A novel individual-tree mixed model to account for competition and environmental heterogeneity: a Bayesian approach
Negative correlation caused by competition among individuals and positive spatial correlation due to environmental heterogeneity may lead to biases in estimating genetic parameters and predicting breeding values (BVs) from forest genetic trials. Former models dealing with competition and environment...
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| Format: | info:ar-repo/semantics/artículo |
| Language: | Inglés |
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Springer
2018
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| Online Access: | http://hdl.handle.net/20.500.12123/3380 https://link.springer.com/article/10.1007/s11295-015-0917-3#citeas https://doi.org/10.1007/s11295-015-0917-3 |
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| author | Cappa, Eduardo Pablo Muñoz, Facundo Sanchez, Leopoldo Cantet, Rodolfo Juan Carlos |
| author_browse | Cantet, Rodolfo Juan Carlos Cappa, Eduardo Pablo Muñoz, Facundo Sanchez, Leopoldo |
| author_facet | Cappa, Eduardo Pablo Muñoz, Facundo Sanchez, Leopoldo Cantet, Rodolfo Juan Carlos |
| author_sort | Cappa, Eduardo Pablo |
| collection | INTA Digital |
| description | Negative correlation caused by competition among individuals and positive spatial correlation due to environmental heterogeneity may lead to biases in estimating genetic parameters and predicting breeding values (BVs) from forest genetic trials. Former models dealing with competition and environmental heterogeneity did not account for the additive relationships among trees or for the full spatial covariance. This paper extends an individual-tree mixed model with direct additive genetic, genetic, and environmental competition effects, by incorporating a two-dimensional smoothing surface to account for complex patterns of environmental heterogeneity (competition + spatial model (CSM)). We illustrate the proposed model using simulated and real data from a loblolly pine progeny trial. The CSM was compared with three reduced individual-tree mixed models using a real dataset, while simulations comprised only CSM versus true-parameters comparisons. Dispersion parameters were estimated using Bayesian techniques via Gibbs sampling. Simulation results showed that the CSM yielded posterior mean estimates of variance components with slight or negligible biases in the studied scenarios, except for the permanent environment variance. The worst performance of the simulated CSM was under a scenario with weak competition effects and small-scale environmental heterogeneity. When analyzing real data, the CSM yielded a lower value of the deviance information criterion than the reduced models. Moreover, although correlations between predicted BVs calculated from CSM and from a standard model with block effects and direct genetic effects only were high, the ranking among the top 5 % ranked individuals showed differences which indicated that the two models will have quite different genotype selections for the next cycle of breeding. |
| format | info:ar-repo/semantics/artículo |
| id | INTA3380 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | INTA33802018-09-17T18:45:20Z A novel individual-tree mixed model to account for competition and environmental heterogeneity: a Bayesian approach Cappa, Eduardo Pablo Muñoz, Facundo Sanchez, Leopoldo Cantet, Rodolfo Juan Carlos Competencia Biológica Biological Competition Environmental Heterogeneity Gibbs Sampling Individual-tree Mixed Model Heterogeneidad Ambiental Muestreo de Gibbs Modelo Mixto Arbol Individual Negative correlation caused by competition among individuals and positive spatial correlation due to environmental heterogeneity may lead to biases in estimating genetic parameters and predicting breeding values (BVs) from forest genetic trials. Former models dealing with competition and environmental heterogeneity did not account for the additive relationships among trees or for the full spatial covariance. This paper extends an individual-tree mixed model with direct additive genetic, genetic, and environmental competition effects, by incorporating a two-dimensional smoothing surface to account for complex patterns of environmental heterogeneity (competition + spatial model (CSM)). We illustrate the proposed model using simulated and real data from a loblolly pine progeny trial. The CSM was compared with three reduced individual-tree mixed models using a real dataset, while simulations comprised only CSM versus true-parameters comparisons. Dispersion parameters were estimated using Bayesian techniques via Gibbs sampling. Simulation results showed that the CSM yielded posterior mean estimates of variance components with slight or negligible biases in the studied scenarios, except for the permanent environment variance. The worst performance of the simulated CSM was under a scenario with weak competition effects and small-scale environmental heterogeneity. When analyzing real data, the CSM yielded a lower value of the deviance information criterion than the reduced models. Moreover, although correlations between predicted BVs calculated from CSM and from a standard model with block effects and direct genetic effects only were high, the ranking among the top 5 % ranked individuals showed differences which indicated that the two models will have quite different genotype selections for the next cycle of breeding. Instituto de Recursos Biológicos Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Muñoz, Facundo. Institut National de la Recherche Agronomique. Unité Amélioration, Génétique et Physiologie Forestières; Francia Fil: Sanchez, Leopoldo. Institut National de la Recherche Agronomique. Unité Amélioration, Génétique et Physiologie Forestières; Francia Fil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina 2018-09-17T18:39:48Z 2018-09-17T18:39:48Z 2015-12 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/3380 https://link.springer.com/article/10.1007/s11295-015-0917-3#citeas 1614-2942 1614-2950 (Online) https://doi.org/10.1007/s11295-015-0917-3 eng info:eu-repo/semantics/restrictedAccess application/pdf Springer Tree genetics and genomes 11 : 120. (December 2015) |
| spellingShingle | Competencia Biológica Biological Competition Environmental Heterogeneity Gibbs Sampling Individual-tree Mixed Model Heterogeneidad Ambiental Muestreo de Gibbs Modelo Mixto Arbol Individual Cappa, Eduardo Pablo Muñoz, Facundo Sanchez, Leopoldo Cantet, Rodolfo Juan Carlos A novel individual-tree mixed model to account for competition and environmental heterogeneity: a Bayesian approach |
| title | A novel individual-tree mixed model to account for competition and environmental heterogeneity: a Bayesian approach |
| title_full | A novel individual-tree mixed model to account for competition and environmental heterogeneity: a Bayesian approach |
| title_fullStr | A novel individual-tree mixed model to account for competition and environmental heterogeneity: a Bayesian approach |
| title_full_unstemmed | A novel individual-tree mixed model to account for competition and environmental heterogeneity: a Bayesian approach |
| title_short | A novel individual-tree mixed model to account for competition and environmental heterogeneity: a Bayesian approach |
| title_sort | novel individual tree mixed model to account for competition and environmental heterogeneity a bayesian approach |
| topic | Competencia Biológica Biological Competition Environmental Heterogeneity Gibbs Sampling Individual-tree Mixed Model Heterogeneidad Ambiental Muestreo de Gibbs Modelo Mixto Arbol Individual |
| url | http://hdl.handle.net/20.500.12123/3380 https://link.springer.com/article/10.1007/s11295-015-0917-3#citeas https://doi.org/10.1007/s11295-015-0917-3 |
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