Genome-based prediction of Bayesian linear and non-linear regression models for ordinal data
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.
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Crop Science Society of America
2020
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| Materias: | MODELS, MARKER-ASSISTED SELECTION, BAYESIAN THEORY, |
| Acceso en línea: | https://hdl.handle.net/10883/20942 |
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dig-cimmyt-10883-209422023-07-11T17:06:06Z Genome-based prediction of Bayesian linear and non-linear regression models for ordinal data Perez-Rodriguez, P. Flores-Galarza, S. Vaquera-Huerta, H. Valle-Paniagua, D.H. del Montesinos-Lopez, O.A. Crossa, J. MODELS MARKER-ASSISTED SELECTION BAYESIAN THEORY 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. 2020-09-02T00:10:22Z 2020-09-02T00:10:22Z 2020 Article Published Version 1940-3372 (Print) https://hdl.handle.net/10883/20942 10.1002/tpg2.20021 English http://hdl.handle.net/11529/10254 https://acsess.onlinelibrary.wiley.com/doi/full/10.1002/tpg2.20021#support-information-section CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose. Open Access PDF Madison, WI (USA) Crop Science Society of America 2 art. e20021 13 The Plant Genome |
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| topic |
MODELS MARKER-ASSISTED SELECTION BAYESIAN THEORY MODELS MARKER-ASSISTED SELECTION BAYESIAN THEORY |
| spellingShingle |
MODELS MARKER-ASSISTED SELECTION BAYESIAN THEORY MODELS MARKER-ASSISTED SELECTION BAYESIAN THEORY Perez-Rodriguez, P. Flores-Galarza, S. Vaquera-Huerta, H. Valle-Paniagua, D.H. del Montesinos-Lopez, O.A. Crossa, J. Genome-based prediction of Bayesian linear and non-linear regression models for ordinal data |
| description |
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. |
| format |
Article |
| topic_facet |
MODELS MARKER-ASSISTED SELECTION BAYESIAN THEORY |
| author |
Perez-Rodriguez, P. Flores-Galarza, S. Vaquera-Huerta, H. Valle-Paniagua, D.H. del Montesinos-Lopez, O.A. Crossa, J. |
| author_facet |
Perez-Rodriguez, P. Flores-Galarza, S. Vaquera-Huerta, H. Valle-Paniagua, D.H. del Montesinos-Lopez, O.A. Crossa, J. |
| author_sort |
Perez-Rodriguez, P. |
| title |
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_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_sort |
genome-based prediction of bayesian linear and non-linear regression models for ordinal data |
| publisher |
Crop Science Society of America |
| publishDate |
2020 |
| url |
https://hdl.handle.net/10883/20942 |
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