Genome‐based prediction of Bayesian linear and non‐linear regression models for ordinal data
Abstract Linear and non‐linear models used in applications of genomic selection (GS) can fit different types of responses (e.g., continuous, ordinal, binary). In recent years, several genomic‐enabled prediction models have been developed for predicting complex traits in genomic‐assisted animal and p...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-07-01
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| Series: | The Plant Genome |
| Online Access: | https://doi.org/10.1002/tpg2.20021 |
| _version_ | 1818791099102134272 |
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| author | Paulino Pérez‐Rodríguez Samuel Flores‐Galarza Humberto Vaquera‐Huerta David Hebert del Valle‐Paniagua Osval A. Montesinos‐López José Crossa |
| author_facet | Paulino Pérez‐Rodríguez Samuel Flores‐Galarza Humberto Vaquera‐Huerta David Hebert del Valle‐Paniagua Osval A. Montesinos‐López José Crossa |
| author_sort | Paulino Pérez‐Rodríguez |
| collection | DOAJ |
| description | Abstract Linear and non‐linear models used in applications of genomic selection (GS) can fit different types of responses (e.g., continuous, ordinal, binary). In recent years, several genomic‐enabled prediction models have been developed for predicting complex traits in genomic‐assisted animal and plant breeding. These models include linear, non‐linear and non‐parametric models, mostly for continuous responses and less frequently for categorical responses. Several linear and non‐linear models are special cases of a more general family of statistical models known as artificial neural networks, which provide better prediction ability than other models. In this paper, we propose a Bayesian Regularized Neural Network (BRNNO) for modelling ordinal data. The proposed model was fitted using a Bayesian framework; we used the data augmentation algorithm to facilitate computations. The proposed model was fitted using the Gibbs Maximum a Posteriori and Generalized EM algorithm implemented by combining code written in C and R programming languages. The new model was tested with two real maize datasets evaluated for Septoria and GLS diseases and was compared with the Bayesian Ordered Probit Model (BOPM). Results indicated that the BRNNO model performed better in terms of genomic‐based prediction than the BOPM model. |
| first_indexed | 2024-12-18T15:05:58Z |
| format | Article |
| id | doaj.art-f191b33a871c4449a55b526cf25e467c |
| institution | Directory Open Access Journal |
| issn | 1940-3372 |
| language | English |
| last_indexed | 2024-12-18T15:05:58Z |
| publishDate | 2020-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Plant Genome |
| spelling | doaj.art-f191b33a871c4449a55b526cf25e467c2022-12-21T21:03:47ZengWileyThe Plant Genome1940-33722020-07-01132n/an/a10.1002/tpg2.20021Genome‐based prediction of Bayesian linear and non‐linear regression models for ordinal dataPaulino Pérez‐Rodríguez0Samuel Flores‐Galarza1Humberto Vaquera‐Huerta2David Hebert del Valle‐Paniagua3Osval A. Montesinos‐López4José Crossa5Colegio de Postgraduados CP 56230, Montecillos, Edo. de MéxicoColegio de Postgraduados CP 56230, Montecillos, Edo. de MéxicoColegio de Postgraduados CP 56230, Montecillos, Edo. de MéxicoColegio de Postgraduados CP 56230, Montecillos, Edo. de MéxicoFacultad de Telemática, Universidad de Colima Colima 28040 MéxicoColegio de Postgraduados CP 56230, Montecillos, Edo. de MéxicoAbstract Linear and non‐linear models used in applications of genomic selection (GS) can fit different types of responses (e.g., continuous, ordinal, binary). In recent years, several genomic‐enabled prediction models have been developed for predicting complex traits in genomic‐assisted animal and plant breeding. These models include linear, non‐linear and non‐parametric models, mostly for continuous responses and less frequently for categorical responses. Several linear and non‐linear models are special cases of a more general family of statistical models known as artificial neural networks, which provide better prediction ability than other models. In this paper, we propose a Bayesian Regularized Neural Network (BRNNO) for modelling ordinal data. The proposed model was fitted using a Bayesian framework; we used the data augmentation algorithm to facilitate computations. The proposed model was fitted using the Gibbs Maximum a Posteriori and Generalized EM algorithm implemented by combining code written in C and R programming languages. The new model was tested with two real maize datasets evaluated for Septoria and GLS diseases and was compared with the Bayesian Ordered Probit Model (BOPM). Results indicated that the BRNNO model performed better in terms of genomic‐based prediction than the BOPM model.https://doi.org/10.1002/tpg2.20021 |
| spellingShingle | Paulino Pérez‐Rodríguez Samuel Flores‐Galarza Humberto Vaquera‐Huerta David Hebert del Valle‐Paniagua Osval A. Montesinos‐López José Crossa Genome‐based prediction of Bayesian linear and non‐linear regression models for ordinal data The Plant Genome |
| title | Genome‐based prediction of Bayesian linear and non‐linear regression models for ordinal data |
| title_full | Genome‐based prediction of Bayesian linear and non‐linear regression models for ordinal data |
| title_fullStr | Genome‐based prediction of Bayesian linear and non‐linear regression models for ordinal data |
| title_full_unstemmed | Genome‐based prediction of Bayesian linear and non‐linear regression models for ordinal data |
| title_short | Genome‐based prediction of Bayesian linear and non‐linear regression models for ordinal data |
| title_sort | genome based prediction of bayesian linear and non linear regression models for ordinal data |
| url | https://doi.org/10.1002/tpg2.20021 |
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