Identification of Yield and Yield-Related Quantitative Trait Loci for the Field High Temperature Condition in Backcross Populations of Rice (<i>Oryza sativa</i>L.)

Seo, Jeonghwan; Lee, So-Myeong; Han, Jae-Hyuk; Shin, Na-Hyun; Koh, Hee‐Jong; Chin, Joong Hyoun

doi:10.9787/pbb.2019.7.4.415

Cited by 7 publications

(6 citation statements)

References 30 publications

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“…A total of 192 indica-japonica Fluidigm markers for SNP genotypes [16], designed based on the genomic difference between two rice subspecies, were used for phylogenetic analysis of LGL and detecting QTLs associated with the three grain size related traits in the F 2 population derived from a cross between LGL and Hanareum. For validation of QTLs in the F 3 population, 11 indica-japonica Fluidigm SNP markers on target regions and 6 additional Fluidigm SNP markers developed in the previous studies [20,28] were used.…”

Section: Fluidigm Genotypingmentioning

confidence: 99%

“…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%

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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: Fluidigm Genotypingmentioning

confidence: 99%

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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“…It is controlled by the expression of multiple quantitative trait loci (QTLs) subject to environmental changes (Fig. 7.4) (Bai et al 2012;Seo et al 2019). Wang et al (2016) conducted genome-wide analyses of the association between SSR markers and seven yield traits and four-grain quality traits of 277 primal parental lines of hybrid rice and revealed a correlation of 29 loci with the 11 agronomic characters; one for plant height, two for tiller number, one for panicle length and the others for grain size and grain quality.…”

Section: Qtls For Yield Componentsmentioning

confidence: 99%

Diversity of Panicle Architecture and Traits Influencing Grain Filling

Mohapatra

Sahu

2021

Panicle Architecture of Rice and Its Relationship With Grain Filling

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In the long history of domestication through cultivation and selection under diverse habitats; the plant acquired adaptability for growth matching with the physico-chemical behavior of the environment, which is most prominently reflected in the phenology of panicle. Panicle morphology varies significantly more than any other phenotypic attribute of the plant and it is both cultivar and environmentspecific. The architectural traits, including branching, panicle length, and spikelet number, are established during the reproductive phase from panicle initiation to heading depending on activities rachis and branch meristems. Termination of rachis meristem activity fixes primary branch number, whereas, the transition of the primary or secondary meristem into spikelet limits spikelet number; the earlier the closure of the events less are the numbers. Many genes, either small effects or major effects, have been identified that are associated with panicle architecture and grain yield. They are distributed across the 12 rice chromosomes.

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“…Five SNP markers, out of the 39 SNP markers, were designed in 3 or 5 UTR. In addition, 14 agronomic traits, related to SNP markers in indica-japonica SNP set 2 [29] and four previously developed yield related SNP markers [36], were also used for the genotyping of 94 diverse germplasms. A total of 57 SNP markers were genotyped for 94 germplasms using the Fluidigm system, and consequently, 54 SNPs showed polymorphism and a clear genotype, except one monomorphic SNP marker designed in Os01g0348900 on the Chr1-2 block and two SNP markers which showed low base call quality, SaF-CT and SLG7-GC, in indica-japonica SNP set 2.…”

Section: Snp Marker Development and Genotyping Using Fluidigm Platformmentioning

confidence: 99%

Characterization of the Common Japonica-Originated Genomic Regions in the High-Yielding Varieties Developed from Inter-Subspecific Crosses in Temperate Rice (Oryza sativa L.)

Seo

Lee

Han

et al. 2020

Genes

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The inter-subspecific crossing between indica and japonica subspecies in rice have been utilized to improve the yield potential of temperate rice. In this study, a comparative study of the genomic regions in the eight high-yielding varieties (HYVs) was conducted with those of the four non-HYVs. The Next-Generation Sequencing (NGS) mapping on the Nipponbare reference genome identified a total of 14 common genomic regions of japonica-originated alleles. Interestingly, the HYVs shared japonica-originated genomic regions on nine chromosomes, although they were developed through different breeding programs. A panel of 94 varieties was classified into four varietal groups with 38 single nucleotide polymorphism (SNP) markers from 38 genes residing in the japonica-originated genomic regions and 16 additional trait-specific SNPs. As expected, the japonica-originated genomic regions were only present in the japonica (JAP) and HYV groups, except for Chr4-1 and Chr4-2. The Wx gene, located within Chr6-1, was present in the HYV and JAP variety groups, while the yield-related genes were conserved as indica alleles in HYVs. The japonica-originated genomic regions and alleles shared by HYVs can be employed in molecular breeding programs to further develop the HYVs in temperate rice.

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Identification of Yield and Yield-Related Quantitative Trait Loci for the Field High Temperature Condition in Backcross Populations of Rice (Oryza sativaL.)

Cited by 7 publications

References 30 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

Diversity of Panicle Architecture and Traits Influencing Grain Filling

Characterization of the Common Japonica-Originated Genomic Regions in the High-Yielding Varieties Developed from Inter-Subspecific Crosses in Temperate Rice (Oryza sativa L.)

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