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Applications of machine learning methods to genomic selection in breeding wheat for rust resistance

Author: González-Camacho, J.M
Author: Ornella, L.
Author: Pérez-Rodríguez, P.
Author: Gianola, D.
Author: Dreisigacker, S.
Author: Crossa, J.
Year: 2018
ISSN: 1940-3372
URI: https://hdl.handle.net/10883/19573
Abstract: New methods and algorithms are being developed for predicting untested phenotypes in schemes commonly used in genomic selection (GS). The prediction of disease resistance in GS has its own peculiarities: a) there is consensus about the additive nature of quantitative adult plant resistance (APR) genes, although epistasis has been found in some populations; b) rust resistance requires effective combinations of major and minor genes; and c) disease resistance is commonly measured based on ordinal scales (e.g., scales from 1?5, 1?9, etc.). Machine learning (ML) is a field of computer science that uses algorithms and existing samples to capture characteristics of target patterns. In this paper we discuss several state-of-the-art ML methods that could be applied in GS. Many of them have already been used to predict rust resistance in wheat. Others are very appealing, given their performance for predicting other wheat traits with similar characteristics. We briefly describe the proposed methods in the Appendix.
Format: PDF
Language: English
Publisher: Crop Science Society of America
Copyright: 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.
Type: Article
Place of Publication: Madison, U.S
Issue: 2
Volume: 11
DOI: 10.3835/plantgenome2017.11.0104
Agrovoc: GENOMIC FEATURES
Agrovoc: BREEDING
Agrovoc: WHEAT
Agrovoc: RUSTS
Agrovoc: PLANT DISEASES
Agrovoc: MACHINE LEARNING
Agrovoc: GENOMIC FEATURES
Agrovoc: BREEDING
Agrovoc: WHEAT
Agrovoc: RUSTS
Agrovoc: PLANT DISEASES
Agrovoc: MACHINE LEARNING
Journal: The Plant Genome


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  • Wheat
    Wheat - breeding, phytopathology, physiology, quality, biotech

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