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Digitalizing breeding in plants: A new trend of next-generation breeding based on genomic prediction

As the world’s population grows and food needs diversification, the demand for cereals and horticultural crops with beneficial traits increases. In order to meet a variety of demands, suitable cultivars and innovative breeding methods need to be developed. Breeding methods have changed over time fol...

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Autores principales: Jeon, Donghyun, Kang, Yuna, Lee, Solji, Choi, Sehyun, Sung, Yeonjun, Lee, Tae-Ho, Kim, Changsoo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9892199/
https://www.ncbi.nlm.nih.gov/pubmed/36743488
http://dx.doi.org/10.3389/fpls.2023.1092584
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author Jeon, Donghyun
Kang, Yuna
Lee, Solji
Choi, Sehyun
Sung, Yeonjun
Lee, Tae-Ho
Kim, Changsoo
author_facet Jeon, Donghyun
Kang, Yuna
Lee, Solji
Choi, Sehyun
Sung, Yeonjun
Lee, Tae-Ho
Kim, Changsoo
author_sort Jeon, Donghyun
collection PubMed
description As the world’s population grows and food needs diversification, the demand for cereals and horticultural crops with beneficial traits increases. In order to meet a variety of demands, suitable cultivars and innovative breeding methods need to be developed. Breeding methods have changed over time following the advance of genetics. With the advent of new sequencing technology in the early 21st century, predictive breeding, such as genomic selection (GS), emerged when large-scale genomic information became available. GS shows good predictive ability for the selection of individuals with traits of interest even for quantitative traits by using various types of the whole genome-scanning markers, breaking away from the limitations of marker-assisted selection (MAS). In the current review, we briefly describe the history of breeding techniques, each breeding method, various statistical models applied to GS and methods to increase the GS efficiency. Consequently, we intend to propose and define the term digital breeding through this review article. Digital breeding is to develop a predictive breeding methods such as GS at a higher level, aiming to minimize human intervention by automatically proceeding breeding design, propagating breeding populations, and to make selections in consideration of various environments, climates, and topography during the breeding process. We also classified the phases of digital breeding based on the technologies and methods applied to each phase. This review paper will provide an understanding and a direction for the final evolution of plant breeding in the future.
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spelling pubmed-98921992023-02-03 Digitalizing breeding in plants: A new trend of next-generation breeding based on genomic prediction Jeon, Donghyun Kang, Yuna Lee, Solji Choi, Sehyun Sung, Yeonjun Lee, Tae-Ho Kim, Changsoo Front Plant Sci Plant Science As the world’s population grows and food needs diversification, the demand for cereals and horticultural crops with beneficial traits increases. In order to meet a variety of demands, suitable cultivars and innovative breeding methods need to be developed. Breeding methods have changed over time following the advance of genetics. With the advent of new sequencing technology in the early 21st century, predictive breeding, such as genomic selection (GS), emerged when large-scale genomic information became available. GS shows good predictive ability for the selection of individuals with traits of interest even for quantitative traits by using various types of the whole genome-scanning markers, breaking away from the limitations of marker-assisted selection (MAS). In the current review, we briefly describe the history of breeding techniques, each breeding method, various statistical models applied to GS and methods to increase the GS efficiency. Consequently, we intend to propose and define the term digital breeding through this review article. Digital breeding is to develop a predictive breeding methods such as GS at a higher level, aiming to minimize human intervention by automatically proceeding breeding design, propagating breeding populations, and to make selections in consideration of various environments, climates, and topography during the breeding process. We also classified the phases of digital breeding based on the technologies and methods applied to each phase. This review paper will provide an understanding and a direction for the final evolution of plant breeding in the future. Frontiers Media S.A. 2023-01-19 /pmc/articles/PMC9892199/ /pubmed/36743488 http://dx.doi.org/10.3389/fpls.2023.1092584 Text en Copyright © 2023 Jeon, Kang, Lee, Choi, Sung, Lee and Kim https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Jeon, Donghyun
Kang, Yuna
Lee, Solji
Choi, Sehyun
Sung, Yeonjun
Lee, Tae-Ho
Kim, Changsoo
Digitalizing breeding in plants: A new trend of next-generation breeding based on genomic prediction
title Digitalizing breeding in plants: A new trend of next-generation breeding based on genomic prediction
title_full Digitalizing breeding in plants: A new trend of next-generation breeding based on genomic prediction
title_fullStr Digitalizing breeding in plants: A new trend of next-generation breeding based on genomic prediction
title_full_unstemmed Digitalizing breeding in plants: A new trend of next-generation breeding based on genomic prediction
title_short Digitalizing breeding in plants: A new trend of next-generation breeding based on genomic prediction
title_sort digitalizing breeding in plants: a new trend of next-generation breeding based on genomic prediction
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9892199/
https://www.ncbi.nlm.nih.gov/pubmed/36743488
http://dx.doi.org/10.3389/fpls.2023.1092584
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