An assessor-specific Bayesian multi-threshold mixed model for analyzing ordered categorical traits in tree breeding
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...
| Autores principales: | , |
|---|---|
| Formato: | info:ar-repo/semantics/artículo |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2018
|
| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12123/3779 https://link.springer.com/article/10.1007/s11295-013-0648-2#citeas https://doi.org/10.1007/s11295-013-0648-2 |
| _version_ | 1855035202047311872 |
|---|---|
| author | Cappa, Eduardo Pablo Varona, Luis |
| author_browse | Cappa, Eduardo Pablo Varona, Luis |
| author_facet | Cappa, Eduardo Pablo Varona, Luis |
| author_sort | Cappa, Eduardo Pablo |
| collection | INTA Digital |
| 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. (J Anim Sci 87:1210–1217, 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 mixed model with observed score (LMM); (2) a linear mixed model with transformed “normal scores” (LMM_NS); (3) a threshold mixed model (TMM); and (4) an assessor-specific multi-threshold mixed 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. |
| format | info:ar-repo/semantics/artículo |
| id | INTA3779 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | INTA37792019-06-13T12:07:26Z An assessor-specific Bayesian multi-threshold mixed model for analyzing ordered categorical traits in tree breeding Cappa, Eduardo Pablo Varona, Luis Bayesian Theory Tree Crops Teoría de Bayes Cultivos Leñosos Ordered Categorical Traits Multi-threshold Mixed Model Rasgos Categóricos Ordenados 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. (J Anim Sci 87:1210–1217, 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 mixed model with observed score (LMM); (2) a linear mixed model with transformed “normal scores” (LMM_NS); (3) a threshold mixed model (TMM); and (4) an assessor-specific multi-threshold mixed 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. 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: Varona, Luis. Universidad de Zaragoza. Unidad de Genética Cuantitativa y Mejora Animal; España 2018-11-05T12:39:49Z 2018-11-05T12:39:49Z 2013-12 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/3779 https://link.springer.com/article/10.1007/s11295-013-0648-2#citeas 1614-2942 1614-2950 (Online) https://doi.org/10.1007/s11295-013-0648-2 eng info:eu-repo/semantics/restrictedAccess application/pdf Springer Tree genetics & genomes 9 (6) : 1423–1434. (December 2013) |
| spellingShingle | Bayesian Theory Tree Crops Teoría de Bayes Cultivos Leñosos Ordered Categorical Traits Multi-threshold Mixed Model Rasgos Categóricos Ordenados Cappa, Eduardo Pablo Varona, Luis 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 |
| 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_short | An assessor-specific Bayesian multi-threshold mixed model for analyzing ordered categorical traits in tree breeding |
| title_sort | assessor specific bayesian multi threshold mixed model for analyzing ordered categorical traits in tree breeding |
| topic | Bayesian Theory Tree Crops Teoría de Bayes Cultivos Leñosos Ordered Categorical Traits Multi-threshold Mixed Model Rasgos Categóricos Ordenados |
| url | http://hdl.handle.net/20.500.12123/3779 https://link.springer.com/article/10.1007/s11295-013-0648-2#citeas https://doi.org/10.1007/s11295-013-0648-2 |
| work_keys_str_mv | AT cappaeduardopablo anassessorspecificbayesianmultithresholdmixedmodelforanalyzingorderedcategoricaltraitsintreebreeding AT varonaluis anassessorspecificbayesianmultithresholdmixedmodelforanalyzingorderedcategoricaltraitsintreebreeding AT cappaeduardopablo assessorspecificbayesianmultithresholdmixedmodelforanalyzingorderedcategoricaltraitsintreebreeding AT varonaluis assessorspecificbayesianmultithresholdmixedmodelforanalyzingorderedcategoricaltraitsintreebreeding |