Identification of quantitative trait loci for panicle length and yield related traits under different water and P application conditions in tropical region in rice (Oryza sativa L.)

Navea, Ian Paul; Dwiyanti, Maria Stefanie; Park, Jonghwa; Kim, Backki; Lee, Sang-Bum; Xing, Huang; Koh, Hee‐Jong; Chin, Joong Hyoun

doi:10.1007/s10681-016-1822-z

Cited by 11 publications

(6 citation statements)

References 43 publications

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“…This requires information on the QTL regions controlling other agronomical traits in the variety under study. We previously performed QTL analysis for yieldrelated traits for the same DT-RILs (Navea et al 2017) and found qGN3.1 (flanked by fd10-id3015453) for grain number, which is located near the QTL cluster (id3015453-id3016090) identified in this study. However, Dasanbyeo contributed to the increase of grain number, whereas the improvement of AACs was contributed by TR22183, indicating that the genomic region underlying AACs in TR22183 is negatively linked to grain number.…”

Section: Discussionsupporting

confidence: 54%

Quantitative trait loci controlling the amino acid content in rice (Oryza sativa L.)

Yoo

2017

J Plant Biotechnol

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The amino acid composition of rice is a major concern of rice breeders because amino acids are among the most important nutrient components in rice. In this study, a genetic map was constructed with a population of 134 recombinant inbred lines (RILs) from a cross between Dasanbyeo (Tongil-type indica) and TR22183 (temperate japonica), as a means to detect the main and epistatic effect quantitative trait loci (QTLs) for the amino acid content (AAC). Using a linkage map which covered a total of 1458 cM based on 239 molecular marker loci, a total of six main-effect QTLs (M-QTLs) was identified for the content of six amino acids that were mapped onto chromosome 3. For all the M-QTLs, the TR22183 allele increased the trait values. The QTL cluster (flanked by id3015453 and id3016090) on chromosome 3 was associated with the content of five amino acids. The phenotypic variation, explained by the individual QTLs located in this cluster, ranged from 10.2 to 12.4%. In addition, 26 epistatic QTLs (Ep-QTLs) were detected and the 25 loci involved in this interaction were distributed on all nine chromosomes. Both the M-QTLs and Ep-QTLs detected in this study will be useful in breeding programs which target the development of rice with improved amino acid composition.Keywords Quantitative trait loci, Amino acid content, rice, QTL mapping Introduction Rice (Oryza sativa L.) is one of the most important staple cereal crops for more than 50% of the world's population (Mather et al. 2007). A total of 85% of rice production is consumed by humans, and rice provides 21% of the global human per capita energy and 15% of the per capita protein.Amino acids are the principal building blocks of enzymes and other proteins (Panthee et al. 2006), and protein content and amino acid content (AAC) are key factors in the nutritional quality of rice. Thus, improvement of these factors is a major breeding objective in the effort to meet the food demands of an increasing global population. The 20 amino acids have been classified into two groups, essential and non-essential. Essential amino acids, which cannot be synthesized by animals, need to be taken up from plants or other organisms (D'Mello 2003). Non-essential amino acids are also important because they are required for the synthesis of essential amino acids and other biochemical compounds (Wen et al. 2009). Therefore, genetic knowledge regarding non-essential amino acids will also be beneficial to balancing amino acid composition and improving protein quality. Because rice protein content differs greatly among varieties (Maclean et al. 2002), it is important to utilize rice varieties containing high levels of essential amino acids as genetic resources to breed for improved protein content. The average protein level of the 17 587 cultivars of unmilled (brown) rice in the International Rice Research Institute (IRRI) germplasm collection was 9.5%, and ranged from 4.3 to 18.2%. This phenotypic variation in protein content provides a useful genetic basis for breeding.Protein content and AAC...

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Section: Discussionsupporting

confidence: 54%

Quantitative trait loci controlling the amino acid content in rice (Oryza sativa L.)

Yoo

2017

J Plant Biotechnol

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“…This phenomenon was caused by the dissimilarity between LGL, Hanareum and the typical japonica and indica. A similar problem occurred using an RILs population derived from TR22183, a Chinese japonica possessing some indica-like introgressions (Figure 3B), and dasanbyeo, a Korean tongil-type variety containing some japonica introgressions [42]. To avoid this problem, identification of the genomic differences between parental lines based on whole-genome resequencing, and the development of additional markers, are required.…”

Section: Discussionmentioning

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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“…For example, the differentially expressed gene RBOHB carrying a polymorphic dinucleotide (GA)n SSR in the 5'-UTR overlaps the QTLs associated with salt tolerance [65][66][67][68], potassium chlorate resistance (Gramene QTL ID#AQF091, #CQI1), sodium uptake (#CQI2), sodium to potassium ratio (CQI3), root penetration (CQAW4), and lodging incidence (#AQDZ002, #AQDZ013). Similarly, the gene RSS1 carrying a trinucleotide (GAT)n SSR in the 3'-UTR is known to confer salt tolerance; it overlaps QTLs associated with osmotic maintenance (#AQDX004), root penetration (#AQC005, #DQF1), and drought tolerance traits [69][70][71][72][73]. We validated the polymorphic SSRs found in the UTR regions of RBOHB and RSS1 by sequencing the amplified genomic regions from IR29, Pokkali, and a handful of indica and japonica rice accessions.…”

Section: Simple Sequence Repeat (Ssr) Marker Sitesmentioning

confidence: 99%

Comparative Analysis of Salinity Response Transcriptomes in Salt-Tolerant Pokkali and Susceptible IR29 Rice

Geniza,

Fox,

Sage

et al. 2023

Preprint

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Rice is a major cereal crop responsible for feeding the world's population. To improve grain yield and quality, meet growing demand, and face the challenges posed by abiotic and biotic stress, it is imperative to explore genetic diversity in rice for candidate genes and loci that may contribute to stress tolerance. High salinity abiotic stress in the rice growth environment affects growth, yield, and quality. Therefore, we conducted a salt stress-responsive RNA-Seq-based transcriptome study of two rice (Oryza sativa) varieties, the salt-tolerant Pokkali and the salt-sensitive breeding line IR29. To identify early and late salinity response genes, we collected samples from the treated and untreated plants in this study at 1, 2, 5, 10, and 24 hours after treatment with 300 mM NaCl solution. We identified 7,209 and 6,595 salt-induced differentially expressed transcripts from Pokkali and IR29, respectively, over all time points. We identified ~190,000 single nucleotide polymorphism (SNP) sites and ~40,000 simple sequence repeat (SSR) sites, allowing analysis of their consequences on genetic diversity, transcript structure, gene function, and differential expression. We identified and validated the polymorphic SSRs in the differentially expressed salt-responsive genes Respiratory Burst Oxidase Homolog B (RBOHB) and Rice Salt Sensitive 1 (RSS1) that underly nearby salt tolerance QTLs. This study provides insight into transcriptional programming during salt stress, evidence for improving Oryza genome annotations, and reveals SNP and SSR sites associated with differential gene expression and potential gene function.

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Identification of quantitative trait loci for panicle length and yield related traits under different water and P application conditions in tropical region in rice (Oryza sativa L.)

Cited by 11 publications

References 43 publications

Quantitative trait loci controlling the amino acid content in rice (Oryza sativa L.)

Quantitative trait loci controlling the amino acid content in rice (Oryza sativa L.)

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

Comparative Analysis of Salinity Response Transcriptomes in Salt-Tolerant Pokkali and Susceptible IR29 Rice

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