A comparison of strategies for Markov chain Monte Carlo computation in quantitative genetics.
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.
Waagepetersen
Rasmus
R
Department of Mathematical Sciences, Aalborg University, 9220 Aalborg, Denmark. rw@math.aau.dk
Ibánez-Escriche
Noelia
N
Sorensen
Daniel
D
D000465:Algorithms; D000818:Animals; D014644:Genetic Variation; D008390:Markov Chains; D009010:Monte Carlo Method; D011817:Rabbits; D012908:Snails; D013552:Swine