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Influence of priors in Bayesian estimation of genetic parameters for multivariate threshold models using Gibbs sampling

Simulated data were used to investigate the influence of the choice of priors on estimation of genetic parameters in multivariate threshold models using Gibbs sampling. We simulated additive values, residuals and fixed effects for one continuous trait and liabilities of four binary traits, and QTL e...

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Autores principales: Stock, Kathrin Friederike, Distl, Ottmar, Hoeschele, Ina
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2682833/
https://www.ncbi.nlm.nih.gov/pubmed/17306197
http://dx.doi.org/10.1186/1297-9686-39-2-123
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author Stock, Kathrin Friederike
Distl, Ottmar
Hoeschele, Ina
author_facet Stock, Kathrin Friederike
Distl, Ottmar
Hoeschele, Ina
author_sort Stock, Kathrin Friederike
collection PubMed
description Simulated data were used to investigate the influence of the choice of priors on estimation of genetic parameters in multivariate threshold models using Gibbs sampling. We simulated additive values, residuals and fixed effects for one continuous trait and liabilities of four binary traits, and QTL effects for one of the liabilities. Within each of four replicates six different datasets were generated which resembled different practical scenarios in horses with respect to number and distribution of animals with trait records and availability of QTL information. (Co)Variance components were estimated using a Bayesian threshold animal model via Gibbs sampling. The Gibbs sampler was implemented with both a flat and a proper prior for the genetic covariance matrix. Convergence problems were encountered in > 50% of flat prior analyses, with indications of potential or near posterior impropriety between about round 10 000 and 100 000. Terminations due to non-positive definite genetic covariance matrix occurred in flat prior analyses of the smallest datasets. Use of a proper prior resulted in improved mixing and convergence of the Gibbs chain. In order to avoid (near) impropriety of posteriors and extremely poorly mixing Gibbs chains, a proper prior should be used for the genetic covariance matrix when implementing the Gibbs sampler.
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spelling pubmed-26828332009-05-16 Influence of priors in Bayesian estimation of genetic parameters for multivariate threshold models using Gibbs sampling Stock, Kathrin Friederike Distl, Ottmar Hoeschele, Ina Genet Sel Evol Research Simulated data were used to investigate the influence of the choice of priors on estimation of genetic parameters in multivariate threshold models using Gibbs sampling. We simulated additive values, residuals and fixed effects for one continuous trait and liabilities of four binary traits, and QTL effects for one of the liabilities. Within each of four replicates six different datasets were generated which resembled different practical scenarios in horses with respect to number and distribution of animals with trait records and availability of QTL information. (Co)Variance components were estimated using a Bayesian threshold animal model via Gibbs sampling. The Gibbs sampler was implemented with both a flat and a proper prior for the genetic covariance matrix. Convergence problems were encountered in > 50% of flat prior analyses, with indications of potential or near posterior impropriety between about round 10 000 and 100 000. Terminations due to non-positive definite genetic covariance matrix occurred in flat prior analyses of the smallest datasets. Use of a proper prior resulted in improved mixing and convergence of the Gibbs chain. In order to avoid (near) impropriety of posteriors and extremely poorly mixing Gibbs chains, a proper prior should be used for the genetic covariance matrix when implementing the Gibbs sampler. BioMed Central 2007-02-17 /pmc/articles/PMC2682833/ /pubmed/17306197 http://dx.doi.org/10.1186/1297-9686-39-2-123 Text en Copyright © 2007 INRA, EDP Sciences
spellingShingle Research
Stock, Kathrin Friederike
Distl, Ottmar
Hoeschele, Ina
Influence of priors in Bayesian estimation of genetic parameters for multivariate threshold models using Gibbs sampling
title Influence of priors in Bayesian estimation of genetic parameters for multivariate threshold models using Gibbs sampling
title_full Influence of priors in Bayesian estimation of genetic parameters for multivariate threshold models using Gibbs sampling
title_fullStr Influence of priors in Bayesian estimation of genetic parameters for multivariate threshold models using Gibbs sampling
title_full_unstemmed Influence of priors in Bayesian estimation of genetic parameters for multivariate threshold models using Gibbs sampling
title_short Influence of priors in Bayesian estimation of genetic parameters for multivariate threshold models using Gibbs sampling
title_sort influence of priors in bayesian estimation of genetic parameters for multivariate threshold models using gibbs sampling
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2682833/
https://www.ncbi.nlm.nih.gov/pubmed/17306197
http://dx.doi.org/10.1186/1297-9686-39-2-123
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