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
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/196030

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network_name_str 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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