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"Quantitative trait loci"

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"Quantitative trait loci"

Research Articles
Characterization of Genes Associated with Salt Tolerance Using Transcriptome Analysis and Quantitative Trait Loci Mapping in Rice
Dong-Min Kim, Ju-Won Kang, Kyu-Chan Shim, Hyun-Jung Kim, Thomas H. Tai, Sang-Nag Ahn
Plant Breed. Biotech. 2021;9(4):318-330.   Published online December 1, 2021
DOI: https://doi.org/10.9787/PBB.2021.9.4.318

We conducted transcriptome profiling analysis of O. glaberrima root using RNA-Seq at the control (OCR) and 100 mM NaCl treatment (OTR) at two time points (6 and 24 hours after treatment) to detect genes induced by salt stress. RNA-Seq analysis generated 102,690,698 sequence reads representing 30,388 predicted transcripts including 6,189 unannotated in Rice Annotation Project database. A total of 539 and 424 of differentially expressed genes (DEGs) were detected between OCR_6hours vs OTR_6hours and between OCR_24hours vs OTR_24hours, respectively (P < 0.001, q < 0.05). Among these DEGs, 262 genes showed constant differential expression at both 6 hours and 24 hours, and these included a bHLH containing protein, WRKY transcription factor, serine/threonine kinase, R2R3 MYB protein, and amino acid transporters. Interestingly, an enhanced seedling salt tolerant introgression line IL55 from a cross between a salt sensitive indica cultivar “Milyang23” and O. glaberrima harbors one DEG, Os02g0787300 encoding a mitogen activated protein kinase kinase (MAPKK) on chromosome 2. Analysis of the salt tolerance of the F2:3 lines from a cross between IL55 and Milyang23 indicated that the O. glaberrima segment on chromosome 2 containing the MAPKK gene was responsible for better shoot growth under salt stress at the seedling stage. The salt inducible genes will be evaluated in introgression lines (ILs) to understand whether the expression of these genes is associated with salt tolerance in ILs having the Milyang23 genetic background. Transcriptome sequence information in this study may be useful for developing DNA markers linked to salinity tolerance for MAS breeding.

Citations

Citations to this article as recorded by  
  • Phylogenomic profiles of whole-genome duplications in Poaceae and landscape of differential duplicate retention and losses among major Poaceae lineages
    Taikui Zhang, Weichen Huang, Lin Zhang, De-Zhu Li, Ji Qi, Hong Ma
    Nature Communications.2024;[Epub]     CrossRef
  • Grain protein function prediction based on self-attention mechanism and bidirectional LSTM
    Jing Liu, Xinghua Tang, Xiao Guan
    Briefings in Bioinformatics.2023;[Epub]     CrossRef
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Genetic Analysis Reveals a Major Effect QTL Associated with High Grain Zinc Content in Rice (Oryza sativa L.)
Shaikh J. Mohiuddin, Ashraful Haque, Manjurul Haque, Tofazzal Islam, Partha S. Biswas
Plant Breed. Biotech. 2020;8(4):327-340.   Published online December 1, 2020
DOI: https://doi.org/10.9787/PBB.2020.8.4.327

Molecular mapping and application of quantitative trait loci (QTL) associated with a higher level of grain Zinc is a viable option to enhance zinc content in rice through breeding. An F2 population derived from a cross between a high yielding variety, BRRI dhan28, and a locally adapted Zn enriched cultivar, Kalobokri was used to map QTLs associated with higher levels of Zn in rice grain. The F2:3 progenies varied significantly (P < 0.0001) in Zinc contents with a mean value remarkably higher than those in the superior parent. Through marker by trait analysis using IciMapping, we detected a large-effect QTL, qGZn3 on chromosome 3 between RM5419 and RM1164 spanning 1.83 Mb interval at the 0.05 level of significance with a threshold LOD of 10.61. This QTL showed a 21.1% (R2 value) contribution to the total phenotypic variation for zinc content in the unpolished rice grains with 4.68 μg/g additive effect of Kalobokri alleles. We also detected 11 metal homeostasis related genes within the interval of qGZn3. In-silico analysis showed that four expressed sequence tags of one candidate gene (LOC_Os03g22810) encoding Cu/Zn superoxide dismutase, a metal-binding protein, are highly active in the endosperm as well as in the embryonic tissue of rice grain. Taken together, our results suggest that qGZn3 is a major QTL associated with high grain Zn content in the F2 progenies of rice. Our findings offer valuable genetic resources to facilitate breeding for high yielding and Zinc-enriched rice variety.

Citations

Citations to this article as recorded by  
  • Precision breeding strategy to enrich iron and zinc in rice
    Rajvir Kaur, Rupinder Kaur, Renu Khanna, Gurjeet Singh, Dinesh Kumar Saini, Amandeep, Kumari Neelam, Navjot Sidhu, Ranvir Singh Gill
    Cereal Research Communications.2026; 54(1): 657.     CrossRef
  • Genomic Insights into the Genetic Control of Iron and Zinc Content in Rice: A Meta-analysis of Key Hotspots
    Om Prakash Raigar, Gaurav Augustine, Rupinder Kaur, Nitika Sandhu
    Journal of Plant Growth Regulation.2025;[Epub]     CrossRef
  • Association analysis of grain zinc and iron content with agro-morphological traits in segregating population of rice
    Rahul Singh, Anand Kumar, Mankesh Kumar, Sweta Sinha, Sareeta Nahakpam, Sunil Kumar, Shashikant, Satyendra, SP Singh
    Oryza-An International Journal on Rice.2024; 61(4): 283.     CrossRef
  • Genomic prediction and QTL analysis for grain Zn content and yield in Aus-derived rice populations
    Tapas Kumer Hore, C. H. Balachiranjeevi, Mary Ann Inabangan-Asilo, C. A. Deepak, Alvin D. Palanog, Jose E. Hernandez, Glenn B. Gregorio, Teresita U. Dalisay, Maria Genaleen Q. Diaz, Roberto Fritsche Neto, Md. Abdul Kader, Partha Sarathi Biswas, B. P. Mall
    Journal of Plant Biochemistry and Biotechnology.2024; 33(2): 216.     CrossRef
  • QTL mapping reveals different set of candidate genes governing stable and location specific QTLs enhancing zinc and iron content in rice
    Sonali Vijay Habde, Shravan Kumar Singh, Dhirendra Kumar Singh, Arun Kumar Singh, Rameswar Prasad Sah, Mounika Korada, Amrutlal R. Khaire, Prasanta Kumar Majhi, Uma Maheshwar Singh, Vikas Kumar Singh, Arvind Kumar
    Euphytica.2024;[Epub]     CrossRef
  • Deciphering the genetic basis of agronomic, yield, and nutritional traits in rice (Oryza sativa L.) using a saturated GBS-based SNP linkage map
    Mark Ian C. Calayugan, Tapas Kumer Hore, Alvin D. Palanog, Amery Amparado, Mary Ann Inabangan-Asilo, Gaurav Joshi, Balachiranjeevi Chintavaram, B. P. Mallikarjuna Swamy
    Scientific Reports.2024;[Epub]     CrossRef
  • Rice biofortification: breeding and genomic approaches for genetic enhancement of grain zinc and iron contents
    P. Senguttuvel, Padmavathi G, Jasmine C, Sanjeeva Rao D, Neeraja CN, Jaldhani V, Beulah P, Gobinath R, Aravind Kumar J, Sai Prasad SV, Subba Rao LV, Hariprasad AS, Sruthi K, Shivani D, Sundaram RM, Mahalingam Govindaraj
    Frontiers in Plant Science.2023;[Epub]     CrossRef
  • QTL Mapping of Mineral Element Contents in Rice Using Introgression Lines Derived from an Interspecific Cross
    Cheryl Adeva, Yeo-Tae Yun, Kyu-Chan Shim, Ngoc Ha Luong, Hyun-Sook Lee, Ju-Won Kang, Hyun-Jung Kim, Sang-Nag Ahn
    Agronomy.2022; 13(1): 76.     CrossRef
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Kompetitive Allele-Specific PCR Marker Development and Quantitative Trait Locus Mapping for Bakanae Disease Resistance in Korean Japonica Rice Varieties
Kyeong-Seong Cheon, Young-Min Jeong, Youn-Young Lee, Jun Oh, Do-Yu Kang, Hyoja Oh, Song Lim Kim, Nyunhee Kim, Eungyeong Lee, Jeongho Baek, Inchan Choi, Kyung-Hwan Kim, Yong Jae Won, In Sun Yoon, Young-il Cho, Jung-Heon Han, Hyeonso Ji
Plant Breed. Biotech. 2019;7(3):208-219.   Published online September 1, 2019
DOI: https://doi.org/10.9787/PBB.2019.7.3.208

High-throughput molecular markers with high genotyping accuracy will be helpful for genetic analysis, mapping of interesting genes, and rice breeding program. To develop high-throughput and cost-effective molecular markers for Korean japonica rice varieties, which are closely-related genetically, we designed kompetitive allele-specific polymerase chain reaction (KASP) assays from the sequence data of 13 Korean japonica rice varieties. Of the 504 new KASP assays, 371 (73.6%) showed polymorphisms among the tested varieties. In addition to the 400 previously developed KASP markers, this resulted in 771 KASP markers being applicable for Korean japonica rice varieties. These KASP markers were used to map the quantitative trait loci (QTLs) for rice bakanae disease (BD) resistance. From the results of QTL mapping and determination of the mortality rate of BD in two F2:F3 populations, a major QTL, qFfR1-1, and a novel QTL, qFfR6, were revealed on chromosome 1 in the Junam/Nampyeong F2:F3 population and on chromosome 6 in the Saenuri/Nampyeong F2:F3 population, respectively. Further, the insertion/deletion markers in the qFfR1-1 region were developed to select BD-resistant japonica rice varieties. The 771 developed KASP markers will accelerate the molecular breeding in Korean japonica rice varieties, and the detected QTLs will be helpful in identifying candidate genes for BD resistance.

Citations

Citations to this article as recorded by  
  • Genetic Diversity and Structural Network Analysis of Korean Rice Varieties Using TCS-based SNPs
    Chang-Min Lee, Hyun-Su Park, Jeonghwan Seo, Song-Hee Park, O-Young Jeong, Keon-Mi Lee, Seul-Gi Park
    Korean Journal of Breeding Science.2026; 58(1): 1.     CrossRef
  • QTL Analysis for Heading Date and Yield-Related Traits Using the Recombinant Inbred Lines Derived from a Cross between ‘Koshihikari’ and ‘IS592BB’
    Hyun-Su Park, Jeonghwan Seo, Songhee Park, Jae-Ryoung Park, Keon-Mi Lee, O-Young Jeong
    Korean Journal of Breeding Science.2026; 58(2): 147.     CrossRef
  • Genome-Wide Association Study to identify Bakanae disease resistance-related QTLs carrying novel candidate genes in rice (Oryza sativa L.)
    Yuting Zeng, Fang-Yuan Cao, Ah-Rim Lee, Dongryung Lee, Backki Kim, Soon-Wook Kwon
    npj Science of Plants.2025;[Epub]     CrossRef
  • Genome-wide association mapping of bakanae disease resistance in rice
    Istiaq Ahmed, Stephen Woodward, Gareth J. Norton
    Computational Biology and Chemistry.2025; 119: 108538.     CrossRef
  • QTL Analysis for Yield and Grain-Related Traits Using the Recombinant Inbred Lines Derived from a Cross between ‘Boramchan’ and ‘Pecos’
    Hyun-Su Park, Chang-Min Lee, Jeonghwan Seo, Songhee Park, Keon-Mi Lee, Jae-Ryoung Park, O-Young Jeong
    Korean Journal of Breeding Science.2025; 57(2): 131.     CrossRef
  • Quantitative Trait Locus Analysis for Quality-Related Traits Using the Recombinant Inbred Lines Derived from a Cross between “Boramchan” and “Pecos” Japonica Rice
    Hyun-Su Park, Chang-Min Lee, Jeonghwan Seo, Songhee Park, Hyeonso Ji, Keon-Mi Lee, Jae-Ryoung Park, O-Young Jeong
    Korean Journal of Breeding Science.2025; 57(4): 373.     CrossRef
  • Genomic Regions and Molecular Markers Associated with Deeper Rooting to Improve Grain Yield in Aerobic Rice (Oryza sativa L.) Production Systems
    Wenliu Gong, Ricky Vinarao, Christopher Proud, Shona Wood, Peter Snell, Shu Fukai, Jaquie Mitchell
    Rice.2025;[Epub]     CrossRef
  • Map-Based Cloning and Characterization of a Major QTL Gene, FfR1, Which Confers Resistance to Rice Bakanae Disease
    Hyeonso Ji, Kyeong-Seong Cheon, Yunji Shin, Chaewon Lee, Seungmin Son, Hyoja Oh, Dong-Kyung Yoon, Seoyeon Lee, Mihyun Cho, Soojin Jun, Gang-Seob Lee, Jeongho Baek, Song Lim Kim, Il-Pyung Ahn, Jae-Hyeon Oh, Hye-Jin Yoon, Young-Soon Cha, Kyung-Hwan Kim
    International Journal of Molecular Sciences.2024; 25(11): 6214.     CrossRef
  • QTL Analysis for Pre-Harvest Sprouting and Low-Temperature Germinability Using Recombinant Inbred Lines Derived from a Cross between ‘Chamdongjin’ and ‘Younghojinmi’
    Hyun-Su Park, Jeonghwan Seo, Heyonso Ji, Gileung Lee, Chang-Min Lee, Jae-Ryoung Park, Songhee Park, Keon-Mi Lee, Mina Jin, O-Young Jeong
    Korean Journal of Breeding Science.2024; 56(2): 79.     CrossRef
  • Quantitative Trait Loci Analysis of Quality-Related Traits Using Recombinant Inbred Lines Derived from a Cross between ‘Chamdongjin’ and ‘Younghojinmi’
    Hyun-Su Park, Jeonghwan Seo, Chang-Min Lee, Songhee Park, Keon-Mi Lee, Jae-Ryoung Park, O-Young Jeong
    Korean Journal of Breeding Science.2024; 56(4): 395.     CrossRef
  • QTL Analysis for Yield-Related Traits Using the Recombinant Inbred Lines Derived From a Cross Between ‘Chamdongjin’ and ‘Younghojinmi’
    Hyun-Su Park, Jeonghwan Seo, Songhee Park, Jae-Ryoung Park, Chang-Min Lee, Mina Jin, O-Young Jeong
    Korean Journal of Breeding Science.2024; 56(1): 31.     CrossRef
  • Bakanae Disease Resistance in Rice: Current Status and Future Considerations
    Liwei Zhan, Ling Chen, Yuxuan Hou, Yuxiang Zeng, Zhijuan Ji
    Agronomy.2024; 14(7): 1507.     CrossRef
  • Identification of qBK2.1, a novel QTL controlling rice resistance against Fusarium fujikuroi
    Szu-Yu Chen, Ming-Hsin Lai, Yi-Ling Chu, Dong-Hong Wu, Chih-Wei Tung, Yue-Jie Chen, Chia-Lin Chung
    Botanical Studies.2023;[Epub]     CrossRef
  • Evaluation of Major Rice Varieties for Bakanae Disease Resistance in Korea
    Sais-Beul Lee, Ju-Won Kang, Ji-Yoon Lee, Gi-Un Seong, Youngho Kwon, So-Myeong Lee, Nkulu Rolly Kabang, Jun-Hyeon Cho, Seong-Hwan Oh, Dongjin Shin, Jong-Hee Lee, Ki-Won Oh, Dong-Soo Park
    Korean Journal of Breeding Science.2023; 55(2): 103.     CrossRef
  • Current insights on rice (Oryza sativa L.) bakanae disease and exploration of its management strategies
    Chinnannan Karthik, Qingyao Shu
    Journal of Zhejiang University-SCIENCE B.2023; 24(9): 755.     CrossRef
  • Fine mapping of qBK1.2, a major QTL governing resistance to bakanae disease in rice
    Amar Kant Kushwaha, Ranjith Kumar Ellur, Sarvesh Kumar Maurya, Gopala Krishnan S., Bishnu Maya Bashyal, Prolay Kumar Bhowmick, K. K. Vinod, Haritha Bollinedi, Nagendra Kumar Singh, Ashok Kumar Singh
    Frontiers in Plant Science.2023;[Epub]     CrossRef
  • Molecular Breeding of Zheyou810, an Indica–Japonica Hybrid Rice Variety with Superior Quality and High Yield
    Jian Song, Yongtao Cui, Honghuan Fan, Liqun Tang, Jianjun Wang
    Agriculture.2023; 13(9): 1807.     CrossRef
  • The Multiple Disease-resistant, Mid-late Maturing Rice Cultivar ‘Chamdongjin’, Carrying the Bacterial Blight Resistance Gene Xa21, with the Genetic Background of ‘Sindongjin’
    Hyun-Su Park, Man-Kee Baek, Woo-Jae Kim, Jung-Pil Suh, Jeom-Ho Lee, Ji-Ung Jeung, Choon-Song Kim, O-Young Jeong, Deok-Ryeol Lee, Chang-Min Lee, Jong-Min Jeong, Young-Jun Mo, Su-Kyung Ha, Dong-Kyu Lee, Hyeonso Ji, Jeonghwan Seo, Jae-Ryoung Park, Hyun-Sook
    Korean Journal of Breeding Science.2023; 55(1): 86.     CrossRef
  • KASP mapping of QTLs for yield components using a RIL population in Basmati rice (Oryza sativa L.)
    Hamza Ashfaq, Reena Rani, Naila Perveen, Allah Ditta Babar, Umer Maqsood, Muhammad Asif, Katherine A. Steele, Muhammad Arif
    Euphytica.2023;[Epub]     CrossRef
  • Development and Application of a Target Capture Sequencing SNP-Genotyping Platform in Rice
    Chaewon Lee, Kyeong-Seong Cheon, Yunji Shin, Hyoja Oh, Young-Min Jeong, Hoon Jang, Yong-Chan Park, Kyung-Yun Kim, Hang-Chul Cho, Yong-Jae Won, Jeongho Baek, Young-Soon Cha, Song-Lim Kim, Kyung-Hwan Kim, Hyeonso Ji
    Genes.2022; 13(5): 794.     CrossRef
  • Identification of Grain Size-Related QTLs in Korean japonica Rice Using Genome Resequencing and High-Throughput Image Analysis
    Yunji Shin, Yong Jae Won, Chaewon Lee, Kyeong-Seong Cheon, Hyoja Oh, Gang-Seob Lee, Jeongho Baek, In Sun Yoon, Song Lim Kim, Young-Soon Cha, Kyung-Hwan Kim, Hyeonso Ji
    Agriculture.2022; 12(1): 51.     CrossRef
  • Characterization and QTL Mapping of a Major Field Resistance Locus for Bacterial Blight in Rice
    Jae-Ryoung Park, Chang-Min Lee, Hyeonso Ji, Man-Kee Baek, Jeonghwan Seo, O-Young Jeong, Hyun-Su Park
    Plants.2022; 11(11): 1404.     CrossRef
  • Breeding of High Cooking and Eating Quality in Rice by Marker-Assisted Backcrossing (MABc) Using KASP Markers
    Me-Sun Kim, Ju-Young Yang, Ju-Kyung Yu, Yi Lee, Yong-Jin Park, Kwon-Kyoo Kang, Yong-Gu Cho
    Plants.2021; 10(4): 804.     CrossRef
  • Recent Advances in Rice Varietal Development for Durable Resistance to Biotic and Abiotic Stresses through Marker-Assisted Gene Pyramiding
    Md Azadul Haque, Mohd Y. Rafii, Martini Mohammad Yusoff, Nusaibah Syd Ali, Oladosu Yusuff, Debi Rani Datta, Mohammad Anisuzzaman, Mohammad Ferdous Ikbal
    Sustainability.2021; 13(19): 10806.     CrossRef
  • Genomic Variation in Korean japonica Rice Varieties
    Hyeonso Ji, Yunji Shin, Chaewon Lee, Hyoja Oh, In Sun Yoon, Jeongho Baek, Young-Soon Cha, Gang-Seob Lee, Song Lim Kim, Kyung-Hwan Kim
    Genes.2021; 12(11): 1749.     CrossRef
  • Evaluation of the Rsistant to Bakanae Disease in Korean Rice Landraces (Oryza sativa L.)
    Soon-Wook Kwon, Na-Eun Kim, Sang-Hyeon Jin, Jeonghwan Seo, Tae-Ho Ham, Joohyun Lee
    Plant Breeding and Biotechnology.2021; 9(4): 355.     CrossRef
  • QTL mapping for pre-harvest sprouting resistance in japonica rice varieties utilizing genome re-sequencing
    Kyeong-Seong Cheon, Yong Jae Won, Young-Min Jeong, Youn-Young Lee, Do-Yu Kang, Jun Oh, Hyoja Oh, Song Lim Kim, Nyunhee Kim, Eungyeong Lee, In Sun Yoon, Inchan Choi, Jeongho Baek, Kyung-Hwan Kim, Hyun-Su Park, Hyeonso Ji
    Molecular Genetics and Genomics.2020; 295(5): 1129.     CrossRef
  • Transcriptome Analysis of Early Defenses in Rice against Fusarium fujikuroi
    An-Po Cheng, Szu-Yu Chen, Ming-Hsin Lai, Dong-Hong Wu, Shih-Shun Lin, Chieh-Yi Chen, Chia-Lin Chung
    Rice.2020;[Epub]     CrossRef
  • Marker integration and development of Fluidigm/KASP assays for high-throughput genotyping of radish
    Hee-Ju Yu, Young-Min Jeong, Young-Joon Lee, Bomi Yim, Ara Cho, Jeong-Hwan Mun
    Horticulture, Environment, and Biotechnology.2020; 61(4): 767.     CrossRef
  • Development of 454 New Kompetitive Allele-Specific PCR (KASP) Markers for Temperate japonica Rice Varieties
    Kyeong-Seong Cheon, Young-Min Jeong, Hyoja Oh, Jun Oh, Do-Yu Kang, Nyunhee Kim, Eungyeong Lee, Jeongho Baek, Song Lim Kim, Inchan Choi, In Sun Yoon, Kyung-Hwan Kim, Yong Jae Won, Young-il Cho, Jung-Heon Han, Hyeonso Ji
    Plants.2020; 9(11): 1531.     CrossRef
  • Multiple Disease Resistant Early Maturing Rice Cultivar ‘IS592BB’ with the Genetic Background of ‘Unkwang’
    Hyun-Su Park, Man-Kee Baek, Woo-Jae Kim, Chang-Min Lee, Hyeonso Ji, Jung-Pil Suh, O-Young Jeong, Young-Chan Cho, Jeom-Ho Lee
    Korean Journal of Breeding Science.2020; 52(4): 473.     CrossRef
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Identification of QTLs Controlling Seedling Traits in Temperate Japonica Rice under Different Water Conditions
Yeo-Tae Yun, Hyun-Jung Kim, Thomas H. Tai
Plant Breed. Biotech. 2019;7(2):106-122.   Published online June 1, 2019
DOI: https://doi.org/10.9787/PBB.2019.7.2.106

The
objective
of this study was to detect QTLs for rice seedling traits under normal water (control) and low water conditions (drought stress). Ninety-eight recombinant inbred lines (RILs) from a cross between closely-related japonica cultivars, M-203 and M-206 were grown under both low water and normal water conditions. QTLs for morphological traits were investigated at seedling stage using 5,164 single nucleotide polymorphisms via genotyping-by-sequencing. Twenty-three QTLs were associated with four seedling traits: shoot length (SL), root length (RL), shoot dry weight (SW) and root dry weight (RW), were detected and most QTLs were clustered on chromosome 1, 6, 7 and 11. Under normal water conditions, nine QTLs for seedling traits were detected and M-203 alleles increased all the values. The locations of most QTLs were consistent with genes that regulate or respond to auxin and gibberellin. For drought tolerance, fourteen QTLs were detected including seven for drought stress conditions and seven for relative performance (values from drought stress conditions/normal water conditions). Seven QTLs from drought stress conditions coincided with the loci of previously identified drought tolerance genes. Based on the shoot and root length under drought stress conditions, five lines exhibiting the highest values in common were selected as a drought tolerance group. Those lines exhibited better growth than the parents under drought stress conditions and had QTLs alleles for drought tolerance detected in this study. QTL information and selected lines may be used for improving seedling vigor and drought tolerance of rice in breeding programs.

Citations

Citations to this article as recorded by  
  • Morpho-physiological and biochemical response of rice (Oryza sativa L.) to drought stress: A review
    Utsav Bhandari, Aakriti Gajurel, Bharat Khadka, Ishwor Thapa, Isha Chand, Dibya Bhatta, Anju Poudel, Meena Pandey, Suraj Shrestha, Jiban Shrestha
    Heliyon.2023; 9(3): e13744.     CrossRef
  • Meta-QTL and ortho-MQTL analyses identified genomic regions controlling rice yield, yield-related traits and root architecture under water deficit conditions
    Bahman Khahani, Elahe Tavakol, Vahid Shariati, Laura Rossini
    Scientific Reports.2021;[Epub]     CrossRef
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Identification of Quantitative Trait Loci Associated with Grain Shape Using Cheongchenong/Nagdong Double Haploid Lines in Rice
Home Regina Wacera, Hyun-Suk Lee, Kyung-Min Kim
Plant Breed. Biotech. 2016;4(2):188-197.   Published online May 31, 2016
DOI: https://doi.org/10.9787/PBB.2016.4.2.188

We performed a molecular marker-based analysis of quantitative trait loci (QTLs) for the traits that determine appearance quality of the grains using 120 double haploid (DH) lines developed by anther culture from F1 hybrid of a cross between Cheongcheong (Oryza sativa L. ssp. Indica) and Nagdong (Oryza sativa L. ssp. Japonica). The traits studied included grain length, grain width, grain thickness, length to width ration and thousand grain weight. This experiment was conducted with two replications of 2013 and 2014. A linkage map with 217 DNA markers was constructed spanning across 2,067.1 centiMorgans (cM) at an average interval of 9.5 cM between markers and covering 12 rice chromosomes. Interval mapping procedure was used to identify the QTLs controlling five grain traits and QTLs detected were further confirmed using composite interval mapping. A total of 24 QTLs affecting grain appearances were identified and mapped on all the twelve chromosomes for 2 years at grain quality. Nine of the 24 QTLs were reproducibly detected in two year trials. Major QTL for grain length was detected on chromosome 5 in 2013 with a phenotypic variation of 64% and chromosome 7 in 2014 that explained 55% of the phenotypic variation. The QTL findings in this study will in future faciltate the gene isolation and breeding application for improvement of rice grain shape and yield.

Citations

Citations to this article as recorded by  
  • Fine mapping of interspecific secondary CSSL populations revealed key regulators for grain weight at qTGW3.1 locus from Oryza nivara
    Malathi Surapaneni, Divya Balakrishnan, Krishnamraju Addanki, Venkateswara Rao Yadavalli, Arun Prem Kumar, P. Prashanthi, R. M. Sundaram, Sarla Neelamraju
    Physiology and Molecular Biology of Plants.2024; 30(7): 1145.     CrossRef
  • Combined Linkage Mapping and Genome-Wide Association Study Identified QTLs Associated with Grain Shape and Weight in Rice (Oryza sativa L.)
    Ju-Won Kang, Nkulu Rolly Kabange, Zarchi Phyo, So-Yeon Park, So-Myeong Lee, Ji-Yun Lee, Dongjin Shin, Jun Hyeon Cho, Dong-Soo Park, Jong-Min Ko, Jong-Hee Lee
    Agronomy.2020; 10(10): 1532.     CrossRef
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Interaction of Rice Quantitative Trait Locus gw9.1 with Three Grain Shape Genes
Yun-Joo Kang, Yun-A Jeon, Ju-Won Kang, Hyun-Sook Lee, Sang-Nag Ahn
Plant Breed. Biotech. 2016;4(1):51-60.   Published online February 28, 2016
DOI: https://doi.org/10.9787/PBB.2016.4.1.51

Grain size is one of the most important factors determining grain yield in rice breeding. In previous studies, we constructed high-density maps for two quantitative trait loci (QTL) for grain weight, tgw2 and gw9.1, using progeny derived from crosses between the japonica cultivar Hwaseong and Oryza grandiglumis, and Hwaseong and O. rufipogon (IRGC 105491), respectively. The wild alleles contributed an increase in grain weight at these two loci. We developed an F2 population (146 plants) by crossing two near isogenic lines (NILs) harboring tgw2 and gw9.1 to know how they interact in the near isogenic background. Simple sequence repeat markers tightly linked to two QTL were used to check the genotype of the F2 population. Based on the genotype at two loci, 146 F2 plants were classified into 9 groups with a combination of three genotypes at each two loci. Two gene interaction was not significant (P=0.99) in the F2. Homozygous plants with wild alleles at two loci showed significantly higher 1,000 grain weight than plants with a single QTL in the F2 and F3. These results indicate that two QTLs act additively in distinct or complementary pathways in controlling GW. Gene expression analysis was also performed to know the relationship of the gw9.1 QTL with three major grain size genes with Hwaseong and two NILs plants at the transcription level. The results from this study provide insight into grain size regulation in rice and are likely to be useful for marker aided selection for grain size.

Citations

Citations to this article as recorded by  
  • Analysis of Yield- and Quality-Related Traits of Risotto Rice Varieties in a Korean Environment
    Songhee Park, Jeonghwan Seo, Chang-Min Lee, Jae-Ryoung Park, Keonmi Lee, O-Young Jeong, Youngjun Mo, Hyun-Su Park
    Korean Journal of Breeding Science.2025; 57(1): 13.     CrossRef
  • QTL Analysis Related to Grain Size Using the Population Derived from a Cross Between Hopum and Basmati 370
    Da-Eun Im, Seong-Gyu Jang, Backki Kim, Jeonghwan Seo, D. S. Kishor, Hee-Jong Koh, Soon-Wook Kwon
    Korean Journal of Breeding Science.2023; 55(2): 118.     CrossRef
  • QTL-by-QTL, QTL-by-environment, and QTL-by-QTL-by-environment interactions of loci controlling grain length in rice
    Tsuneo Kato, Akira Horibata
    Euphytica.2022;[Epub]     CrossRef
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Analysis of QTL Interaction for Grain Weight using Near Isogenic Lines in Rice
Hae-hwang Kim, Dong-min Kim, Ju-won Kang, Hyun-Sook Lee, Yun-ju Kang, Sang-nag Ahn
Plant Breed. Biotech. 2015;3(1):30-38.   Published online March 31, 2015
DOI: https://doi.org/10.9787/PBB.2015.3.1.030

Grain weight (GW) is one of the most important targets for grain yield in rice breeding. In previous studies, two quantitative trait loci (QTLs) for grain weight, tgw2 and gw8.1, were identified using progeny derived from crosses between the japonica cultivar Hwaseong and Oryza grandiglumis, and between Hwaseong and O. rufipogon (IRGC 105491), respectively. The wild alleles increased GW at two loci. An F2 population (186 plants) was developed by crossing two near isogenic lines (NILs) harboring tgw2 and gw8.1 to test their interaction. Simple sequence repeat (SSR) markers tightly linked to the two QTLs were used to check the genotype of the F2 population. Based on the genotype at the two loci, tgw2 and gw8.1, the F2 plants were classified into 9 groups with a combination of three genotypes at each of the two loci. Two-way ANOVA revealed no interaction between the 2 QTLs in the F2 population. The 1,000 grain weight (TGW) of homozygous plants with wild alleles at the two loci was significantly higher than that of plants with a single QTL in the F2 and F3 lines. These results indicate that two QTLs act additively, in distinct or complementary pathways, to control grain weight. Based on these results, it is desirable to pyramid the two QTLs into a single line, since the double-QTL line displayed further increase of TGW in the Hwaseong background.

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Identification of QTL for Grain Protein Content and Grain Hardness from Winter Wheat for Genetic Improvement of Spring Wheat
Hwayoung Heo, Jamie Sherman
Plant Breed. Biotech. 2013;1(4):347-353.   Published online December 31, 2013
DOI: https://doi.org/10.9787/PBB.2013.1.4.347

To utilize the favorable gene(s) from winter wheat for genetic improvement of spring wheat, this study was carried out to identify the quantitative trait loci (QTL) associated with grain protein content (GPC) and grain hardness (GH) by analysis of recombinant inbred lines (RILS) derived from a cross between spring wheat and spring version of winter wheat. A genetic map of 334 loci was constructed which covered 1575.30cM on all 21 chromosomes. Two QTLs on 3B and 5B chromosome were detected for GPC. A QTL identified barc77 on chromosome 3B had additive effect of 0.17 and the other QTL identified by gwm499 on chromosome 5B had additive effect of 0.19. There were two major QTLs for GH identified on Chromosome 1B and chromosome 5A. The QTL on 1B was localized within a 18.7cM region flanked by wmc719 and wmc367-1 with 1.75 additive effect. The QTL on chromosome 5A flanked by SNP markers, IWA6573 and IWA2363, had additive effect of 1.44.

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