Strategies for MCMC computation in quantitative genetics.
Rasmus Plenge Waagepetersen, Noelia Ibáñez‐Escriche, Daniel Sørensen · VBN Forskningsportal (Aalborg Universitet) · 2006
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 highdimensional but with a sparse inverse. Maximum likelihood inference and Bayesian inference for the linear mixed model are well-studied topics (Sorensen and Gianola, 2002). Regarding Bayesian inference, with appropriate choice of priors, the full conditional distributions are standard distributions and Gibbs sampling can be implemented relatively straightforwardly.