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New Genetic Loci Associated with Preharvest Sprouting and Its Evaluation Based on the Model Equation in Rice
Preharvest sprouting (PHS) in rice panicles is an important quantitative trait that causes both yield losses and the deterioration of grain quality under unpredictable moisture conditions at the ripening stage. However, the molecular mechanism underlying PHS has not yet been elucidated. Here, we exp...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5550670/ https://www.ncbi.nlm.nih.gov/pubmed/28848592 http://dx.doi.org/10.3389/fpls.2017.01393 |
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author | Lee, Gi-An Jeon, Young-Ah Lee, Ho-Sun Hyun, Do Yoon Lee, Jung-Ro Lee, Myung-Chul Lee, Sok-Young Ma, Kyung-Ho Koh, Hee-Jong |
author_facet | Lee, Gi-An Jeon, Young-Ah Lee, Ho-Sun Hyun, Do Yoon Lee, Jung-Ro Lee, Myung-Chul Lee, Sok-Young Ma, Kyung-Ho Koh, Hee-Jong |
author_sort | Lee, Gi-An |
collection | PubMed |
description | Preharvest sprouting (PHS) in rice panicles is an important quantitative trait that causes both yield losses and the deterioration of grain quality under unpredictable moisture conditions at the ripening stage. However, the molecular mechanism underlying PHS has not yet been elucidated. Here, we explored the genetic loci associated with PHS in rice and formulated a model regression equation for rapid screening for use in breeding programs. After re-sequencing 21 representative accessions for PHS and performing enrichment analysis, we found that approximately 20,000 SNPs revealed distinct allelic distributions between PHS resistant and susceptible accessions. Of these, 39 candidate SNP loci were selected, including previously reported QTLs. We analyzed the genotypes of 144 rice accessions to determine the association between PHS and the 39 candidate SNP loci, 10 of which were identified as significantly affecting PHS based on allele type. Based on the allele types of the SNP loci, we constructed a regression equation for evaluating PHS, accounting for an R(2) value of 0.401 in japonica rice. We validated this equation using additional accessions, which exhibited a significant R(2) value of 0.430 between the predicted values and actual measurements. The newly detected SNP loci and the model equation could facilitate marker-assisted selection to predict PHS in rice germplasm and breeding lines. |
format | Online Article Text |
id | pubmed-5550670 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-55506702017-08-28 New Genetic Loci Associated with Preharvest Sprouting and Its Evaluation Based on the Model Equation in Rice Lee, Gi-An Jeon, Young-Ah Lee, Ho-Sun Hyun, Do Yoon Lee, Jung-Ro Lee, Myung-Chul Lee, Sok-Young Ma, Kyung-Ho Koh, Hee-Jong Front Plant Sci Plant Science Preharvest sprouting (PHS) in rice panicles is an important quantitative trait that causes both yield losses and the deterioration of grain quality under unpredictable moisture conditions at the ripening stage. However, the molecular mechanism underlying PHS has not yet been elucidated. Here, we explored the genetic loci associated with PHS in rice and formulated a model regression equation for rapid screening for use in breeding programs. After re-sequencing 21 representative accessions for PHS and performing enrichment analysis, we found that approximately 20,000 SNPs revealed distinct allelic distributions between PHS resistant and susceptible accessions. Of these, 39 candidate SNP loci were selected, including previously reported QTLs. We analyzed the genotypes of 144 rice accessions to determine the association between PHS and the 39 candidate SNP loci, 10 of which were identified as significantly affecting PHS based on allele type. Based on the allele types of the SNP loci, we constructed a regression equation for evaluating PHS, accounting for an R(2) value of 0.401 in japonica rice. We validated this equation using additional accessions, which exhibited a significant R(2) value of 0.430 between the predicted values and actual measurements. The newly detected SNP loci and the model equation could facilitate marker-assisted selection to predict PHS in rice germplasm and breeding lines. Frontiers Media S.A. 2017-08-08 /pmc/articles/PMC5550670/ /pubmed/28848592 http://dx.doi.org/10.3389/fpls.2017.01393 Text en Copyright © 2017 Lee, Jeon, Lee, Hyun, Lee, Lee, Lee, Ma and Koh. http://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) or licensor 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 Lee, Gi-An Jeon, Young-Ah Lee, Ho-Sun Hyun, Do Yoon Lee, Jung-Ro Lee, Myung-Chul Lee, Sok-Young Ma, Kyung-Ho Koh, Hee-Jong New Genetic Loci Associated with Preharvest Sprouting and Its Evaluation Based on the Model Equation in Rice |
title | New Genetic Loci Associated with Preharvest Sprouting and Its Evaluation Based on the Model Equation in Rice |
title_full | New Genetic Loci Associated with Preharvest Sprouting and Its Evaluation Based on the Model Equation in Rice |
title_fullStr | New Genetic Loci Associated with Preharvest Sprouting and Its Evaluation Based on the Model Equation in Rice |
title_full_unstemmed | New Genetic Loci Associated with Preharvest Sprouting and Its Evaluation Based on the Model Equation in Rice |
title_short | New Genetic Loci Associated with Preharvest Sprouting and Its Evaluation Based on the Model Equation in Rice |
title_sort | new genetic loci associated with preharvest sprouting and its evaluation based on the model equation in rice |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5550670/ https://www.ncbi.nlm.nih.gov/pubmed/28848592 http://dx.doi.org/10.3389/fpls.2017.01393 |
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