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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...

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Detalles Bibliográficos
Autores principales: Narawatthana, Supatthra, Phansenee, Yotwarit, Thammasamisorn, Bang-On, Vejchasarn, Phanchita
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
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
Descripción
Sumario: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.