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Genetic analysis of rice seedling traits related to machine transplanting under different seeding densities

BACKGROUND: Due to the diversity of rice varieties and cropping systems in China, the limitation of seeding density and seedling quality makes it hard to improve machine-transplanted efficiency. Previous studies have shown that indica and japonica varieties varied in machine transplanting efficiency...

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Autores principales: Zhu, Dan, Zhang, Yuping, Xiang, Jing, Wang, Yaliang, Zhu, Defeng, Zhang, Yikai, Chen, Huizhe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7690112/
https://www.ncbi.nlm.nih.gov/pubmed/33243137
http://dx.doi.org/10.1186/s12863-020-00952-1
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author Zhu, Dan
Zhang, Yuping
Xiang, Jing
Wang, Yaliang
Zhu, Defeng
Zhang, Yikai
Chen, Huizhe
author_facet Zhu, Dan
Zhang, Yuping
Xiang, Jing
Wang, Yaliang
Zhu, Defeng
Zhang, Yikai
Chen, Huizhe
author_sort Zhu, Dan
collection PubMed
description BACKGROUND: Due to the diversity of rice varieties and cropping systems in China, the limitation of seeding density and seedling quality makes it hard to improve machine-transplanted efficiency. Previous studies have shown that indica and japonica varieties varied in machine transplanting efficiency and optimal seeding density. In this study, a RIL population derived from ‘9311’ and ‘Nipponbare’ were performed to explore the seedling traits variations and the genetic mechanism under three seeding densities. RESULTS: The parents and RIL population exhibited similar trends as the seeding density increased, including seedling height and first leaf sheath length increases, shoot dry weight and root dry weight decreases. Among the 37 QTLs for six traits detected under the three seeding densities, 12 QTLs were detected in both three seeding densities. Five QTL hotspots identified clustered within genomic regions on chromosomes 1, 2, 4, 6 and 11. Specific QTLs such as qRDW(1.1) and qFLSL(5.1) were detected under low and high seeding densities, respectively. Detailed analysis the QTL regions identified under specific seeding densities revealed several candidate genes involved in phytohormones signals and abiotic stress responses. Whole-genome additive effects showed that ‘9311’ contributed more loci enhancing trait performances than ‘Nipponbare’, indicating ‘9311’ was more sensitive to the seeding density than ‘Nipponbare’. The prevalence of negative epistasis effects indicated that the complementary two-locus homozygotes may not have marginal advantages over the means of the two parental genotypes. CONCLUSIONS: Our results revealed the differences between indica rice and japonica rice seedling traits in response to seeding density. Several QTL hotspots involved in different traits and specific QTLs (such as qRDW(1.1) and qFLSL(5.1)) in diverse seeding densities had been detected. Genome-wide additive and two-locus epistasis suggested a dynamic of the genetic control underlying different seeding densities. It was concluded that novel QTLs, additive and epistasis effects under specific seeding density would provide adequate information for rice seedling improvement during machine transplanting. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12863-020-00952-1.
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spelling pubmed-76901122020-11-30 Genetic analysis of rice seedling traits related to machine transplanting under different seeding densities Zhu, Dan Zhang, Yuping Xiang, Jing Wang, Yaliang Zhu, Defeng Zhang, Yikai Chen, Huizhe BMC Genet Research Article BACKGROUND: Due to the diversity of rice varieties and cropping systems in China, the limitation of seeding density and seedling quality makes it hard to improve machine-transplanted efficiency. Previous studies have shown that indica and japonica varieties varied in machine transplanting efficiency and optimal seeding density. In this study, a RIL population derived from ‘9311’ and ‘Nipponbare’ were performed to explore the seedling traits variations and the genetic mechanism under three seeding densities. RESULTS: The parents and RIL population exhibited similar trends as the seeding density increased, including seedling height and first leaf sheath length increases, shoot dry weight and root dry weight decreases. Among the 37 QTLs for six traits detected under the three seeding densities, 12 QTLs were detected in both three seeding densities. Five QTL hotspots identified clustered within genomic regions on chromosomes 1, 2, 4, 6 and 11. Specific QTLs such as qRDW(1.1) and qFLSL(5.1) were detected under low and high seeding densities, respectively. Detailed analysis the QTL regions identified under specific seeding densities revealed several candidate genes involved in phytohormones signals and abiotic stress responses. Whole-genome additive effects showed that ‘9311’ contributed more loci enhancing trait performances than ‘Nipponbare’, indicating ‘9311’ was more sensitive to the seeding density than ‘Nipponbare’. The prevalence of negative epistasis effects indicated that the complementary two-locus homozygotes may not have marginal advantages over the means of the two parental genotypes. CONCLUSIONS: Our results revealed the differences between indica rice and japonica rice seedling traits in response to seeding density. Several QTL hotspots involved in different traits and specific QTLs (such as qRDW(1.1) and qFLSL(5.1)) in diverse seeding densities had been detected. Genome-wide additive and two-locus epistasis suggested a dynamic of the genetic control underlying different seeding densities. It was concluded that novel QTLs, additive and epistasis effects under specific seeding density would provide adequate information for rice seedling improvement during machine transplanting. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12863-020-00952-1. BioMed Central 2020-11-26 /pmc/articles/PMC7690112/ /pubmed/33243137 http://dx.doi.org/10.1186/s12863-020-00952-1 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Zhu, Dan
Zhang, Yuping
Xiang, Jing
Wang, Yaliang
Zhu, Defeng
Zhang, Yikai
Chen, Huizhe
Genetic analysis of rice seedling traits related to machine transplanting under different seeding densities
title Genetic analysis of rice seedling traits related to machine transplanting under different seeding densities
title_full Genetic analysis of rice seedling traits related to machine transplanting under different seeding densities
title_fullStr Genetic analysis of rice seedling traits related to machine transplanting under different seeding densities
title_full_unstemmed Genetic analysis of rice seedling traits related to machine transplanting under different seeding densities
title_short Genetic analysis of rice seedling traits related to machine transplanting under different seeding densities
title_sort genetic analysis of rice seedling traits related to machine transplanting under different seeding densities
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7690112/
https://www.ncbi.nlm.nih.gov/pubmed/33243137
http://dx.doi.org/10.1186/s12863-020-00952-1
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