An assessor-specific Bayesian multi-threshold mixed model for analyzing ordered categorical traits in tree breeding

Autores
Cappa, Eduardo Pablo; Varona, Luis
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Many traits of biological interest in tree breeding are assessed using more than two ordered discrete categories. These scores have a more or less arbitrary and subjective assignment by the assessors, which could lead to a strong departure from the Gaussian distribution. Different assessors may also use different regions of the available scale. This study describes the use of the multi-threshold mixed model proposed by Varona et al. (2009), which allows different thresholds for different assessors on an underlying Gaussian distribution. This method was applied to a six-point score for stem-quality in an open-pollinated progeny trial of Prosopis alba Griseb. Four mixed models were used: 1) a linear model with observed score (LMM); 2) a linear model with transformed "normal scores" (LMM_NS); 3) a threshold model (TMM); and 4) an assessor-specific multi-threshold model (MTMM). Dispersion parameters were estimated using Bayesian techniques via the Gibbs sampling with a data augmentation step. The proposed MTMM produced higher posterior mean heritabilities (0.096) than the commonly used LMM (0.077). Posterior mean heritabilities from LMM_NS (0.094) and TMM (0.097) were comparable to those obtained using MTMM; however, MTMM yielded slightly more precise estimates than TMM. Although correlations of the estimated breeding values were high between different models (from 0.88 to 0.99), the heterogeneity in the estimated posterior means of the thresholds between the three assessors caused notable changes in the top 10 families between TMM and MTMM. The proposed model is helpful in fitting subjective ordered categorical traits assessed by different assessors in tree breeding.
Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria. Centro Nacional de Investigaciones Agropecuarias. Centro de Investigación de Recursos Naturales. Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Varona, Luis. Universidad de Zaragoza. Unidad de Genética Cuantitativa y Mejora Animal; España
Materia
Ordered Categorical Traits
Assessor
Multi-Threshold Mixed Model
Bayesian Inference
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:11336/3863

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spelling An assessor-specific Bayesian multi-threshold mixed model for analyzing ordered categorical traits in tree breedingCappa, Eduardo PabloVarona, LuisOrdered Categorical TraitsAssessorMulti-Threshold Mixed ModelBayesian Inferencehttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1https://purl.org/becyt/ford/4.1https://purl.org/becyt/ford/4Many traits of biological interest in tree breeding are assessed using more than two ordered discrete categories. These scores have a more or less arbitrary and subjective assignment by the assessors, which could lead to a strong departure from the Gaussian distribution. Different assessors may also use different regions of the available scale. This study describes the use of the multi-threshold mixed model proposed by Varona et al. (2009), which allows different thresholds for different assessors on an underlying Gaussian distribution. This method was applied to a six-point score for stem-quality in an open-pollinated progeny trial of Prosopis alba Griseb. Four mixed models were used: 1) a linear model with observed score (LMM); 2) a linear model with transformed "normal scores" (LMM_NS); 3) a threshold model (TMM); and 4) an assessor-specific multi-threshold model (MTMM). Dispersion parameters were estimated using Bayesian techniques via the Gibbs sampling with a data augmentation step. The proposed MTMM produced higher posterior mean heritabilities (0.096) than the commonly used LMM (0.077). Posterior mean heritabilities from LMM_NS (0.094) and TMM (0.097) were comparable to those obtained using MTMM; however, MTMM yielded slightly more precise estimates than TMM. Although correlations of the estimated breeding values were high between different models (from 0.88 to 0.99), the heterogeneity in the estimated posterior means of the thresholds between the three assessors caused notable changes in the top 10 families between TMM and MTMM. The proposed model is helpful in fitting subjective ordered categorical traits assessed by different assessors in tree breeding.Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria. Centro Nacional de Investigaciones Agropecuarias. Centro de Investigación de Recursos Naturales. Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Varona, Luis. Universidad de Zaragoza. Unidad de Genética Cuantitativa y Mejora Animal; EspañaSpringer Heidelberg2013-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/3863Cappa, Eduardo Pablo; Varona, Luis; An assessor-specific Bayesian multi-threshold mixed model for analyzing ordered categorical traits in tree breeding; Springer Heidelberg; Tree Genetics & Genomes; 9; 6; 12-2013; 1423-14341614-2942enginfo:eu-repo/semantics/altIdentifier/url/http://link.springer.com/article/10.1007%2Fs11295-013-0648-2info:eu-repo/semantics/altIdentifier/url/http://dx.doi.org/10.1007/s11295-013-0648-2info:eu-repo/semantics/altIdentifier/issn/1614-2942info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:32:28Zoai:ri.conicet.gov.ar:11336/3863instacron: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 10:32:28.866CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv An assessor-specific Bayesian multi-threshold mixed model for analyzing ordered categorical traits in tree breeding
title An assessor-specific Bayesian multi-threshold mixed model for analyzing ordered categorical traits in tree breeding
spellingShingle An assessor-specific Bayesian multi-threshold mixed model for analyzing ordered categorical traits in tree breeding
Cappa, Eduardo Pablo
Ordered Categorical Traits
Assessor
Multi-Threshold Mixed Model
Bayesian Inference
title_short An assessor-specific Bayesian multi-threshold mixed model for analyzing ordered categorical traits in tree breeding
title_full An assessor-specific Bayesian multi-threshold mixed model for analyzing ordered categorical traits in tree breeding
title_fullStr An assessor-specific Bayesian multi-threshold mixed model for analyzing ordered categorical traits in tree breeding
title_full_unstemmed An assessor-specific Bayesian multi-threshold mixed model for analyzing ordered categorical traits in tree breeding
title_sort An assessor-specific Bayesian multi-threshold mixed model for analyzing ordered categorical traits in tree breeding
dc.creator.none.fl_str_mv Cappa, Eduardo Pablo
Varona, Luis
author Cappa, Eduardo Pablo
author_facet Cappa, Eduardo Pablo
Varona, Luis
author_role author
author2 Varona, Luis
author2_role author
dc.subject.none.fl_str_mv Ordered Categorical Traits
Assessor
Multi-Threshold Mixed Model
Bayesian Inference
topic Ordered Categorical Traits
Assessor
Multi-Threshold Mixed Model
Bayesian Inference
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
dc.description.none.fl_txt_mv Many traits of biological interest in tree breeding are assessed using more than two ordered discrete categories. These scores have a more or less arbitrary and subjective assignment by the assessors, which could lead to a strong departure from the Gaussian distribution. Different assessors may also use different regions of the available scale. This study describes the use of the multi-threshold mixed model proposed by Varona et al. (2009), which allows different thresholds for different assessors on an underlying Gaussian distribution. This method was applied to a six-point score for stem-quality in an open-pollinated progeny trial of Prosopis alba Griseb. Four mixed models were used: 1) a linear model with observed score (LMM); 2) a linear model with transformed "normal scores" (LMM_NS); 3) a threshold model (TMM); and 4) an assessor-specific multi-threshold model (MTMM). Dispersion parameters were estimated using Bayesian techniques via the Gibbs sampling with a data augmentation step. The proposed MTMM produced higher posterior mean heritabilities (0.096) than the commonly used LMM (0.077). Posterior mean heritabilities from LMM_NS (0.094) and TMM (0.097) were comparable to those obtained using MTMM; however, MTMM yielded slightly more precise estimates than TMM. Although correlations of the estimated breeding values were high between different models (from 0.88 to 0.99), the heterogeneity in the estimated posterior means of the thresholds between the three assessors caused notable changes in the top 10 families between TMM and MTMM. The proposed model is helpful in fitting subjective ordered categorical traits assessed by different assessors in tree breeding.
Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria. Centro Nacional de Investigaciones Agropecuarias. Centro de Investigación de Recursos Naturales. Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Varona, Luis. Universidad de Zaragoza. Unidad de Genética Cuantitativa y Mejora Animal; España
description Many traits of biological interest in tree breeding are assessed using more than two ordered discrete categories. These scores have a more or less arbitrary and subjective assignment by the assessors, which could lead to a strong departure from the Gaussian distribution. Different assessors may also use different regions of the available scale. This study describes the use of the multi-threshold mixed model proposed by Varona et al. (2009), which allows different thresholds for different assessors on an underlying Gaussian distribution. This method was applied to a six-point score for stem-quality in an open-pollinated progeny trial of Prosopis alba Griseb. Four mixed models were used: 1) a linear model with observed score (LMM); 2) a linear model with transformed "normal scores" (LMM_NS); 3) a threshold model (TMM); and 4) an assessor-specific multi-threshold model (MTMM). Dispersion parameters were estimated using Bayesian techniques via the Gibbs sampling with a data augmentation step. The proposed MTMM produced higher posterior mean heritabilities (0.096) than the commonly used LMM (0.077). Posterior mean heritabilities from LMM_NS (0.094) and TMM (0.097) were comparable to those obtained using MTMM; however, MTMM yielded slightly more precise estimates than TMM. Although correlations of the estimated breeding values were high between different models (from 0.88 to 0.99), the heterogeneity in the estimated posterior means of the thresholds between the three assessors caused notable changes in the top 10 families between TMM and MTMM. The proposed model is helpful in fitting subjective ordered categorical traits assessed by different assessors in tree breeding.
publishDate 2013
dc.date.none.fl_str_mv 2013-12
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/11336/3863
Cappa, Eduardo Pablo; Varona, Luis; An assessor-specific Bayesian multi-threshold mixed model for analyzing ordered categorical traits in tree breeding; Springer Heidelberg; Tree Genetics & Genomes; 9; 6; 12-2013; 1423-1434
1614-2942
url http://hdl.handle.net/11336/3863
identifier_str_mv Cappa, Eduardo Pablo; Varona, Luis; An assessor-specific Bayesian multi-threshold mixed model for analyzing ordered categorical traits in tree breeding; Springer Heidelberg; Tree Genetics & Genomes; 9; 6; 12-2013; 1423-1434
1614-2942
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://link.springer.com/article/10.1007%2Fs11295-013-0648-2
info:eu-repo/semantics/altIdentifier/url/http://dx.doi.org/10.1007/s11295-013-0648-2
info:eu-repo/semantics/altIdentifier/issn/1614-2942
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 Springer Heidelberg
publisher.none.fl_str_mv Springer Heidelberg
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)
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