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Multi-model genome-wide association studies of leaf anatomical traits and vein architecture in rice
INTRODUCTION: The anatomy of rice leaves is closely related to photosynthesis and grain yield. Therefore, exploring insight into the quantitative trait loci (QTLs) and alleles related to rice flag leaf anatomical and vein traits is vital for rice improvement. METHODS: Here, we aimed to explore the g...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10130391/ https://www.ncbi.nlm.nih.gov/pubmed/37123816 http://dx.doi.org/10.3389/fpls.2023.1107718 |
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author | Narawatthana, Supatthra Phansenee, Yotwarit Thammasamisorn, Bang-On Vejchasarn, Phanchita |
author_facet | Narawatthana, Supatthra Phansenee, Yotwarit Thammasamisorn, Bang-On Vejchasarn, Phanchita |
author_sort | Narawatthana, Supatthra |
collection | PubMed |
description | INTRODUCTION: The anatomy of rice leaves is closely related to photosynthesis and grain yield. Therefore, exploring insight into the quantitative trait loci (QTLs) and alleles related to rice flag leaf anatomical and vein traits is vital for rice improvement. METHODS: Here, we aimed to explore the genetic architecture of eight flag leaf traits using one single-locus model; mixed-linear model (MLM), and two multi-locus models; fixed and random model circulating probability unification (FarmCPU) and Bayesian information and linkage disequilibrium iteratively nested keyway (BLINK). We performed multi-model GWAS using 329 rice accessions of RDP1 with 700K single-nucleotide polymorphisms (SNPs) markers. RESULTS: The phenotypic correlation results indicated that rice flag leaf thickness was strongly correlated with leaf mesophyll cells layer (ML) and thickness of both major and minor veins. All three models were able to identify several significant loci associated with the traits. MLM identified three non-synonymous SNPs near NARROW LEAF 1 (NAL1) in association with ML and the distance between minor veins (IVD) traits. DISCUSSION: Several numbers of significant SNPs associated with known gene function in leaf development and yield traits were detected by multi-model GWAS performed in this study. Our findings indicate that flag leaf traits could be improved via molecular breeding and can be one of the targets in high-yield rice development. |
format | Online Article Text |
id | pubmed-10130391 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101303912023-04-27 Multi-model genome-wide association studies of leaf anatomical traits and vein architecture in rice Narawatthana, Supatthra Phansenee, Yotwarit Thammasamisorn, Bang-On Vejchasarn, Phanchita Front Plant Sci Plant Science INTRODUCTION: The anatomy of rice leaves is closely related to photosynthesis and grain yield. Therefore, exploring insight into the quantitative trait loci (QTLs) and alleles related to rice flag leaf anatomical and vein traits is vital for rice improvement. METHODS: Here, we aimed to explore the genetic architecture of eight flag leaf traits using one single-locus model; mixed-linear model (MLM), and two multi-locus models; fixed and random model circulating probability unification (FarmCPU) and Bayesian information and linkage disequilibrium iteratively nested keyway (BLINK). We performed multi-model GWAS using 329 rice accessions of RDP1 with 700K single-nucleotide polymorphisms (SNPs) markers. RESULTS: The phenotypic correlation results indicated that rice flag leaf thickness was strongly correlated with leaf mesophyll cells layer (ML) and thickness of both major and minor veins. All three models were able to identify several significant loci associated with the traits. MLM identified three non-synonymous SNPs near NARROW LEAF 1 (NAL1) in association with ML and the distance between minor veins (IVD) traits. DISCUSSION: Several numbers of significant SNPs associated with known gene function in leaf development and yield traits were detected by multi-model GWAS performed in this study. Our findings indicate that flag leaf traits could be improved via molecular breeding and can be one of the targets in high-yield rice development. Frontiers Media S.A. 2023-04-12 /pmc/articles/PMC10130391/ /pubmed/37123816 http://dx.doi.org/10.3389/fpls.2023.1107718 Text en Copyright © 2023 Narawatthana, Phansenee, Thammasamisorn and Vejchasarn 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 Narawatthana, Supatthra Phansenee, Yotwarit Thammasamisorn, Bang-On Vejchasarn, Phanchita Multi-model genome-wide association studies of leaf anatomical traits and vein architecture in rice |
title | Multi-model genome-wide association studies of leaf anatomical traits and vein architecture in rice |
title_full | Multi-model genome-wide association studies of leaf anatomical traits and vein architecture in rice |
title_fullStr | Multi-model genome-wide association studies of leaf anatomical traits and vein architecture in rice |
title_full_unstemmed | Multi-model genome-wide association studies of leaf anatomical traits and vein architecture in rice |
title_short | Multi-model genome-wide association studies of leaf anatomical traits and vein architecture in rice |
title_sort | multi-model genome-wide association studies of leaf anatomical traits and vein architecture in rice |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10130391/ https://www.ncbi.nlm.nih.gov/pubmed/37123816 http://dx.doi.org/10.3389/fpls.2023.1107718 |
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