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"M. Akhlasur Rahman"

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"M. Akhlasur Rahman"

Research Articles
Dissecting QTLs for Reproductive Stage Salinity Tolerance in Rice from BRRI dhan 47
Sejuti Mondal, Teresita H. Borromeo, M. Genaleen Q. Diaz, Junrey Amas, M. Akhlasur Rahman, Michael J. Thomson, Glenn B. Gregorio
Plant Breed. Biotech. 2019;7(4):302-312.   Published online December 1, 2019
DOI: https://doi.org/10.9787/PBB.2019.7.4.302

Salinity is a common and increasing problem in many coastal rice producing areas around the world. Salinity tolerance at the reproductive stage in rice is crucial as it determines grain yield. An F2 mapping population was developed from two modern rice cultivars contrasting in tolerance: NSIC Rc222 (a high-yielding salt-sensitive variety released in the Philippines) and BRRI dhan 47 (a salt-tolerant variety released in Bangaldesh). The performance of the F2 population showed transgressive segregation in the yield components under salinity stress of EC 10 dS/m under salinized field conditions. Ninety-six single nucleotide polymorphism (SNP) markers using 96-plex FluidigmTM genotyping were used to construct a linkage map of 1306.2 cM (Kosambi), with an average interval size of 13.6 cM. Seven putative quantitative trait loci (QTLs) for reproductive stage salinity tolerance traits having LOD values ranging from 2.9 to 4.1 were identified on chromosomes 1, 2, 5 and 11, explaining 13.4 to 18.4% of the phenotypic variation. Results of this mapping study identified a genomic region on chromosome 2 that confers salinity tolerance at the reproductive stage as measured by the number of filled spikelets, percent filled spikelets and yield. This study reports the molecular mapping of QTLs controlling reproductive-stage salinity tolerance-related traits, which will be useful in marker-assisted selection and breeding population development in rice.

Citations

Citations to this article as recorded by  
  • Identification of QTLs for reproductive stage salinity tolerance in rice using a cross between CSR28 and BRRI dhan28
    Sejuti Mondal, Robert Vaughn, Endang M. Septiningsih, Rakesh K. Singh, Michael J. Thomson
    Crop Science.2025;[Epub]     CrossRef
  • Meta-analysis of identified genomic regions and candidate genes underlying salinity tolerance in rice (Oryza sativa L.)
    Pratik Satasiya, Sanyam Patel, Ritesh Patel, Om Prakash Raigar, Kaushal Modha, Vipul Parekh, Haimil Joshi, Vipul Patel, Ankit Chaudhary, Deepak Sharma, Maulik Prajapati
    Scientific Reports.2024;[Epub]     CrossRef
  • Evaluation of salinity tolerance of lowland rice genotypes (Oryza sativa L.) at the reproductive stage
    Rafaliarivony Safidimanjato, Lisy Tiana Ranarijaona Hery, Rasoafalimanana Mbolarinosy, Radanielina Tendro, Wissuwa Matthias
    African Journal of Agricultural Research.2023; 19(10): 945.     CrossRef
  • Mapping and Identification a Salt-Tolerant QTL in a Salt-Resistant Rice Landrace, Haidao86
    Lixia Xie, Chongke Zheng, Wen Li, Menglin Pu, Guanhua Zhou, Wei Sun, Xiu Wu, Xiangyu Zhao, Xianzhi Xie
    Journal of Plant Growth Regulation.2022; 41(6): 2347.     CrossRef
  • Mapping QTLs for Reproductive Stage Salinity Tolerance in Rice Using a Cross between Hasawi and BRRI dhan28
    Sejuti Mondal, Endang M. Septiningsih, Rakesh K. Singh, Michael J. Thomson
    International Journal of Molecular Sciences.2022; 23(19): 11376.     CrossRef
  • Identification of Quantitative Trait Loci Related to Salt Tolerance of Indica Rice RIL Population in Different Growth Stages
    S. M. M. Razi, R. Shirzadian-Khorramabad, H. Sabouri, B. Rabiei, H. H. Moghadam
    Russian Journal of Genetics.2022; 58(9): 1091.     CrossRef
  • Genetic Mapping to Detect Stringent QTLs Using 1k-RiCA SNP Genotyping Platform from the New Landrace Associated with Salt Tolerance at the Seedling Stage in Rice
    Sheikh Maniruzzaman, Mohammad Akhlasur Rahman, Mehfuz Hasan, Mohammad Golam Rasul, Abul Hossain Molla, Hasina Khatun, Salma Akter
    Plants.2022; 11(11): 1409.     CrossRef
  • QTL MAPPING FOR SALT TOLERANCE AT REPRODUCTIVE STAGE IN RICE: A MINIREVIEW
    Nguyen Sao MAI, Yoshihiko HIRAI
    Journal of Environmental Science for Sustainable Society.2021; 10(Supplement): MR08_p31.     CrossRef
  • Identification and Validation of QTLs for Yield and Yield Components under Long-Term Salt Stress Using IR64 CSSLs in the Genetic Background of Koshihikari and Their Backcross Progenies
    Nguyen Sao Mai, Dao Duy Hanh, Mai Nakashima, Kotaro Kumamoto, Nguyen Thi Thu Thuy, Tohru Kobata, Kuniyuki Saitoh, Yoshihiko Hirai
    Agriculture.2021; 11(8): 777.     CrossRef
  • Genome-Wide Association Mapping for Salt Tolerance of Rice Seedlings Grown in Hydroponic and Soil Systems Using the Bengal and Assam Aus Panel
    Caijin Chen, Gareth J. Norton, Adam H. Price
    Frontiers in Plant Science.2020;[Epub]     CrossRef
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Estimating Pedigree-Based Breeding Values and Stability Parameters of Elite Rice Breeding Lines for Yield under Salt Stress during the Boro Season in Bangladesh
M. Ruhul Quddus, M. Akhlasur Rahman, Nusrat Jahan, Sanjoy K. Debsharma, Ribed F. Disha, M. Moniruzzaman Hasan, Tamal L. Aditya, KM Iftekharuddaula, Bertrand C.Y. Collard
Plant Breed. Biotech. 2019;7(3):257-271.   Published online September 1, 2019
DOI: https://doi.org/10.9787/PBB.2019.7.3.257

In salinity affected areas, variation in salinity level is the major cause of yield fluctuations in rice during the dry season (boro season). To sustain food security in Bangladesh, plant breeders need to develop new rice varieties which are higher yielding, salinity tolerant and stable across different environments. We evaluated the yield performance and stability of 51 rice elite genotypes including two salinity tolerant rice varieties (BRRI dhan67 and BINA dhan-10) and the dominant boro rice variety (BRRI dhan28) in three locations, including a salinity “hotspot”. Best linear unbiased predictions (BLUPs) were used to identify superior genotypes from multi-environment trials. Selection from a large set of candidates is required for evaluation and recommending a new variety for release. Estimated breeding values (EBVs) were used to select parents. Six parents with high EBVs (BR8982-5, IR 87870-6-1-1-1-1-B; BR8943-B-1-2-7, BR8940-B-17-4-7, A69-1 and BR8943-B-5-5-14) would be useful as parents to develop new breeding populations. To evaluate yield performance and stability across environments, we used additive main effects and multiplicative interaction (AMMI) model using a randomized complete block design with two replications. Genotype × environmental analysis was performed using GGE biplot analysis. Based on yield performance and stability, BR8982-5, IR 87870-6-1-1-1-1-B, BR8943-B-1-2-7, BR8940-B-17-4-7, A69-1 and BR8943-B-5-5-14 were promising across the tested environments, as they showed yield advantages over check varieties. The results in this study will be useful for selecting the elite lines suitable for salinity affected areas and parents for rapid breeding of salinity tolerance.

Citations

Citations to this article as recorded by  
  • Identification of elite rice lines with better breeding values using genomic prediction and multi-trait genotype ideotype distance index (MGIDI) for grain yield under irrigation cropping system
    S. S. Chaity, M. R. Islam, M. Faruquee, J. U. Ahmed, A. K. M. Aminul Islam, C Anilkumar
    PLOS One.2026; 21(2): e0340188.     CrossRef
  • Mean deviation is reliable parameter for evaluating inbred-line yield potential in hybrid breeding
    Y.D. Zhang, H.M. Chen, Z.W. Li, W.H. Yao, F.Y. Jiang, T.Z. Wang, I. Babar, C.X. Xu
    Journal of Crop Improvement.2026; 40(3): 320.     CrossRef
  • Identification of High-Performing and Stable Pearl Millet Genotypes Using Multi-trait Stability Index
    Tripti Singhal, C. Tara Satyavathi, S. P. Singh, Mukesh Sankar, Sunaina Yadav, M. Mallik, C. Bharadwaj
    Agricultural Research.2026; 15(1): 92.     CrossRef
  • AMMI analysis of elite bread wheat (Triticum aestivum L.) selections for genotype by environment interaction and stability of grain yield in Southern Ethiopia
    Liyew Alemayehu, Mesfin Kebede, Eyasu Wada, Mehdi Rahimi
    PLOS ONE.2025; 20(1): e0318559.     CrossRef
  • Multivariate analysis for identifying high-yielding rice cultivars based on seed yield and morphological traits
    Md. Asadulla Al Galib, Sumaiya Farzana, Tusher Chakrobarty, Mohammad Zahidul Islam, Bir Jahangir Shirazy, Md. Akhlasur Rahman, Shahin Imran, Md. Tahjib-Ul-Arif, Mohammad Saidur Rhaman
    Discover Plants.2025;[Epub]     CrossRef
  • Cultivating sustainable futures: multi-environment evaluation and seed yield stability of faba bean (Vicia faba L.) genotypes by using different stability parameters in Ethiopia
    Demekech Wondaferew, Destaw Mullualem, Walelgn Bitewlgn, Zelalem Kassa, Yekoye Abebaw, Habib Ali, Kelelaw Kebede, Tessema Astatkie
    BMC Plant Biology.2024;[Epub]     CrossRef
  • Identification and evaluation of high-performing advanced germplasm of rice through morphological and breeding value analysis
    Md. Ariful Islam, Md. Moniruzzaman Hasan, Mohammad Anwar Hossain, Md. Ashraful Haque, Md. Nur Alam Siddique, Md. Shamsuddoha, Muhammad Ashraful Habib, Sumaiya Sultana Risha
    Discover Agriculture.2024;[Epub]     CrossRef
  • Bunch component performance of AVROS and Yangambi population
    A N Simamora, H Y Rahmadi, Y Yenni, Sujadi, H A Siregar, N Supena, M Arif, E Supriyanto, A R Purba
    IOP Conference Series: Earth and Environmental Science.2024; 1308(1): 012017.     CrossRef
  • Developing climate-resilient rice varieties (BRRI dhan97 and BRRI dhan99) suitable for salt-stress environments in Bangladesh
    Sanjoy K. Debsharma, M. Akhlasur Rahman, Mahmuda Khatun, Ribed F. Disha, Nusrat Jahan, Md. Ruhul Quddus, Hasina Khatun, Sharifa S. Dipti, Md. Ibrahim, K. M. Iftekharuddaula, Md. Shahjahan Kabir, Md Ashrafuzzaman
    PLOS ONE.2024; 19(1): e0294573.     CrossRef
  • Genotype-by-environment interaction and stability analysis of grain yield of bread wheat (Triticum aestivum L.) genotypes using AMMI and GGE biplot analyses
    Destaw Mullualem, Alemu Tsega, Tesfaye Mengie, Desalew Fentie, Zelalem Kassa, Amare Fassil, Demekech Wondaferew, Temesgen Assefa Gelaw, Tessema Astatkie
    Heliyon.2024; 10(12): e32918.     CrossRef
  • Comparative analysis of parametric and non-parametric statistics for grain yield stability in rice (Oryza sativa L.)
    Hari Kesh, Khushi Ram Battan, Mujahid Khan
    Vegetos.2024; 38(2): 686.     CrossRef
  • B × R cross de rived parental line development using breeding value: A new model for hybrid rice parental line development
    Md. Ruhul Quddus, Md. Jamil Hasan, Mst. Umma Kulsum, Satyen Mondal
    Plant Gene.2023; 36: 100431.     CrossRef
  • SNP Based Trait Characterization Detects Genetically Important and Stable Multiple Stress Tolerance Rice Genotypes in Salt-Stress Environments
    Sanjoy K. Debsharma, Mohammad Akhlasur Rahman, Mohammad Ruhul Quddus, Hasina Khatun, Ribed F. Disha, Popy R. Roy, Sharif Ahmed, Mohamed El-Sharnouby, Khandakar Md. Iftekharuddaula, Salman Aloufi, Fahad M. Alzuaibr, Mohammed Alqurashi, Mohamed I. Sakran, M
    Plants.2022; 11(9): 1150.     CrossRef
  • Harnessing on Genetic Variability and Diversity of Rice (Oryza sativa L.) Genotypes Based on Quantitative and Qualitative Traits for Desirable Crossing Materials
    Sanjoy K. Debsharma, Md. Abu Syed, Md. Hannan Ali, Sheikh Maniruzzaman, Popy R. Roy, Marian Brestic, Ahmed Gaber, Akbar Hossain
    Genes.2022; 14(1): 10.     CrossRef
  • Evaluating Genotype × Environment Interactions of Yield Traits and Adaptability in Rice Cultivars Grown under Temperate, Subtropical and Tropical Environments
    Xing Huang, Su Jang, Backki Kim, Zhongze Piao, Edilberto Redona, Hee-Jong Koh
    Agriculture.2021; 11(6): 558.     CrossRef
  • Estimating Breeding Value of Agronomic Traits in Oriental Tobacco Genotypes under Broomrape Stress and Normal Conditions
    Maryam Tahmasbali, Reza Darvishzadeh, Amir Fayaz Moghaddam
    Plant Genetic Researches.2020; 7(1): 103.     CrossRef
  • Application of Pedimap: a pedigree visualization tool to facilitate the decisioning of rice breeding in Sri Lanka
    Rasika Rathnayake, Shehan Sahibdeen, Kapila Udawela, Cholani Weebadde, Wijayasiri Weerakoon, Suneth Sooriyapathirana
    Scientific Reports.2020;[Epub]     CrossRef
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