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Plant Science Today (PST)

Research Articles

Vol. 13 No. 2 (2026)

Identification of genomic regions of rice for yield improvement in coastal saline soils through Correlation and QTL Mapping

DOI
https://doi.org/10.14719/pst.12717
Submitted
13 November 2025
Published
21-05-2026 — Updated on 29-05-2026
Versions

Abstract

Salt stress is the major limiting factor in rice productivity and is being aggravated in varied climatic conditions. The present study focused on association and identification of genomic regions for yield improvement in coastal saline soils. The association studies indicated that selecting the recombinant inbred lines (RILs) with yield traits like good plant height, more number of tillers, longer panicles, more number of filled grains, higher 100-grain weight and salinity traits like lower Na+/K+ ratio and lower salinity scores (< 3) is desirable for breeding salt tolerant varieties. Genetic architecture was studied at EC 7.2 dSm-1 by using quantitative trait loci (QTLs). Genotyping using diversity arrays technology (DArT) sequencing platform with 1K-Rice custom amplicon (1K-RiCA) panel using 1076 single nucleotide polymorphic markers (SNPs) for 150 RILs accounted 24.72 % of polymorphism. The linkage map spanning the length of 3605.80 cM covering all 12 rice chromosomes was generated using inclusive composite interval mapping (ICIM) software version 4.2. Fourteen QTLs were identified for the yield traits viz., ear bearing tillers hill-1, plant height (cm), panicle length (cm), number of filled grains panicle-1 and 100 grain weight (g). However, the major QTLs that are identified for the yield traits-plant height (qPH5, qPH8) and panicle length (qPL10) associated with genes such as OsGPRP3, OsNHX3, OsMT2b and OsPERK8 might facilitate the genetic improvement of rice through marker-assisted selection for achieving salt tolerance in coastal sector where rice is grown in low-lying lands.

References

  1. 1. Hussain M, Ahmad S, Hussain S, Lal R, Ul-Allah S, Nawaz A. Rice in saline soils: physiology, biochemistry, genetics and management. Adv Agron. 2018;148:231–87. https://doi.org/10.1016/bs.agron.2017.11.002
  2. 2. Samy PM, Singh RK, Gregorio GB, Gautam RK, Krishnamurthy SL, Thirumeni S. Genetic improvement of rice for salt tolerance. In: Genetic improvement of rice for salt tolerance. Singapore: Springer Nature Singapore; 2024:1–8. https://doi.org/10.1007/978-981-99-3830-8_1
  3. 3. Mandal UK, Nayak DB, Ghosh A, Bhardwaj AK, Lama TD, Mahajan GR, et al. Delineation of saline soils in coastal India using satellite remote sensing. Curr Sci. 2023;125(12):1339–53. https://doi.org/10.18520/cs/v125/i12/1339-1353
  4. 4. Pranaya J, Roja V, Rao P, Rani MG, Ramesh D. Evaluation of germplasm for seedling stage salinity tolerance in rice (Oryza sativa L.). Int J Plant Soil Sci. 2024;36(5):73–81. https://doi.org/10.9734/ijpss/2024/v36i54503
  5. 5. Falconer DS. An introduction to quantitative genetics. London: Oliver and Boyd; 1964.
  6. 6. Khush GS, Brar DS, Hardy B, editors. Rice genetics IV: proceedings of the 4th International Rice Genetics Symposium. Los Baños: Philippines; 2001.
  7. 7. Mheni NT, Kilasi N, Quiloy FA, Heredia MC, Bilaro A, Meliyo J, et al. Breeding rice for salinity tolerance and salt-affected soils in Africa: A review. Cogent Food Agric. 2024;10(1):2327666. https://doi.org/10.1080/23311932.2024.2327666
  8. 8. Mondal S, Septiningsih EM, Singh RK, Thomson MJ. Mapping QTLs for reproductive stage salinity tolerance in rice using a cross between Hasawi and BRRI dhan28. Int J Mol Sci. 2022;23(19):11376. https://doi.org/10.3390/ijms231911376
  9. 9. Satasiya P, Patel S, Patel R, Raigar OP, Modha K, Parekh V, et al. Meta-analysis of identified genomic regions and candidate genes underlying salinity tolerance in rice (Oryza sativa L.). Sci Rep. 2024;14(1):5730. https://doi.org/10.1038/s41598-024-54764-9
  10. 10. Takuno S, Terauchi R, Innan H. The power of QTL mapping with RILs. PLoS One. 2012;7(10):46545. https://doi.org/10.1371/journal.pone.0046545
  11. 11. Praveena MV. Mapping and confirmation of QTLs for seedling and reproductive stage salinity tolerance in rice (Oryza sativa L.). Lam: Acharya NG Ranga Agricultural University; 2023.
  12. 12. Thomson MJ, de Ocampo M, Egdane J, Rahman MA, Sajise AG, Adorada DL, et al. Characterizing the Saltol quantitative trait locus for salinity tolerance in rice. Rice. 2010;3(2):148–60. https://doi.org/10.1007/s12284-010-9053-8
  13. 13. Waziri A, Kumar P, Purty RS. Saltol QTL and their role in salinity tolerance in rice. Austin J Biotechnol Bioeng. 2016;3(3):15.
  14. 14. Rani GM, Sailaja V, Padmavathi J, Sanjay KK. Identification of favourable alleles for multiple traits and nutrient management in salt tolerant rice varieties to enhance productivity in coastal saline ecosystem. Asian J Microbiol Biotechnol Environ Sci. 2024;26(1):99–105. https://doi.org/10.53550/AJMBES.2024.v26i01.016
  15. 15. IRRI. Standard evaluation system for rice (SES). Los Baños: International Rice Research Institute; 2014.
  16. 16. Singh YP, Singh D, Krishnamurthy SL. Grouping of advanced rice breeding lines based on grain yield and Na:K ratio under alkaline conditions. J Soil Salinity Water Qual. 2014;6(1):21–7.
  17. 17. Singh RK, Chaudhary BD. Biometrical methods in quantitative genetic analysis. New Delhi: Kalyani Publishers; 1985.
  18. 18. Intertek. Agritech-intertek. Available from: https://www.intertek.com/agriculture/agritech
  19. 19. Arbelaez JD, Dwiyanti MS, Tandayu E, Llantada K, Jarana A, Ignacio JC, et al. 1K-RiCA (1K-Rice Custom Amplicon) a novel genotyping amplicon-based SNP assay for genetics and breeding applications in rice. Rice. 2019;12(1):55. https://doi.org/10.1186/s12284-019-0311-0
  20. 20. Wang J, Li H, Zhang L, Li C, Meng L. Users’ manual of QTL IciMapping v4.2. Beijing: Institute of Crop Science, CAAS; 2011.
  21. 21. Kosambi DD. The estimation of map distances from recombination values. In: Selected works in mathematics and statistics. New Delhi: Springer India; 2017:125–30. https://doi.org/10.1007/978-81-322-3676-4_16
  22. 22. McCouch SR. Gene nomenclature system for rice. Rice. 2008;1(1):72–84. https://doi.org/10.1007/s12284-008-9004-9
  23. 23. Kumar S, Vimal SC, Meena RP, Singh A, Srikanth B, Pandey AK, et al. Exploring genetic variability, correlation and path analysis for yield and its component traits in rice (Oryza sativa L.). Plant Arch. 2024;24(1):157–62.
  24. 24. Singh B, Gauraha D, Sao A, Nair SK. Assessment of genetic variability, heritability and genetic advance for yield and quality traits in advanced breeding lines of rice (Oryza sativa L.). J Pharm Innov. 2021;10(8):1627–30.
  25. 25. Li Z, Zhou T, Zhu K, Wang W, Zhang W, Zhang H, et al. Effects of salt stress on grain yield and quality parameters in rice cultivars with differing salt tolerance. Plants. 2023;12(18):3243. https://doi.org/10.3390/plants12183243
  26. 26. Ranawake AL, Amarasinghe UGS. Relationship of yield and yield related traits of some traditional rice cultivars in Sri Lanka as described by correlation analysis. J Sci Res Rep. 2014;3(18):2395–403. https://doi.org/10.9734/JSRR/2014/12050
  27. 27. Balasubramanian M, Vennila S. Comprehensive evaluation of rice genotypes for salt tolerance: in vitro screening, association studies and principal component analysis. Environ Ecol. 2024;42(4A):1677–87. https://doi.org/10.60151/envec/ZANZ3740
  28. 28. Williams K, Mishra A, Verma A, Suresh BG, Lavanya GR. Genetic variability and correlation studies for yield and yield related traits in rice (Oryza sativa L.) genotypes. Int J Curr Microbiol Appl Sci. 2021;10(1):752–64. https://doi.org/10.20546/ijcmas.2021.1001.093
  29. 29. Sitoe HM, Zhang Y, Chen S, Li Y, Ali M, Sowadan O, et al. Detection of QTLs for plant height architecture traits in rice (Oryza sativa L.) by association mapping and the RSTEP-LRT method. Plants (Basel). 2022;11(7):999. https://doi.org/10.3390/plants11070999
  30. 30. Barnaby JY, McClung AM, Edwards JD, Pinson SR. Identification of quantitative trait loci for tillering, root and shoot biomass at the maximum tillering stage in rice. Sci Rep. 2022;12(1):13304. https://doi.org/10.1038/s41598-022-17109-y
  31. 31. Zhu Z, Li X, Wei Y, Guo S, Sha A. Identification of a novel QTL for panicle length from wild rice (Oryza minuta) by specific locus amplified fragment sequencing and high density genetic mapping. Front Plant Sci. 2018;9:1492. https://doi.org/10.3389/fpls.2018.01492
  32. 32. Brondani C, Rangel N, Brondani V, Ferreira E. QTL mapping and introgression of yield related traits from Oryza glumaepatula to cultivated rice (Oryza sativa) using microsatellite markers. Theor Appl Genet. 2002;104(6):1192–203. https://doi.org/10.1007/s00122-002-0869-5
  33. 33. Ashfaq H, Rani R, Perveen N, Babar AD, Maqsood U, Asif M, et al. KASP mapping of QTLs for yield components using a RIL population in basmati rice (Oryza sativa L.). Euphytica. 2023;219(7):79. https://doi.org/10.1007/s10681-023-03206-0
  34. 34. Sakai H, Lee SS, Tanaka T, Numa H, Kim J, Kawahara Y, et al. Rice annotation project database (RAP-DB): an integrative and interactive database for rice genomics. Plant Cell Physiol. 2013;54(2):1–11. https://doi.org/10.1093/pcp/pcs183
  35. 35. Xue T, Wang D, Zhang S, Ehlting J, Ni F, Jakab S, et al. Genome-wide and expression analysis of protein phosphatase 2C in rice and Arabidopsis. BMC Genomics. 2008;9(1):550. https://doi.org/10.1186/1471-2164-9-550
  36. 36. Wang X, Yan X, Tian X, Zhang Z, Wu W, Shang J, et al. Glycine- and proline-rich protein OSGPRP3 regulates grain size and quality in rice. J Agric Food Chem. 2020;68(29):7581–90. https://doi.org/10.1021/acs.jafc.0c01803
  37. 37. Tan Z, Dai Q, Zhang Q, Huang Y, Yan X, Wang X, et al. Disruption of OsGPRP3 impairs salt tolerance and growth vigor in rice. Plant Physiol Biochem. 2025;229:110658. https://doi.org/10.1016/j.plaphy.2025.110658
  38. 38. Bui HHT, Nguyen DH, Dinh LTT, Trinh HTT, Vu TK, Bui VN. Comprehensive in silico analysis of the NHX (Na+/H+ antiporter) gene in rice (Oryza sativa L.). Int J Plant Biol. 2025;16(1):6. https://doi.org/10.3390/ijpb16010006
  39. 39. Chen J, Wen Y, Pan Y, He Y, Gong X, Yang W, et al. Analysis of the role of the rice metallothionein gene OsMT2b in grain size regulation. Plant Sci. 2024;349:112272. https://doi.org/10.1016/j.plantsci.2024.112272

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