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|a 10.1016/j.jplph.2020.153354
|2 doi
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|a eng
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1 |
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|a Tong, Hao
|e verfasserin
|4 aut
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|a Machine learning approaches for crop improvement
|b Leveraging phenotypic and genotypic big data
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|c 2021
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Completed 27.05.2021
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|a Date Revised 27.05.2021
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Copyright © 2020 The Author(s). Published by Elsevier GmbH.. All rights reserved.
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|a Highly efficient and accurate selection of elite genotypes can lead to dramatic shortening of the breeding cycle in major crops relevant for sustaining present demands for food, feed, and fuel. In contrast to classical approaches that emphasize the need for resource-intensive phenotyping at all stages of artificial selection, genomic selection dramatically reduces the need for phenotyping. Genomic selection relies on advances in machine learning and the availability of genotyping data to predict agronomically relevant phenotypic traits. Here we provide a systematic review of machine learning approaches applied for genomic selection of single and multiple traits in major crops in the past decade. We emphasize the need to gather data on intermediate phenotypes, e.g. metabolite, protein, and gene expression levels, along with developments of modeling techniques that can lead to further improvements of genomic selection. In addition, we provide a critical view of factors that affect genomic selection, with attention to transferability of models between different environments. Finally, we highlight the future aspects of integrating high-throughput molecular phenotypic data from omics technologies with biological networks for crop improvement
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|a Journal Article
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|a Review
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|a Genomic prediction
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| 650 |
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|a Genomic selection
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|a G×E interaction
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| 650 |
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|a Machine learning
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| 650 |
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|a Multi-omics
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| 650 |
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|a Multiple traits
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| 700 |
1 |
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|a Nikoloski, Zoran
|e verfasserin
|4 aut
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|i Enthalten in
|t Journal of plant physiology
|d 1979
|g 257(2021) vom: 01. Feb., Seite 153354
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|x 1618-1328
|7 nnas
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|g volume:257
|g year:2021
|g day:01
|g month:02
|g pages:153354
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|u http://dx.doi.org/10.1016/j.jplph.2020.153354
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