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Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters.
Heredity (Edinb). 2012 Oct; 109(4):235-45.H

Abstract

Accurate and fast estimation of genetic parameters that underlie quantitative traits using mixed linear models with additive and dominance effects is of great importance in both natural and breeding populations. Here, we propose a new fast adaptive Markov chain Monte Carlo (MCMC) sampling algorithm for the estimation of genetic parameters in the linear mixed model with several random effects. In the learning phase of our algorithm, we use the hybrid Gibbs sampler to learn the covariance structure of the variance components. In the second phase of the algorithm, we use this covariance structure to formulate an effective proposal distribution for a Metropolis-Hastings algorithm, which uses a likelihood function in which the random effects have been integrated out. Compared with the hybrid Gibbs sampler, the new algorithm had better mixing properties and was approximately twice as fast to run. Our new algorithm was able to detect different modes in the posterior distribution. In addition, the posterior mode estimates from the adaptive MCMC method were close to the REML (residual maximum likelihood) estimates. Moreover, our exponential prior for inverse variance components was vague and enabled the estimated mode of the posterior variance to be practically zero, which was in agreement with the support from the likelihood (in the case of no dominance). The method performance is illustrated using simulated data sets with replicates and field data in barley.

Authors+Show Affiliations

Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany. boby.mathew@hotmail.comNo affiliation info availableNo affiliation info availableNo 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

22805656

Citation

Mathew, B, et al. "Bayesian Adaptive Markov Chain Monte Carlo Estimation of Genetic Parameters." Heredity, vol. 109, no. 4, 2012, pp. 235-45.
Mathew B, Bauer AM, Koistinen P, et al. Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters. Heredity (Edinb). 2012;109(4):235-45.
Mathew, B., Bauer, A. M., Koistinen, P., Reetz, T. C., Léon, J., & Sillanpää, M. J. (2012). Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters. Heredity, 109(4), 235-45. https://doi.org/10.1038/hdy.2012.35
Mathew B, et al. Bayesian Adaptive Markov Chain Monte Carlo Estimation of Genetic Parameters. Heredity (Edinb). 2012;109(4):235-45. PubMed PMID: 22805656.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters. AU - Mathew,B, AU - Bauer,A M, AU - Koistinen,P, AU - Reetz,T C, AU - Léon,J, AU - Sillanpää,M J, Y1 - 2012/07/18/ PY - 2012/7/19/entrez PY - 2012/7/19/pubmed PY - 2013/2/16/medline SP - 235 EP - 45 JF - Heredity JO - Heredity (Edinb) VL - 109 IS - 4 N2 - Accurate and fast estimation of genetic parameters that underlie quantitative traits using mixed linear models with additive and dominance effects is of great importance in both natural and breeding populations. Here, we propose a new fast adaptive Markov chain Monte Carlo (MCMC) sampling algorithm for the estimation of genetic parameters in the linear mixed model with several random effects. In the learning phase of our algorithm, we use the hybrid Gibbs sampler to learn the covariance structure of the variance components. In the second phase of the algorithm, we use this covariance structure to formulate an effective proposal distribution for a Metropolis-Hastings algorithm, which uses a likelihood function in which the random effects have been integrated out. Compared with the hybrid Gibbs sampler, the new algorithm had better mixing properties and was approximately twice as fast to run. Our new algorithm was able to detect different modes in the posterior distribution. In addition, the posterior mode estimates from the adaptive MCMC method were close to the REML (residual maximum likelihood) estimates. Moreover, our exponential prior for inverse variance components was vague and enabled the estimated mode of the posterior variance to be practically zero, which was in agreement with the support from the likelihood (in the case of no dominance). The method performance is illustrated using simulated data sets with replicates and field data in barley. SN - 1365-2540 UR - https://www.unboundmedicine.com/medline/citation/22805656/Bayesian_adaptive_Markov_chain_Monte_Carlo_estimation_of_genetic_parameters_ L2 - https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22805656/ DB - PRIME DP - Unbound Medicine ER -