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Machine learning assisted dynamic phenotypes and genomic variants help understand the ecotype divergence in rapeseed

Three ecotypes of rapeseed, winter, spring, and semi-winter, have been formed to enable the plant to adapt to different geographic areas. Although several major loci had been found to contribute to the flowering divergence, the genomic footprints and associated dynamic plant architecture in the vege...

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Autores principales: Feng, Hui, Guo, Chaocheng, Li, Zongyi, Gao, Yuan, Zhang, Qinghua, Geng, Zedong, Wang, Jing, Chen, Guoxing, Liu, Kede, Li, Haitao, Yang, Wanneng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9705987/
https://www.ncbi.nlm.nih.gov/pubmed/36457523
http://dx.doi.org/10.3389/fpls.2022.1028779
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author Feng, Hui
Guo, Chaocheng
Li, Zongyi
Gao, Yuan
Zhang, Qinghua
Geng, Zedong
Wang, Jing
Chen, Guoxing
Liu, Kede
Li, Haitao
Yang, Wanneng
author_facet Feng, Hui
Guo, Chaocheng
Li, Zongyi
Gao, Yuan
Zhang, Qinghua
Geng, Zedong
Wang, Jing
Chen, Guoxing
Liu, Kede
Li, Haitao
Yang, Wanneng
author_sort Feng, Hui
collection PubMed
description Three ecotypes of rapeseed, winter, spring, and semi-winter, have been formed to enable the plant to adapt to different geographic areas. Although several major loci had been found to contribute to the flowering divergence, the genomic footprints and associated dynamic plant architecture in the vegetative growth stage underlying the ecotype divergence remain largely unknown in rapeseed. Here, a set of 41 dynamic i-traits and 30 growth-related traits were obtained by high-throughput phenotyping of 171 diverse rapeseed accessions. Large phenotypic variation and high broad-sense heritability were observed for these i-traits across all developmental stages. Of these, 19 i-traits were identified to contribute to the divergence of three ecotypes using random forest model of machine learning approach, and could serve as biomarkers to predict the ecotype. Furthermore, we analyzed genomic variations of the population, QTL information of all dynamic i-traits, and genomic basis of the ecotype differentiation. It was found that 213, 237, and 184 QTLs responsible for the differentiated i-traits overlapped with the signals of ecotype divergence between winter and spring, winter and semi-winter, and spring and semi-winter, respectively. Of which, there were four common divergent regions between winter and spring/semi-winter and the strongest divergent regions between spring and semi-winter were found to overlap with the dynamic QTLs responsible for the differentiated i-traits at multiple growth stages. Our study provides important insights into the divergence of plant architecture in the vegetative growth stage among the three ecotypes, which was contributed to by the genetic differentiation, and might contribute to environmental adaption and yield improvement.
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spelling pubmed-97059872022-11-30 Machine learning assisted dynamic phenotypes and genomic variants help understand the ecotype divergence in rapeseed Feng, Hui Guo, Chaocheng Li, Zongyi Gao, Yuan Zhang, Qinghua Geng, Zedong Wang, Jing Chen, Guoxing Liu, Kede Li, Haitao Yang, Wanneng Front Plant Sci Plant Science Three ecotypes of rapeseed, winter, spring, and semi-winter, have been formed to enable the plant to adapt to different geographic areas. Although several major loci had been found to contribute to the flowering divergence, the genomic footprints and associated dynamic plant architecture in the vegetative growth stage underlying the ecotype divergence remain largely unknown in rapeseed. Here, a set of 41 dynamic i-traits and 30 growth-related traits were obtained by high-throughput phenotyping of 171 diverse rapeseed accessions. Large phenotypic variation and high broad-sense heritability were observed for these i-traits across all developmental stages. Of these, 19 i-traits were identified to contribute to the divergence of three ecotypes using random forest model of machine learning approach, and could serve as biomarkers to predict the ecotype. Furthermore, we analyzed genomic variations of the population, QTL information of all dynamic i-traits, and genomic basis of the ecotype differentiation. It was found that 213, 237, and 184 QTLs responsible for the differentiated i-traits overlapped with the signals of ecotype divergence between winter and spring, winter and semi-winter, and spring and semi-winter, respectively. Of which, there were four common divergent regions between winter and spring/semi-winter and the strongest divergent regions between spring and semi-winter were found to overlap with the dynamic QTLs responsible for the differentiated i-traits at multiple growth stages. Our study provides important insights into the divergence of plant architecture in the vegetative growth stage among the three ecotypes, which was contributed to by the genetic differentiation, and might contribute to environmental adaption and yield improvement. Frontiers Media S.A. 2022-11-15 /pmc/articles/PMC9705987/ /pubmed/36457523 http://dx.doi.org/10.3389/fpls.2022.1028779 Text en Copyright © 2022 Feng, Guo, Li, Gao, Zhang, Geng, Wang, Chen, Liu, Li and Yang 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
Feng, Hui
Guo, Chaocheng
Li, Zongyi
Gao, Yuan
Zhang, Qinghua
Geng, Zedong
Wang, Jing
Chen, Guoxing
Liu, Kede
Li, Haitao
Yang, Wanneng
Machine learning assisted dynamic phenotypes and genomic variants help understand the ecotype divergence in rapeseed
title Machine learning assisted dynamic phenotypes and genomic variants help understand the ecotype divergence in rapeseed
title_full Machine learning assisted dynamic phenotypes and genomic variants help understand the ecotype divergence in rapeseed
title_fullStr Machine learning assisted dynamic phenotypes and genomic variants help understand the ecotype divergence in rapeseed
title_full_unstemmed Machine learning assisted dynamic phenotypes and genomic variants help understand the ecotype divergence in rapeseed
title_short Machine learning assisted dynamic phenotypes and genomic variants help understand the ecotype divergence in rapeseed
title_sort machine learning assisted dynamic phenotypes and genomic variants help understand the ecotype divergence in rapeseed
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9705987/
https://www.ncbi.nlm.nih.gov/pubmed/36457523
http://dx.doi.org/10.3389/fpls.2022.1028779
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