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Genome-wide association study and Mendelian randomization analysis provide insights for improving rice yield potential
Rice yield per plant has a complex genetic architecture, which is mainly determined by its three component traits: the number of grains per panicle (GPP), kilo-grain weight (KGW), and tillers per plant (TP). Exploring ideotype breeding based on selection for genetically less complex component traits...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7994632/ https://www.ncbi.nlm.nih.gov/pubmed/33767346 http://dx.doi.org/10.1038/s41598-021-86389-7 |
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author | Su, Jing Xu, Kai Li, Zirong Hu, Yuan Hu, Zhongli Zheng, Xingfei Song, Shufeng Tang, Zhonghai Li, Lanzhi |
author_facet | Su, Jing Xu, Kai Li, Zirong Hu, Yuan Hu, Zhongli Zheng, Xingfei Song, Shufeng Tang, Zhonghai Li, Lanzhi |
author_sort | Su, Jing |
collection | PubMed |
description | Rice yield per plant has a complex genetic architecture, which is mainly determined by its three component traits: the number of grains per panicle (GPP), kilo-grain weight (KGW), and tillers per plant (TP). Exploring ideotype breeding based on selection for genetically less complex component traits is an alternative route for further improving rice production. To understand the genetic basis of the relationship between rice yield and component traits, we investigated the four traits of two rice hybrid populations (575 + 1495 F(1)) in different environments and conducted meta-analyses of genome-wide association study (meta-GWAS). In total, 3589 significant loci for three components traits were detected, while only 3 loci for yield were detected. It indicated that rice yield is mainly controlled by minor-effect loci and hardly to be identified. Selecting quantitative trait locus/gene affected component traits to further enhance yield is recommended. Mendelian randomization design is adopted to investigate the genetic effects of loci on yield through component traits and estimate the genetic relationship between rice yield and its component traits by these loci. The loci for GPP or TP mainly had a positive genetic effect on yield, but the loci for KGW with different direction effects (positive effect or negative effect). Additionally, TP (Beta = 1.865) has a greater effect on yield than KGW (Beta = 1.016) and GPP (Beta = 0.086). Five significant loci for component traits that had an indirect effect on yield were identified. Pyramiding superior alleles of the five loci revealed improved yield. A combination of direct and indirect effects may better contribute to the yield potential of rice. Our findings provided a rationale for using component traits as indirect indices to enhanced rice yield, which will be helpful for further understanding the genetic basis of yield and provide valuable information for improving rice yield potential. |
format | Online Article Text |
id | pubmed-7994632 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-79946322021-03-29 Genome-wide association study and Mendelian randomization analysis provide insights for improving rice yield potential Su, Jing Xu, Kai Li, Zirong Hu, Yuan Hu, Zhongli Zheng, Xingfei Song, Shufeng Tang, Zhonghai Li, Lanzhi Sci Rep Article Rice yield per plant has a complex genetic architecture, which is mainly determined by its three component traits: the number of grains per panicle (GPP), kilo-grain weight (KGW), and tillers per plant (TP). Exploring ideotype breeding based on selection for genetically less complex component traits is an alternative route for further improving rice production. To understand the genetic basis of the relationship between rice yield and component traits, we investigated the four traits of two rice hybrid populations (575 + 1495 F(1)) in different environments and conducted meta-analyses of genome-wide association study (meta-GWAS). In total, 3589 significant loci for three components traits were detected, while only 3 loci for yield were detected. It indicated that rice yield is mainly controlled by minor-effect loci and hardly to be identified. Selecting quantitative trait locus/gene affected component traits to further enhance yield is recommended. Mendelian randomization design is adopted to investigate the genetic effects of loci on yield through component traits and estimate the genetic relationship between rice yield and its component traits by these loci. The loci for GPP or TP mainly had a positive genetic effect on yield, but the loci for KGW with different direction effects (positive effect or negative effect). Additionally, TP (Beta = 1.865) has a greater effect on yield than KGW (Beta = 1.016) and GPP (Beta = 0.086). Five significant loci for component traits that had an indirect effect on yield were identified. Pyramiding superior alleles of the five loci revealed improved yield. A combination of direct and indirect effects may better contribute to the yield potential of rice. Our findings provided a rationale for using component traits as indirect indices to enhanced rice yield, which will be helpful for further understanding the genetic basis of yield and provide valuable information for improving rice yield potential. Nature Publishing Group UK 2021-03-25 /pmc/articles/PMC7994632/ /pubmed/33767346 http://dx.doi.org/10.1038/s41598-021-86389-7 Text en © The Author(s) 2021 Open Access This 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/. |
spellingShingle | Article Su, Jing Xu, Kai Li, Zirong Hu, Yuan Hu, Zhongli Zheng, Xingfei Song, Shufeng Tang, Zhonghai Li, Lanzhi Genome-wide association study and Mendelian randomization analysis provide insights for improving rice yield potential |
title | Genome-wide association study and Mendelian randomization analysis provide insights for improving rice yield potential |
title_full | Genome-wide association study and Mendelian randomization analysis provide insights for improving rice yield potential |
title_fullStr | Genome-wide association study and Mendelian randomization analysis provide insights for improving rice yield potential |
title_full_unstemmed | Genome-wide association study and Mendelian randomization analysis provide insights for improving rice yield potential |
title_short | Genome-wide association study and Mendelian randomization analysis provide insights for improving rice yield potential |
title_sort | genome-wide association study and mendelian randomization analysis provide insights for improving rice yield potential |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7994632/ https://www.ncbi.nlm.nih.gov/pubmed/33767346 http://dx.doi.org/10.1038/s41598-021-86389-7 |
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