Bayesian inference for multi-environment spatial individual-tree models with additive and full-sib family genetic effects for large forest genetic trials
- Autores
- Cappa, Eduardo Pablo; Yanchuk, Alvin D.; Cartwright, Charlie V.
- Año de publicación
- 2012
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Context: The gain in accuracy of breeding values with the use of single trial spatial analysis is well known in forestry. However, spatial analyses methodology for single forest genetic trials must be adapted for use with combined analyses of forest genetic trials across sites. Aims: This paper extends a methodology for spatial analysis of single forest genetic trial to a multi-environment trial (MET) setting. Methods: A two-stage spatial MET approach using an individual-tree model with additive and full-sib family genetic effects was developed. Dispersion parameters were estimated using Bayesian techniques via Gibbs sampling. The procedure is illustrated using height growth data at age 10 from eight large Tsuga heterophylla (Raf.) Sarg. second-generation full-sib progeny trials from two series established across seven sites in British Columbia (Canada) and on one in Washington (USA). Results: The proposed multi-environment spatial mixed model displayed a consistent reduction of the posterior mean and an increase in the precision of error variances than the model with Sets in Replicates or incomplete block alpha designs. Also, the multi-environment spatial model provided an average increase in the posterior means of the narrow- and broad-sense individual-tree heritabilities (h2N and h2B, respectively). No consistent changes were observed in the posterior means of additive genetic correlations (rAjj'). Conclusion: Although computationally demanding, all dispersion parameters were successfully estimated from the proposed multi-environment spatial individual-tree model using Bayesian techniques via Gibbs sampling. The proposed two-stage spatial MET approach produced better results than the commonly used non-spatial MET analysis.
Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Yanchuk, Alvin D.. British Columbia Forest Service; Canadá
Fil: Cartwright, Charlie V.. British Columbia Forest Service; Canadá - Materia
-
ADDITIVE GENETIC CORRELATIONS
FULL-SIB FAMILY GENETIC EFFECTS
GIBBS SAMPLING
MODEL COMPARISON
MULTI-ENVIRONMENT SPATIAL MODEL
WESTERN HEMLOCK - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/196030
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oai:ri.conicet.gov.ar:11336/196030 |
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3498 |
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CONICET Digital (CONICET) |
spelling |
Bayesian inference for multi-environment spatial individual-tree models with additive and full-sib family genetic effects for large forest genetic trialsCappa, Eduardo PabloYanchuk, Alvin D.Cartwright, Charlie V.ADDITIVE GENETIC CORRELATIONSFULL-SIB FAMILY GENETIC EFFECTSGIBBS SAMPLINGMODEL COMPARISONMULTI-ENVIRONMENT SPATIAL MODELWESTERN HEMLOCKhttps://purl.org/becyt/ford/4.5https://purl.org/becyt/ford/4Context: The gain in accuracy of breeding values with the use of single trial spatial analysis is well known in forestry. However, spatial analyses methodology for single forest genetic trials must be adapted for use with combined analyses of forest genetic trials across sites. Aims: This paper extends a methodology for spatial analysis of single forest genetic trial to a multi-environment trial (MET) setting. Methods: A two-stage spatial MET approach using an individual-tree model with additive and full-sib family genetic effects was developed. Dispersion parameters were estimated using Bayesian techniques via Gibbs sampling. The procedure is illustrated using height growth data at age 10 from eight large Tsuga heterophylla (Raf.) Sarg. second-generation full-sib progeny trials from two series established across seven sites in British Columbia (Canada) and on one in Washington (USA). Results: The proposed multi-environment spatial mixed model displayed a consistent reduction of the posterior mean and an increase in the precision of error variances than the model with Sets in Replicates or incomplete block alpha designs. Also, the multi-environment spatial model provided an average increase in the posterior means of the narrow- and broad-sense individual-tree heritabilities (h2N and h2B, respectively). No consistent changes were observed in the posterior means of additive genetic correlations (rAjj'). Conclusion: Although computationally demanding, all dispersion parameters were successfully estimated from the proposed multi-environment spatial individual-tree model using Bayesian techniques via Gibbs sampling. The proposed two-stage spatial MET approach produced better results than the commonly used non-spatial MET analysis.Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Yanchuk, Alvin D.. British Columbia Forest Service; CanadáFil: Cartwright, Charlie V.. British Columbia Forest Service; CanadáEDP Sciences2012-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/196030Cappa, Eduardo Pablo; Yanchuk, Alvin D.; Cartwright, Charlie V.; Bayesian inference for multi-environment spatial individual-tree models with additive and full-sib family genetic effects for large forest genetic trials; EDP Sciences; Annals of Forest Science; 69; 5; 7-2012; 627-6401286-4560CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s13595-011-0179-7info:eu-repo/semantics/altIdentifier/doi/10.1007/s13595-011-0179-7info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:45:38Zoai:ri.conicet.gov.ar:11336/196030instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:45:39.276CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Bayesian inference for multi-environment spatial individual-tree models with additive and full-sib family genetic effects for large forest genetic trials |
title |
Bayesian inference for multi-environment spatial individual-tree models with additive and full-sib family genetic effects for large forest genetic trials |
spellingShingle |
Bayesian inference for multi-environment spatial individual-tree models with additive and full-sib family genetic effects for large forest genetic trials Cappa, Eduardo Pablo ADDITIVE GENETIC CORRELATIONS FULL-SIB FAMILY GENETIC EFFECTS GIBBS SAMPLING MODEL COMPARISON MULTI-ENVIRONMENT SPATIAL MODEL WESTERN HEMLOCK |
title_short |
Bayesian inference for multi-environment spatial individual-tree models with additive and full-sib family genetic effects for large forest genetic trials |
title_full |
Bayesian inference for multi-environment spatial individual-tree models with additive and full-sib family genetic effects for large forest genetic trials |
title_fullStr |
Bayesian inference for multi-environment spatial individual-tree models with additive and full-sib family genetic effects for large forest genetic trials |
title_full_unstemmed |
Bayesian inference for multi-environment spatial individual-tree models with additive and full-sib family genetic effects for large forest genetic trials |
title_sort |
Bayesian inference for multi-environment spatial individual-tree models with additive and full-sib family genetic effects for large forest genetic trials |
dc.creator.none.fl_str_mv |
Cappa, Eduardo Pablo Yanchuk, Alvin D. Cartwright, Charlie V. |
author |
Cappa, Eduardo Pablo |
author_facet |
Cappa, Eduardo Pablo Yanchuk, Alvin D. Cartwright, Charlie V. |
author_role |
author |
author2 |
Yanchuk, Alvin D. Cartwright, Charlie V. |
author2_role |
author author |
dc.subject.none.fl_str_mv |
ADDITIVE GENETIC CORRELATIONS FULL-SIB FAMILY GENETIC EFFECTS GIBBS SAMPLING MODEL COMPARISON MULTI-ENVIRONMENT SPATIAL MODEL WESTERN HEMLOCK |
topic |
ADDITIVE GENETIC CORRELATIONS FULL-SIB FAMILY GENETIC EFFECTS GIBBS SAMPLING MODEL COMPARISON MULTI-ENVIRONMENT SPATIAL MODEL WESTERN HEMLOCK |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/4.5 https://purl.org/becyt/ford/4 |
dc.description.none.fl_txt_mv |
Context: The gain in accuracy of breeding values with the use of single trial spatial analysis is well known in forestry. However, spatial analyses methodology for single forest genetic trials must be adapted for use with combined analyses of forest genetic trials across sites. Aims: This paper extends a methodology for spatial analysis of single forest genetic trial to a multi-environment trial (MET) setting. Methods: A two-stage spatial MET approach using an individual-tree model with additive and full-sib family genetic effects was developed. Dispersion parameters were estimated using Bayesian techniques via Gibbs sampling. The procedure is illustrated using height growth data at age 10 from eight large Tsuga heterophylla (Raf.) Sarg. second-generation full-sib progeny trials from two series established across seven sites in British Columbia (Canada) and on one in Washington (USA). Results: The proposed multi-environment spatial mixed model displayed a consistent reduction of the posterior mean and an increase in the precision of error variances than the model with Sets in Replicates or incomplete block alpha designs. Also, the multi-environment spatial model provided an average increase in the posterior means of the narrow- and broad-sense individual-tree heritabilities (h2N and h2B, respectively). No consistent changes were observed in the posterior means of additive genetic correlations (rAjj'). Conclusion: Although computationally demanding, all dispersion parameters were successfully estimated from the proposed multi-environment spatial individual-tree model using Bayesian techniques via Gibbs sampling. The proposed two-stage spatial MET approach produced better results than the commonly used non-spatial MET analysis. Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Yanchuk, Alvin D.. British Columbia Forest Service; Canadá Fil: Cartwright, Charlie V.. British Columbia Forest Service; Canadá |
description |
Context: The gain in accuracy of breeding values with the use of single trial spatial analysis is well known in forestry. However, spatial analyses methodology for single forest genetic trials must be adapted for use with combined analyses of forest genetic trials across sites. Aims: This paper extends a methodology for spatial analysis of single forest genetic trial to a multi-environment trial (MET) setting. Methods: A two-stage spatial MET approach using an individual-tree model with additive and full-sib family genetic effects was developed. Dispersion parameters were estimated using Bayesian techniques via Gibbs sampling. The procedure is illustrated using height growth data at age 10 from eight large Tsuga heterophylla (Raf.) Sarg. second-generation full-sib progeny trials from two series established across seven sites in British Columbia (Canada) and on one in Washington (USA). Results: The proposed multi-environment spatial mixed model displayed a consistent reduction of the posterior mean and an increase in the precision of error variances than the model with Sets in Replicates or incomplete block alpha designs. Also, the multi-environment spatial model provided an average increase in the posterior means of the narrow- and broad-sense individual-tree heritabilities (h2N and h2B, respectively). No consistent changes were observed in the posterior means of additive genetic correlations (rAjj'). Conclusion: Although computationally demanding, all dispersion parameters were successfully estimated from the proposed multi-environment spatial individual-tree model using Bayesian techniques via Gibbs sampling. The proposed two-stage spatial MET approach produced better results than the commonly used non-spatial MET analysis. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-07 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/196030 Cappa, Eduardo Pablo; Yanchuk, Alvin D.; Cartwright, Charlie V.; Bayesian inference for multi-environment spatial individual-tree models with additive and full-sib family genetic effects for large forest genetic trials; EDP Sciences; Annals of Forest Science; 69; 5; 7-2012; 627-640 1286-4560 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/196030 |
identifier_str_mv |
Cappa, Eduardo Pablo; Yanchuk, Alvin D.; Cartwright, Charlie V.; Bayesian inference for multi-environment spatial individual-tree models with additive and full-sib family genetic effects for large forest genetic trials; EDP Sciences; Annals of Forest Science; 69; 5; 7-2012; 627-640 1286-4560 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s13595-011-0179-7 info:eu-repo/semantics/altIdentifier/doi/10.1007/s13595-011-0179-7 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
EDP Sciences |
publisher.none.fl_str_mv |
EDP Sciences |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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1844613429502935040 |
score |
13.070432 |