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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/3863
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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) |
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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1844614338433777664 |
score |
13.070432 |