- THIS ARTICLE
- Full Text
- Full Text (PDF)
- Alert me when this article is cited
- Alert me if a correction is posted
- SERVICES
- Similar articles in this journal
- Similar articles in PubMed
- Alert me to new issues of the journal
- Download to citation manager
- Reprints & Permissions
- GOOGLE SCHOLAR
- Articles by Waldmann, P.
- Articles by Sillanpää, M. J.
- PUBMED
- PubMed Citation
- Articles by Waldmann, P.
- Articles by Sillanpää, M. J.
Genetics, Vol. 179, 1101-1112, June 2008, Copyright © 2008
doi:10.1534/genetics.107.084160
Efficient Markov Chain Monte Carlo Implementation of Bayesian Analysis of Additive and Dominance Genetic Variances in Noninbred Pedigrees
Patrik Waldmann*,1,
Jon Hallander*,
Fabian Hoti
,
and
Mikko J. Sillanpää
* Department of Forest Genetics and Plant Physiology, Swedish Agricultural University (SLU), SE-901 83 Umeå, Sweden, Department of Mathematics and Statistics, University of Helsinki, FIN-00014 Helsinki, Finland and Department of Vaccines, National Public Health Institute, FIN-00300 Helsinki, Finland
1 Corresponding author: Department of Forest Genetics and Plant Physiology, Swedish Agricultural University (SLU), SE-901 83 Umeå, Sweden.
E-mail: patrik.waldmann{at}genfys.slu.se
Accurate and fast computation of quantitative genetic variance parameters is of great importance in both natural and breeding populations. For experimental designs with complex relationship structures it can be important to include both additive and dominance variance components in the statistical model. In this study, we introduce a Bayesian Gibbs sampling approach for estimation of additive and dominance genetic variances in the traditional infinitesimal model. The method can handle general pedigrees without inbreeding. To optimize between computational time and good mixing of the Markov chain Monte Carlo (MCMC) chains, we used a hybrid Gibbs sampler that combines a single site and a blocked Gibbs sampler. The speed of the hybrid sampler and the mixing of the single-site sampler were further improved by the use of pretransformed variables. Two traits (height and trunk diameter) from a previously published diallel progeny test of Scots pine (Pinus sylvestris L.) and two large simulated data sets with different levels of dominance variance were analyzed. We also performed Bayesian model comparison on the basis of the posterior predictive loss approach. Results showed that models with both additive and dominance components had the best fit for both height and diameter and for the simulated data with high dominance. For the simulated data with low dominance, we needed an informative prior to avoid the dominance variance component becoming overestimated. The narrow-sense heritability estimates in the Scots pine data were lower compared to the earlier results, which is not surprising because the level of dominance variance was rather high, especially for diameter. In general, the hybrid sampler was considerably faster than the blocked sampler and displayed better mixing properties than the single-site sampler.