Major QTLs, qARO1 and qARO9, Additively Regulate Adaxial Leaf Rolling in Rice

Jang, Su; Shim, Sangrea; Lee, Yoon Kyung; Lee, Dongryung; Koh, Hee‐Jong

doi:10.3389/fpls.2021.626523

Cited by 3 publications

(3 citation statements)

References 35 publications

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“…The Fluidigm SNP genotyping system has automated polymerase chain reaction (PCR) and integrated fluidic circuit (IFC) technology, which automatically mixes PCR reagents through microfluidic channel networks. The indica-japonica SNP assays, based on the Fluidigm system developed in the previous study [16], have been applied to various genetic analyses and molecular breeding, such as bulked segregant analysis (BSA) [17], genetic diversity analysis [18,19], QTL analysis [20][21][22][23], and background profiling [24][25][26]. This Fluidigm SNP marker set has provided a faster and more cost-effective tool than other high-throughput SNP genotyping systems for primary analysis during molecular breeding using inter-subspecific populations, to date.…”

Section: Introductionmentioning

confidence: 99%

QTL Analysis of Rice Grain Size Using Segregating Populations Derived from the Large Grain Line

et al. 2021

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Grain size affects the yield and quality of rice. The large grain line (LGL), showing a large grain size and japonica-like genome, was selected in the breeding field. The 94 F2 plants derived from a cross between LGL and Hanareum (a high-yielding tongil-type variety) were used for the quantitative trait loci (QTL) analysis of grain length (GL), grain width (GW), and grain thickness (GT). A linkage map of the F2 population, covering 1312 cM for all 12 chromosomes, was constructed using 123 Fluidigm SNP markers. A total of nine QTLs for the three traits were detected on chromosomes two, three, four, six, and seven. Two QTLs for GL on chromosomes two and six explained 17.3% and 16.2% of the phenotypic variation, respectively. Two QTLs were identified for GW on chromosomes two and three, and explained 24.3% and 23.5% of the phenotypic variation, respectively. The five QTLs for GT detected on chromosomes two, three, five, six and seven, explained 13.2%, 14.5%, 16.6%, 10.9%, and 10.2% of the phenotypic variation, respectively. A novel QTL for GT, qGT2, was validated on the same region of chromosome two in the selected F3 population. The QTLs identified in this study, and LGL, could be applied to the development of large-grain rice varieties.

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Section: Introductionmentioning

confidence: 99%

QTL Analysis of Rice Grain Size Using Segregating Populations Derived from the Large Grain Line

et al. 2021

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“…A photo-sensitive leaf rolling 1 (psl1) was identi ed with 'napping' phenotype and reduced growth (Zhang et al 2021). Three stable rice QTLs, qARO1, qARO5, and qARO9, which control adaxial leaf rolling in a RIL population was identi ed using a high-density SNP genotyping map (Jang et al 2021). For XY9308 with moderate leaf rolling, the leaf phenotype of the two parents was signi cantly different, and both the upper and lower surfaces of leaves can receive light normally, so the photosynthetic rate is high, which is a suitable genetic material for studying leaf rolling.…”

Section: Discussionmentioning

confidence: 99%

Genetic dissection of the leaf shape in super hybrid rice Xieyou9308 RILs by genome-wide association study

Lian

Zhao

Cheng

et al. 2022

Preprint

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Leaf shape is an important factor of plant architecture and the primary contributor to photosynthesis in cereal crops. However, the genetic and molecular mechanism underlying flag leaf phenotypes remains largely unknown. In this research, 139 RILs derived from hybrid rice Xieyou9308, the parents of which are genetically different in leaf phenotype, were used to identify genetic variants for flag leaf length (FLL), width (FLW), and rolling rate (FLR) with high-density single nucleotide polymorphism (SNP) genotyping data. GWAS was carried out using 262,224 SNPs by modeling them as a quantitative phenotype. In the analysis implemented with QTXNetwork, 21 significant SNPs were observed (8 SNPs for FLL, 5 SNPs for FLW, 8 SNPs for FLR) with accumulated explanatory heritability ranging from 53.29–89.25%. Furthermore, 16 significant associated peaks (2 SNPs for FLL, 11 SNPs for FLW, 3 SNPs for FLR) were detected by EMMAX with explanatory effects ranging from − 8.138 to 37.15. To further validate our findings, relative expression analysis was conducted for 13 candidate genes and 6 known marker genes, revealing that 18 genes are differentially expressed in parents. Among them, the expression level of LOC_Os03g52510 is over 11,000 times in ZH9308 than that in XB, which may positively regulate FLL; the expression level of LOC_Os12g39000 is lower in ZH9308 than that in XB, which is potentially regulating FLW. LOC_Os03g52470, LOC_Os08g29809, and LOC_Os03g21660 had extremely differential expressions in parents. So far, these three genes have not been reported to regulate leaf morphology, indicating that they may be novel genes that regulate FLL, FLW, and FLR. These results may lay a theoretical basis and provide genetic resources for ideal plant architecture breeding in rice.

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“…HD-ZIP transcription factors conferring adaxial identity and KANADI transcription factors conferring abaxial identity also regulate leaf blade rolling (140,420) which occurs in grasses due to shrinkage of 'bulliform' cells on the adaxial blade epidermis. Other factors, including BR and auxin, control the extent of leaf rolling by regulating bulliform specialisation, polar distribution and size as well as leaf thickness (88,141,144,164,399,406,420). Moderate leaf rolling in rice supports high yields by enhancing photosynthesis and upright posture (396), although variation in drought-induced leaf rolling may not always be related to bulliform size or number (35).…”

Section: Leaf Surfacesmentioning

confidence: 99%

Cereal Architecture and Its Manipulation

Dixon

Esse

Hirsz

et al. 2022

Annual Plant Reviews Online

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Our lives depend on an incredibly small number of cereal species whose grain provides more calories to our diet than any other source. The extraordinary productivity of cultivated cereals reflects millennia of selection, recent directed breeding, and modern agricultural practices. Here, we examine selected architectural and agronomic features of major cereal body parts: leaf, branch, inflorescence, stem, and root; and discuss how their manipulation enhanced crop performance. Highlighting synergistic research across laboratory models and field‐based systems, we consider how diversified molecular circuitry, novel regulators and conserved components of genetic, hormonal, and molecular mechanisms control cereal architecture. Lastly, we emphasise the agricultural importance of developmental decisions during cereal growth and propose future perspectives for robust architectural improvement, made ever more urgent by our accelerating climate crisis.

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Major QTLs, qARO1 and qARO9, Additively Regulate Adaxial Leaf Rolling in Rice

Cited by 3 publications

References 35 publications

QTL Analysis of Rice Grain Size Using Segregating Populations Derived from the Large Grain Line

QTL Analysis of Rice Grain Size Using Segregating Populations Derived from the Large Grain Line

Genetic dissection of the leaf shape in super hybrid rice Xieyou9308 RILs by genome-wide association study

Cereal Architecture and Its Manipulation

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