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Plant Science Today (PST)

Research Articles

Vol. 12 No. sp3 (2025): Advances in Plant Health Improvement for Sustainable Agriculture

Genetic insights and principal component analysis of inter-subspecific derivatives in rice (Oryza sativa L.)

DOI
https://doi.org/10.14719/pst.8214
Submitted
12 March 2025
Published
17-06-2025

Abstract

Rice is a staple crop in Southeast Asia and increasing its productivity through hybrid breeding is essential to meet the growing population demand. The development of new restorers is a key goal in hybrid rice breeding to increase heterosis. Studying newly developed breeding lines and their trait associations is crucial for their effective use in future breeding activities. The newly developed lines, derived from indica and tropical japonica crosses, were evaluated for eight quantitative traits across four locations to study genetic variation and trait associations. Single plant yield (20.92) had the highest genotypic coefficient of variation (CVg), while total spikelets per panicle (17.71), productive tillers (15.35) and plant height (13.31), recorded moderate CVg. Further, all traits except spikelet fertility, exhibited high broad-sense heritability spikelet fertility. The first three principal components (PCs), with eigenvalues greater than one, explained 67 % of the total variation. The biplot of the first two PCs, accounting for 53 % of the variation, showed significant divergence among genotypes, with plant height contributing most, followed by single plant yield and spikelet fertility. Spikelet fertility (0.44) and the total number of spikelets per panicle (0.25) showed a strong correlation with single plant yield in the combined analysis and across locations, making them promising traits for selection. Thus, greater emphasis must be placed on spikelet fertility and the number of spikelets per panicle when selecting inter-subspecific cross derivatives. The newly developed inter-subspecific breeding lines in the present study showed significant variation in yield and other traits, making them valuable resources for heterosis breeding.

References

  1. 1. Fukagawa NK, Ziska LH. Rice: Importance for global nutrition. J Nutr Sci Vitaminol (Tokyo). 2019;65(Supplement):S2–3. https://doi.org/10.3177/jnsv.65.s2
  2. 2. Fahad S, Adnan M, Noor M, Arif M, Alam M, Khan IA, et al. Major constraints for global rice production. In: Advances in rice research for abiotic stress tolerance. Elsevier; 2019. p. 1–22. https://doi.org/10.1016/B978-0-12-814332-2.00001-0
  3. 3. Alexandratos N, Bruinsma J. World agriculture towards 2030/2050: the 2012 revision. 2012.
  4. 4. Rezk AA, Mohamed HI, El-Beltagi HS. Genetic variability and diversity analysis in Oryza sativa L. genotypes using quantitative traits and SSR markers. Saudi J Biol Sci. 2024;31(3):103944. https://doi.org/10.1016/j.sjbs.2024.103944
  5. 5. Li J, Yuan L. Hybrid rice: genetics, breeding, and seed production. Plant Breed Rev. 1999;17:15–158. https://doi.org/10.1002/9780470650134.ch2
  6. 6. Zhang G. The next generation of rice: inter-subspecific indica-japonica hybrid rice. Front Plant Sci. 2022;13:857896.
  7. 7. Zheng X, Wei F, Cheng C, Qian Q. A historical review of hybrid rice breeding. J Integr Plant Biol. 2024;66(3):532–45. https://doi.org/10.1111/jipb.13598
  8. 8. Li J, Zhou J, Zhang Y, Yang Y, Pu Q, Tao D. New insights into the nature of interspecific hybrid sterility in rice. Front Plant Sci. 2020;11:555572. https://doi.org/10.3389/fpls.2020.555572
  9. 9. Nivedha R, Manonmani S, Kalaimagal T, Raveendran M, Kavitha S. Assessing the genetic diversity of parents for developing hybrids through morphological and molecular markers in rice (Oryza sativa L.). Rice. 2024;17(1):17. https://doi.org/10.1186/s12284-024-00691-2
  10. 10. Awad-Allah MM, Shafie WW, Alsubeie MS, Alatawi A, Safhi FA, ALshamrani SM, et al. Utilization of genetic resources, genetic diversity and genetic variability for selecting new restorer lines of rice (Oryza sativa L.). Genes (Basel). 2022;13(12):2227. https://doi.org/10.3390/genes13122227
  11. 11. Demeke B, Dejene T, Abebe D. Genetic variability, heritability, and genetic advance of morphological, yield related and quality traits in upland rice (Oryza Sativa L.) genotypes at pawe, northwestern Ethiopia. Cogent Food Agric. 2023;9(1):2157099. https://doi.org/10.1080/23311932.2022.2157099
  12. 12. Patel T, Singh SK, Anand KJ, Bichewar N. Yield trait analysis in rice (Oryza sativa L.) Restorer lines through genetic variability, correlation and path coefficient studies. Plant Arch. 2025;25(1):565–73. https://doi.org/10.51470/PLANTARCHIVES.2025.v25.supplement-1.076
  13. 13. Chouhan YN, Singh SK, Chakrawarty VK, Elahi T, Chandanan S, Gautam R. Genetic assessment of fertility restorer rice lines for yield and yield contributing traits. Plant Arch. 2024;24(2). https://doi.org/10.51470/PLANTARCHIVES.2024.v24.no.2.119
  14. 14. Awad-Allah MM, Elekhtyar NM, El-Abd MA, Abdelkader MF, Mahmoud MH, Mohamed AH, et al. Development of new restorer lines carrying some restoring fertility genes with flowering, yield and grains quality characteristics in rice (Oryza sativa L.). Genes (Basel). 2022;13(3):458. https://doi.org/10.3390/genes13030458
  15. 15. Agalya JS, Hari PP, Ramchander S, Dinesh KP, Devesena N, Naveenkumar R, et al. Assessment of variability parameters and diversity of panicle architectural traits associated with yield in rice (Oryza sativa L.). Plant Science Today. 2024;11(1):109–18. https://doi.org/10.14719/pst.2658
  16. 16. Heera PK, Ram M, Kumar R, Murali S, Kumar AS. Analysis of genetic variability, heritability and genetic advance for yield and yield associated traits in Rice (Oryza sativa L.). Ecology, Environment & Conservation. 2023;29:S160–3. https://doi.org/10.53550/EEC.2023.v29i05s.028
  17. 17. Sruthi K, Eswari KB, Raju CD, Madhav MS, Dhandapani A, Sree MB, et al Breeding superior hybrid rice parental lines through inter sub-specific hybridization. Extended Summaries. 2020;66.
  18. 18. Edukondalu B, Reddy VR, Rani TS, Kumari CHA, Soundharya B. Assessment of variation in rice maintainer lines using principal component analysis. Electron J Plant Breed. 2024;15(1):270–6. https://doi.org/10.37992/2024.1501.024
  19. 19. Saketh T, Shankar VG, Srinivas B, Hari Y. Correlation and path coefficient studies for grain yield and yield components in rice (Oryza sativa L.). Int J Plant Soil Sci. 2023;35(19):1549–58. https://doi.org/10.9734/ijpss/2023/v35i193700
  20. 20. Tiwari S, Singh Y, Upadhyay PK, Koutu GK. Principal component analysis and genetic divergence studies for yield and quality-related attributes of rice restorer lines. Indian Journal of Genetics and Plant breeding. 2022;82(01):94–8. https://doi.org/10.31742/IJGPB.82.1.13
  21. 21. Semeskandi MN, Mazloom P, Arabzadeh B, Moghadam MN, Ahmadi T. Application of correlation coefficients and principal components analysis in stability of quantitative and qualitative traits on rice improvement cultivation. Brazilian Journal of Biology. 2023;84:e268981. https://doi.org/10.1590/1519-6984.268981
  22. 22. Tnau. Crop Production Manual. https://agritech.tnau.ac.in/pdf/AGRICULTURE.pdf. 2020.
  23. 23. IRRI. Rice standard evaluation system. 2015. International Rice Research Institute, Phillipines. http://www.knowledgebank.irri.org/images/docs/rice-standard-evaluation-system.pdf.
  24. 24. Patterson HD, Thompson R. Recovery of inter-block information when block sizes are unequal. Biometrika. 1971;58(3):545–54. https://doi.org/10.2307/2334389
  25. 25. Olivoto T, Lúcio AD. metan: An R package for multi‐environment trial analysis. Methods Ecol Evol. 2020;11(6):783–9. https://doi.org/10.1111/2041-210X.13384
  26. 26. Fernández EB, Pelea LP. Generalized linear mixed models. Its application in plant breeding. Cultivos Tropicales. 2018; 39(1):127-133.
  27. 27. Lee SY, Lee HS, Lee CM, Ha SK, Park HM, Lee SM, et al. Multi-environment trials and stability analysis for yield-related traits of commercial rice cultivars. Agriculture. 2023;13(2):256. https://doi.org/10.3390/agriculture13020256
  28. 28. Uphoff N, Fasoula V, Iswandi A, Kassam A, Thakur AK. Improving the phenotypic expression of rice genotypes: Rethinking “intensification” for production systems and selection practices for rice breeding. Crop J. 2015;3(3):174–89. https://doi.org/10.1016/j.cj.2015.04.001
  29. 29. Olivoto T, Nardino M, Carvalho IR, Follmann DN, Ferrari M, Szareski VJ, et al. REML/BLUP and sequential path analysis in estimating genotypic values and interrelationships among simple maize grain yield-related traits. Genetics and Molecular Research. 2017;16(1):1–19. https://doi.org/10.4238/gmr16019525
  30. 30. Prakash S, Reddy SS, Chaudhary S, Vimal SC, Kumar A. Multivariate analysis in rice (Oryza sativa L.) germplasms for yield attributing traits. Plant Science Today. 2024;11(1):64–75. https://doi.org/10.14719/pst.2231
  31. 31. Limbongan Y, Sjahril R, Pata’dungan AM, Parari TY. Genetic performance, heritability, and correlation of traits in new plant type of rice lines for highland ecosystem. Reproduction and Breeding. 2024;4(4):203–11. https://doi.org/10.1016/j.repbre.2024.08.002
  32. 32. Vinay I, Shivani D, Senguttuvel P, Barbadikar KM. Estimates of genetic parameters for yield attributing characters in restorer lines of rice (Oryza sativa L.). Int J Environ Clim Change. 2023;13(10):3954–61. https://doi.org/10.9734/ijecc/2023/v13i103069
  33. 33. Jasmine C, Shivani D, Senguttuvel P, Naik SD. Genetic variability and association studies in maintainer and restorer lines of rice (Oryza sativa L.). Pharm Innov J. 2022;11(1):569–76.
  34. 34. Debsharma SK, Syed MA, Ali MH, Maniruzzaman S, Roy PR, Brestic M, et al. Harnessing on genetic variability and diversity of rice (Oryza sativa L.) genotypes based on quantitative and qualitative traits for desirable crossing materials. Genes (Basel). 2022;14(1):10. https://doi.org/10.3390/genes14010010
  35. 35. Hasan JM, Kulsum UM, Majumder RR, Sarker U. Genotypic variability for grain quality attributes in restorer lines of hybrid rice. Genetika. 2020;52(3):973–89. https://doi.org/10.2298/GENSR2003973H
  36. 36. Bornare SS, Mittra SK, Mehta AK. Genetic variability, correlation and path analysis of floral, yield and its component traits in CMS and restorer lines of rice (Oryza sativa L.). Bangladesh J Bot. 2014;43(1):45–52. https://doi.org/10.3329/bjb.v43i1.19745
  37. 37. Kobata T, Ishi H, Iwasaki H. A Reduction in spikelet number and fertility causes yield vulnerability in high‐yielding rice. Agron J. 2017;109(1):175–84. https://doi.org/10.2134/agronj2016.05.0274
  38. 38. Piyari J, Saraswathi R, Kumari BM, Kumaresan D, Balasubramani V, Gopalakrishnan C. Molecular marker and test cross information aid selective advancement of F4 generation of CB174R/Azucena: An inter sub-specific cross in rice for restorer development. Int J Plant Soil Sci. 2023;35(13):240–53. https://doi.org/10.9734/ijpss/2023/v35i133091
  39. 39. Chin JH, Chu SH, Jiang W, Cho YI, Basyirin R, Brar DS, et al. Identification of QTLs for hybrid fertility in inter-subspecific crosses of rice (Oryza sativa L.). Genes Genomics. 2011;33:39–48. https://.doi.org/10.1007/s13258-010-0100-z
  40. 40. Marathi B, Guleria S, Mohapatra T, Parsad R, Mariappan N, Kurungara VK, et al. QTL analysis of novel genomic regions associated with yield and yield related traits in new plant type based recombinant inbred lines of rice (Oryza sativa L.). BMC Plant Biol. 2012;12:1–19. https://doi.org/10.1186/1471-2229-12-137
  41. 41. Sruthi K, Divya B, Senguttuvel P, Revathi P, Kemparaju KB, Koteswararao P, et al. Evaluation of genetic diversity of parental lines for development of heterotic groups in hybrid rice (Oryza sativa L.). J Plant Biochem Biotechnol. 2020;29:236–52. https://doi.org/10.1007/s13562-019-00529-9
  42. 42. Sruthi K, Eswari KB, Damodhar Raju C, Sheshu Madhav M, Dhandapani A, Senguttuvel P, et al. Identification of stable restorer lines developed through inter‐sub‐specific hybridization in rice (Oryza sativa L.) using multi‐trait stability index. Plant Breed. 2024;143(1):105–19. https://doi.org/10.1111/pbr.13097
  43. 43. Amudha K. Multivariate diversity analysis of yield and yield components in pearl millet [Pennisetum glaucum (L.) R. Br]. Bangladesh J Bot. 2024;53(4):1035–42. https://doi.org/10.3329/bjb.v53i4.78633
  44. 44. Tejaswini KLY, Manukonda S, Kumar BR, Rao PVR, Raju SK. Application of principal component analysis for rice F5 families characterization and evaluation. Emergent Life Sci Res. 2018;4:72–84. https://doi.org/10.31783/elsr.2018.417284
  45. 45. Hasan M, Kulsum U, Rahman M, Chowdhury M, Chowdhury A. Genetic diversity analysis of parental lines for hybrid development in rice (Oryza sativa L.). Bangladesh Journal of Agricultural Research. 2013 Apr 2;37(4):617–24. https://doi.org/10.3329/bjar.v37i4.14386
  46. 46. Tuhina-Khatun Mst, Hanafi MM, Rafii Yusop M, Wong MY, Salleh FM, Ferdous J. Genetic variation, heritability, and diversity analysis of upland rice (Oryza sativa L.) genotypes based on quantitative traits. Biomed Res Int. 2015;2015:1–7. https://doi.org/10.1155/2015/290861
  47. 47. Christina GR, Thirumurugan T, Jeyaprakash P, Rajanbabu V. Principal component analysis of yield and yield related traits in rice (Oryza sativa L.) landraces. Electron J Plant Breed. 2021;12(3):907–11. https://doi.org/10.37992/2021.1203.125
  48. 48. Choudhary M, Kumar B, Singh P, Sharma M. Diversity analysis for yield and its contributing traits in rice germplasm (Oryza sativa L.) using principal component analysis approach. Int J Environ Clim. 2022;12(9):143–50. https://doi.org/10.9734/ijecc/2022/v12i930748
  49. 49. Mvuyekure SM, Sibiya J, Derera J, Nzungize J, Nkima G. Application of principal components analysis for selection of parental materials in rice breeding. J Genet Genomic Sci. 2018;3(10):10–24966. https://doi.org/10.24966/GGS-2485/100010

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