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Báo cáo sinh học: " A comparison of strategies for Markov chain Monte Carlo computation in quantitative genetics"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học thế giới đề tài: A comparison of strategies for Markov chain Monte Carlo computation in quantitative genetics | Genet. Sel. Evol. 40 2008 161-176 INRA EDP Sciences 2008 DOI 10.1051 gse 2007042 Available online at www.gse-journal.org Original article A comparison of strategies for Markov chain Monte Carlo computation in quantitative genetics Rasmus WAAGEPETERSEN1 Noelia IbAnEZ-ESCRICHE2 Daniel Sorensen3 1 Department of Mathematical Sciences Aalborg University 9220 Aalborg Denmark 2IRTA 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 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 Langevin-Hastings Markov chain Monte Carlo normal approximation proposal distributions reparameterization 1. INTRODUCTION Given observations of a trait and a pedigree for a group of animals the basic model in quantitative genetics is a linear mixed model with genetic random effects. The correlation matrix of the genetic random effects is determined by the pedigree and is typically very high-dimensional but with a sparse inverse. Maximum likelihood inference and Bayesian inference for the linear mixed model are well-studied topics 16 . Regarding Bayesian inference with appropriate choice of priors the full conditional distributions are .