Genome-based prediction of Bayesian linear and non-linear regression models for ordinal data.
Journal
The plant genome
ISSN: 1940-3372
Titre abrégé: Plant Genome
Pays: United States
ID NLM: 101273919
Informations de publication
Date de publication:
07 2020
07 2020
Historique:
received:
10
01
2020
revised:
21
03
2020
accepted:
28
03
2020
entrez:
5
10
2020
pubmed:
6
10
2020
medline:
26
11
2020
Statut:
ppublish
Résumé
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.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
e20021Informations de copyright
© 2020 The Authors. The Plant Genome published by Wiley Periodicals, LLC on behalf of Crop Science Society of America.
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