-Escriche Noelia,
  • Waagepetersen Rasmus

DOI
https://doi.org/10.1186/1297-9686-40-2-161
Journal volume & issue
Vol. 40, no. 2
pp. 161 – 176

Abstract

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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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