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Efficient Markov chain Monte Carlo implementation of Bayesian analysis of additive and dominance genetic variances in noninbred pedigrees.
Genetics. 2008 Jun; 179(2):1101-12.G

Abstract

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.

Authors+Show Affiliations

Department of Forest Genetics and Plant Physiology, Swedish Agricultural University (SLU), SE-901 83 Umeå, Sweden. patrik.waldmann@genfys.slu.seNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

18558655

Citation

Waldmann, Patrik, et al. "Efficient Markov Chain Monte Carlo Implementation of Bayesian Analysis of Additive and Dominance Genetic Variances in Noninbred Pedigrees." Genetics, vol. 179, no. 2, 2008, pp. 1101-12.
Waldmann P, Hallander J, Hoti F, et al. Efficient Markov chain Monte Carlo implementation of Bayesian analysis of additive and dominance genetic variances in noninbred pedigrees. Genetics. 2008;179(2):1101-12.
Waldmann, P., Hallander, J., Hoti, F., & Sillanpää, M. J. (2008). Efficient Markov chain Monte Carlo implementation of Bayesian analysis of additive and dominance genetic variances in noninbred pedigrees. Genetics, 179(2), 1101-12. https://doi.org/10.1534/genetics.107.084160
Waldmann P, et al. Efficient Markov Chain Monte Carlo Implementation of Bayesian Analysis of Additive and Dominance Genetic Variances in Noninbred Pedigrees. Genetics. 2008;179(2):1101-12. PubMed PMID: 18558655.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Efficient Markov chain Monte Carlo implementation of Bayesian analysis of additive and dominance genetic variances in noninbred pedigrees. AU - Waldmann,Patrik, AU - Hallander,Jon, AU - Hoti,Fabian, AU - Sillanpää,Mikko J, PY - 2008/6/19/pubmed PY - 2008/8/22/medline PY - 2008/6/19/entrez SP - 1101 EP - 12 JF - Genetics JO - Genetics VL - 179 IS - 2 N2 - 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. SN - 0016-6731 UR - https://www.unboundmedicine.com/medline/citation/18558655/Efficient_Markov_chain_Monte_Carlo_implementation_of_Bayesian_analysis_of_additive_and_dominance_genetic_variances_in_noninbred_pedigrees_ L2 - http://www.genetics.org/cgi/pmidlookup?view=long&pmid=18558655 DB - PRIME DP - Unbound Medicine ER -