Czech J. Genet. Plant Breed., 2026, 62(3):146-156 | DOI: 10.17221/133/2025-CJGPB

Genetic diversity of selected Malaysian rice accessions using microsatellite markersOriginal Paper

Shahril Ab Razak ORCID...1*, Alny Marlynni Abd Majid ORCID...2, Rahiniza Kamaruzaman ORCID...1, Norliza Abu Bakar2, Rabiatul Adawiah Zainal Abidin ORCID...2, Yun Shin Sew2, Norfarhan Mohd-Assaad ORCID...3,4, Asmuni Mohd Ikmal ORCID...1, Noraziyah Abd Aziz Shamsudin ORCID...1
1 Department of Biological Sciences and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia
2 Agri-omic and Bioinformatic Programme, Biotechnology & Nanotechnology Research Centre, MARDI Headquarters, Serdang, Malaysia
3 Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia
4 Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, Bangi, Malaysia

Genetic diversity of plant genetic resources provides the foundation for breeding programmes aimed at developing high-yielding rice varieties with tolerance to biotic and abiotic stresses. Given the abundance of available genetic resources, efficient approaches for their characterisation are essential. In this study, 182 Malaysian rice accessions representing different maturity groups were characterised using 20 polymorphic simple sequence repeat (SSR) markers. The analysis identified 183 alleles, ranging from two (RM507) to 22 (RM154), with an average of 9.15 alleles per locus. Observed and expected heterozygosity ranged from 0.000 to 0.506 and 0.319 to 0.864, respectively. Polymorphism information content (PIC) values ranged from 0.2744 (RM495) to 0.8475 (RM154), with an average of 0.6216 per locus. Unweighted pair group method with arithmetic mean (UPGMA) analysis revealed two major groups. In general, the accessions were clustered according to their adaptive ecosystem type, with most lowland varieties, including lowland breeding lines (92.4%), assigned to Group I, whereas most upland varieties (86.7%) belonged to Group II. This grouping pattern was supported by STRUCTURE analysis, which identified K = 2 as the optimal number of clusters, indicating that the studied accessions were structured into two major genetic groups. Principal coordinate analysis (PCoA) further supported this grouping pattern, with the first three axes explaining 39.63% of the total variation. Analysis of molecular variance (AMOVA) showed that 31% of the total variation occurred among populations, 63% among accessions, and 6% within accessions. The results also indicated the possible presence of duplicate accessions within the collection. This study provides valuable insights for future breeding programmes aimed at developing high-yielding rice varieties with a broad genetic base and supports the effective management and conservation of rice genetic resources.

Keywords: genetic variability; population structure; SSR marker; traditional rice

Received: December 29, 2025; Revised: April 28, 2026; Accepted: April 28, 2026; Prepublished online: June 10, 2026; Published: June 18, 2026  Show citation

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Ab Razak S, Abd Majid AM, Kamaruzaman R, Abu Bakar N, Zainal Abidin RA, Sew YS, et al.. Genetic diversity of selected Malaysian rice accessions using microsatellite markers. Czech Journal of Genetics and Plant Breeding. 2026;62(3):146-156. doi: 10.17221/133/2025-CJGPB.
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    References

    1. Ab Razak S., Azman N., Kamaruzaman R., Saidon S.A., Yusof M.F.M., Ismail S.N., Jaafar M.A., Abdullah N. (2020): Genetic diversity of released Malaysian rice varieties based on single nucleotide polymorphism markers. Czech Journal of Genetics and Plant Breeding, 56: 62-70. Go to original source...
    2. Arif I.A., Khan H.A., Shobrak M., Al Homaidan A.A., Al Sadoon M., Al Farhan A.H., Bahkali A.H. (2010): Interpretation of electrophoretograms of seven microsatellite loci to determine the genetic diversity of the Arabian Oryx. Genetic and Molecular Research, 9: 259-265. Go to original source... Go to PubMed...
    3. Beena R., Sowmiya S., Visakh R.L., Shelvy S., Sasmita B., Sah R.P. (2025): Unravelling genetic mechanisms for heat tolerance in rice landraces of Kerala, India. Euphytica, 221: 51. Go to original source...
    4. Botstein D., White R.L., Skolnick M., Davis R.W. (1980): Construction of a genetic linkage map in man using restriction fragment length polymorphisms. American Journal of Human Genetics, 32: 314.
    5. Chakraborty R., Kimmel M., Stivers D.N., Davison L.J., Deka R. (1997): Relative mutation rates at di-, tri-, and tetranucleotide microsatellite loci. Proceedings of the National Academy of Sciences, 94: 1041-1046. Go to original source... Go to PubMed...
    6. Chesnokov Y.V., Kosolapov V.M., Savchenko I.V. (2020): Morphological genetic markers in plants. Russian Journal of Genetics, 56: 1406-1415. Go to original source...
    7. Comertpay G., Baloch F.S., Derya M., Andeden E.E., Alsaleh A., Surek H., Özkan H. (2016): Population structure of rice varieties used in Turkish rice breeding programs determined using simple sequence repeat and inter primer binding site retrotransposon data. Genetic and Molecular Research, 15: gmr.15017158. Go to original source...
    8. Dang X., Giang Tran Thi T., Mawuli Edzesi W., Liang L., Liu Q., Liu E., Wang Y., Qiang S., Liu L., Hong D. (2015): Population genetic structure of Oryza sativa in East and Southeast Asia and the discovery of elite alleles for grain traits. Scientific Reports, 5: 1-13. Go to original source... Go to PubMed...
    9. Dao S., Traoré D., Teketé C., Kassogué A., Coulibaly M., Ouattara O., Goïta O. (2024): Genetic diversity assessment of lowland and upland rice varieties of Mali using microsatellite markers. International Journal of Genetics and Molecular Biology, 16: 1-11. Go to original source...
    10. Evanno G., Regnaut S., Goudet J. (2005): Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Molecular Ecology, 14: 2611-2620. Go to original source... Go to PubMed...
    11. Fufa H., Baenziger P.S., Beecher B.S., Dweikat I., Graybosch R.A., Eskridge K.M. (2005): Comparison of phenotypic and molecular marker-based classifications of hard red winter wheat cultivars. Euphytica, 145: 133-146. Go to original source...
    12. Hanamaratti N.G., Prashanthi S.K., Salimath P.M., Hanchinal R.R., Mohankumar H.D., Parameshwarappa K.G. (2008): Traditional landraces of rice in Karnataka: Reservoirs of valuable traits. Current Science, 94: 242-247.
    13. Kumar S., Stecher G., Tamura K. (2016): MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Molecular Biology and Evolution, 33: 1870-1874. Go to original source... Go to PubMed...
    14. Li Y.L., Liu J.X. (2018): StructureSelector: A web-based software to select and visualize the optimal number of clusters using multiple methods. Molecular Ecology Resources, 18: 176-177. Go to original source... Go to PubMed...
    15. Liu K., Muse S.V. (2005): PowerMarker: An integrated analysis environment for genetic marker analysis. Bioinformatics, 21: 2128-2129. Go to original source...
    16. Mace E.S., Buhariwalla K.K., Buhariwalla H.K., Crouch J.H. (2003): A high-throughput DNA extraction protocol for tropical molecular breeding programs. Plant Molecular Biology Reporter, 21: 459-460. Go to original source...
    17. Merritt B.J., Culley T.M., Avanesyan A., Stokes R., Brzyski J. (2015): An empirical review: Characteristics of plant microsatellite markers that confer higher levels of genetic variation. Applications in Plant Sciences, 3: 1500025. Go to original source... Go to PubMed...
    18. Misman S.N., Zakaria L. (2019): Pathotype identification of rice blast pathogen, Pyricularia oryzae using differential varieties in Peninsular Malaysia. Tropical Life Sciences Research, 30: 181-190. Go to original source...
    19. Mudhale A., Sar P., Kumar J., Bhowmick P.K., Banerjee A., Chakraborty K., Roy S. (2024): Characterization of rice (Oryza sativa L.) landraces from Majuli and surrounding riverine ecologies in Assam: Assessment of morphogenetic variability and submergence tolerance. Plant Breeding, 143: 469-480. Go to original source...
    20. Peakall R.O.D., Smouse P.E. (2006): GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes, 6: 288-295. Go to original source...
    21. Pritchard J.K., Stephens M., Donnelly P. (2000): Inference of population structure using multilocus genotype data. Genetics, 155: 945-959. Go to original source... Go to PubMed...
    22. Rocha S.S., Londe L.C.N., Pimenta S., Cardoso M.M., Goncalves N.P., Gome W.S., Calaes J.G. (2020): Congruence between morphological and molecular markers for genetic diversity analysis applied to forage palm genotypes propagated via bioreactors. Industrial Crops and Products, 147: 112230. Go to original source...
    23. Salgotra R.K., Chauhan B.S. (2023): Genetic diversity, conservation, and utilization of plant genetic resources. Genes, 14: 174. Go to original source...
    24. Schuelke M. (2000): An economic method for the fluorescent labeling of PCR fragments. Nature Biotechnology, 18: 233-234. Go to original source... Go to PubMed...
    25. Singh N., Choudhury D.R., Tiwari G., Singh A.K., Kumar S., Srinivasan K., Tyagi R.K., Sharma A.D., Singh N.K., Singh R. (2016): Genetic diversity trend in Indian rice varieties: An analysis using SSR markers. BMC Genetics, 17: 1-13. Go to original source... Go to PubMed...
    26. Singh S., Gupta S.K., Thudi M., Das R.R., Vemula A., Garg V., Yadav D.V. (2018): Genetic diversity patterns and heterosis prediction based on SSRs and SNPs in hybrid parents of pearl millet. Crop Science, 58: 2379-2390. Go to original source...
    27. Thomson M.J., Polato N.R., Prasetiyono J., Trijatmiko K.R., Silitonga T.S., McCouch S.R. (2009): Genetic diversity of isolated populations of Indonesian landraces of rice (Oryza sativa L.) collected in East Kalimantan on the island of Borneo. Rice, 2: 80-92. Go to original source...
    28. Van Oosterhout C., Hutchinson W.F., Wills D.P., Shipley P. (2004): MICRO-CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Molecular Ecology Notes, 4: 535-538. Go to original source...
    29. Venkateshwarlu C., Kole P.C., Singh A.K., Paul P.J., Sinha P., Singh V.K., Kumar A. (2025): Mapping genomic regions for reproductive stage drought tolerance in rice from exotic landrace-derived population. Frontiers in Plant Science, 15: 1495241. Go to original source... Go to PubMed...
    30. Vieira M.L.C., Santini L., Diniz A.L., Munhoz C.D.F. (2016): Microsatellite markers: What they mean and why they are so useful. Genetics and Molecular Biology, 39: 312-328. Go to original source... Go to PubMed...
    31. Villa T.C.C., Maxted N., Scholten M., Ford-Lloyd B. (2005): Defining and identifying crop landraces. Plant Genetic Resources, 3: 373-384. Go to original source...
    32. Yeh F.C., Yang R.C., Boyle T.J., Ye Z., Xiyan J.M. (2000): PopGene32. Microsoft Windows-based free software for population genetic analysis. Release 1.32. Available at https://www.scienceopen.com/book?vid=2d45ad78-b140-4b66-b80f-2c9f513ec997
    33. Zeven A.C. (1998): Landraces: A review of definitions and classifications. Euphytica, 104: 127-139. Go to original source...
    34. Zhang L.N., Cao G.L., Han L.Z. (2013): Genetic diversity of rice landraces from lowland and upland accessions of China. Rice Science, 20: 259-266. Go to original source...
    35. Zhu Y., Chen H., Fan J., Wang Y., Li Y., Chen J., Fan J., Yang S., Hu L., Leung H., Mew T.W. (2000): Genetic diversity and disease control in rice. Nature, 406: 718-722. Go to original source... Go to PubMed...
    36. Zurn J.D., Nyberg A., Montanari S., Postman J., Neale D., Bassil N. (2020): A new SSR fingerprinting set and its comparison to existing SSR-and SNP-based genotyping platforms to manage Pyrus germplasm resources. Tree Genetics & Genomes, 16: 72. Go to original source...

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