Assessment of genetic diversity in medium-duration rice (Oryza sativa L.) genotypes using clustering and principal component analysis

*Article not assigned to an issue yet

, , , , ,


Research Articles | Published:

E-ISSN: 2229-4473.
Website: www.vegetosindia.org
Pub Email: contact@vegetosindia.org
DOI: 10.1007/s42535-026-01729-0
First Page: 0
Last Page: 0
Views: 90

Keywords: Crop breeding, Genetic diversity, Hierarchical cluster analysis, Principal component analysis, Rice


Abstract


Medium-duration rice genotypes are preferred in India for their efficient resource use, stress avoidance, and better fit in intensive cropping systems. Breeding for medium-duration, high-yielding, climate-resilient cultivars is crucial to enhance productivity and ensure sustainable rice production. With this aim, the present study was conducted to evaluate 85 genetically diverse medium-duration rice genotypes in an alpha lattice design with two replications during Kharif-2023 at the Agricultural Research Farm of Banaras Hindu University, Varanasi, Uttar Pradesh, India. The observations were recorded for 24 characters to determine the relationship and genetic divergence among the individuals by hierarchical cluster analysis and principal component analysis. Results from cluster analysis grouped 85 genotypes into five distinct clusters with minimum genotypes in Cluster III (5 genotypes) and maximum in Cluster IV (27 genotypes). The genotypes in Cluster II (intra-cluster distance: 5.928) showed maximum divergence. The maximum inter-cluster Euclidean distance was observed between Clusters II and III, followed by Clusters I and III, giving a scope for selection of parents for a hybridization programme from these clusters to realize high genetic variation and novel combinations for yield increment. Results from the PCA analysis revealed that the first nine principal components with eigenvalues greater than 1, explained a cumulative variance of 85.6%. PC1, contributing 20.8% of the total variance, was primarily associated with biomass, plot yield, and straw yield. Genotypes such as HL-19-WS-33 A-401, BHU-STM-2023-39, BHU-STM-2023-4, BHU-STM-2023-29, BHU-STM-2023-26, and HL-18-WS-39-24-25 exhibited superior performance for biomass, straw yield, plot yield, kernel length before cooking and test weight, making them valuable donors for yield improvement. Overall, the study offered valuable insights into the genetic diversity of medium-duration rice genotypes, identifying diverse parental lines suitable for hybridization and the development of advanced breeding lines to enhance genetic gain. It also highlighted key principal components that can be strategically targeted in future rice improvement programs.

Crop breeding, Genetic diversity, Hierarchical cluster analysis, Principal component analysis, Rice


References


Awad-Allah MMA, Shafie WWM, Alsubeie MS, Alatawi A, Safhi FA, ALshamrani SM, Albalawi DA, Al-Amrah H, Alshehri D, Alshegaihi RM, Basahi MA, Masrahi AS (2022) Utilization of genetic resources, genetic diversity and genetic variability for selecting new restorer lines of rice (Oryza sativa L). Genes 13(12):2227. https://doi.org/10.3390/genes13122227


Barik SR, Moharana A, Pandit E, Behera A, Mishra A, Mohanty SP, Mohapatra S, Sanghamitra P, Meher J, Pani DR, Bhadana VP, Datt S, Sahoo CR, Pradhan SK (2023) Transfer of stress resilient QTLs and panicle traits into the rice variety, Reeta through classical and marker-assisted breeding approaches. Int J Mol Sci 24(13):10708. https://doi.org/10.3390/ijms241310708


Chandraker P, Sharma B, Parikh M, Saxena RR (2024) Assessment of genetic diversity in aromatic short grain rice (Oryza sativa L.) genotypes using pca and cluster analysis. Int J Plant Soil Sci 36(5):82–94. https://doi.org/10.9734/ijpss/2024/v36i54504


Deepika K, Lavuri K, Rathod S, Yeshala CM, Jukanti AK, Reddy SN, Lv SR, Badri J (2021) Multivariate analysis of geographically diverse rice germplasm for genetic improvement of yield, dormancy and shattering-related traits. Plant Genet Resour 19(2):144–152. https://doi.org/10.1017/S1479262121000186


Dhakal A, Pokhrel A, Sharma S, Poudel A (2020) Multivariate analysis of phenotypic diversity of rice (Oryza sativa L.) landraces from lamjung and tanahun districts. Nepal Int J Agron 2020(1):8867961. https://doi.org/10.1155/2020/8867961


Ekbiç E, Tırınk C (2024) Genetic diversity of white cabbage (Brassica oleracea var. Capitata subvar. Alba) inbreed lines using SRAP markers. Black Sea J Agr 7(5):429. https://doi.org/10.47115/bsagriculture.1509098


Gebrie G, Abebe D (2022) Determining the level of genotypic variability of upland rice genotypes using cluster analysis. Int J Schol Res Rev 1(01):001–008. https://doi.org/10.56781/ijsrr.1.1.0021


Glauber Joseph W, Mamun A (2024) Global rice market: Current Outlook and future prospects. IFPRI Discussion Paper 2310. Washington, DC: International Food Policy Research Institute. https://hdl.handle.net/10568/168523


Gu Z, Han B (2024) Unlocking the mystery of heterosis opens the era of intelligent rice breeding. Plant Physiol 196(2):735–744. https://doi.org/10.1093/plphys/kiae385


Islam MA, Hasan MM, Hossain MA, Haque MA, Siddique MNA, Shamsuddoha M, Habib MA, Risha SS (2024) Identification and evaluation of high-performing advanced germplasm of rice through morphological and breeding value analysis. Discov Agric 2:126. https://doi.org/10.1007/s44279-024-00143-x


Jena BK, Chandra KP, Pradhan SK, ranjan Barik S, Mohanty SP, Moharana A, Sahoo A, Pandit E, Das SR (2024) Principal component and cluster analysis in grain appearance, milling and cooking quality traits in rice (Oryza sativa L.): Principal components of rice grain quality traits. ORYZA-An Int J Rice 61(4):348–358. https://doi.org/10.35709/ory.2024.61.4.9


Juliano BO (1992) Structure, chemistry, and functional properties of rice grain and its fractions. Cereal Foods World 37:772–779


Kassambara A, Mundt F (2020) factoextra: Extract and Visualize the Results of Multivariate Data Analyses. R Package Version 1.0.7. https://CRAN.R-project.org/package=factoextra


Krishna VA, Bisane RD, Poudel AP, Singh A, Singh SK (2024) Application of next-generation sequencing technology for rice improvement. In: Singh A, Singh SK, Shrestha J (eds) Climate-Smart Rice Breeding. Springer, Singapore, pp 323–349. https://doi.org/10.1007/978-981-97-7098-4_13


Lê S, Josse J, Husson F (2008) FactoMineR: an R package for multivariate analysis. J Statis Soft 25(1):1–18. https://doi.org/10.18637/jss.v025.i01


McNally KL, Henry A (2023) Tools for using the International Rice Genebank to breed for climate-resilient varieties. PLoS Biol 21(7):e3002215. https://doi.org/10.1371/journal.pbio.3002215


Mondal S, Pradhan P, Das B, Kumar D, Paramanik B, Yonzone R, Barman R, Saha D, Karforma J, Basak A, Dey P (2024) Genetic characterization and diversity analysis of indigenous aromatic rice. Heliyon 10(10):e28763. https://doi.org/10.1016/j.heliyon.2024.e31232


Mst F, Hossain M, Kang SG, Matin M (2023) Genetic variation, population structure, and marker-trait association of rice (Oryza sativa L.) cultivars using morphological characteristics and molecular markers. https://doi.org/10.21203/rs.3.rs-2813496/v1


Murtagh F, Legendre P (2014) Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion? J Classif 31(3):274–295. https://doi.org/10.1007/s00357-014-9161-z


Nivedha R, Manonmani S, Kalaimagal T, Raveendran M, Kavitha S (2024) Assessing the genetic diversity of parents for developing hybrids through morphological and molecular markers in rice (Oryza sativa L). Rice 17(1):17. https://doi.org/10.1186/s12284-024-00691-2


Patel A, Sao A, Nair S, Mandavi J, Tamrakar N (2022) Principal component analysis for eight quantitative traits in 55 indigenous rice germplasm (Oryza sativa L). Pharma Innov J 11(9):1201–1206. https://www.thepharmajournal.com/archives/?year=2022&vol=11&issue=9&ArticleId=15467


Pathak H, Tripathi R, Jambhulkar NN, Bisen JP, Panda BB (2020) Eco-regional-based rice farming for enhancing productivity, profitability and sustainability. NRRI Research Bulletin No. 22, ICAR National Rice Research Institute, Cuttack 753006, Odisha, India. 6–30. https://doi.org/10.13140/RG.2.2.26486.42563


R Studio Team (2024) RStudio: Integrated Development Environment for R. Version 2024.12.1 + 563 (Kousa Dogwood). Posit Software, PBC


Rahangdale S, Singh Y, Upadhyay P, Koutu GK (2021) Principal component analysis of JNPT lines of rice for the important traits responsible for yield and quality. Ind J Genet Plant Breed 81(01):Article01. https://doi.org/10.31742/IJGPB.81.1.14


Revelle W (2023) Package ‘psych’. Compr R archive Netw 337(338):161–165


Salunkhe H, Kumar A, Krishna B, Talekar N, Pawar P (2023) Genetic diversity and principal component analysis for yield and its component trait in rice. Biol Forum–An Int J 15:102–107


Sar P, Kole PC (2023) Principal component and cluster analyses for assessing agro-morphological diversity in rice. Oryza 60(1):117–124. https://doi.org/10.35709/ory.2023.60.1.2


Sharif S, Javaid RA, Majeed A, Ahmed MS, Sani QA, Siddique F, Arshad M, Ali N (2023) Genetic diversity estimation of rice genotypes based on morphological and quality parameters through principal component analysis. Pak J Agrl Res 36(3):207–216. https://doi.org/10.17582/journal.pjar/2023/36.3.207.216


Shoba D, Vijayan R, Robin S, Manivannan N, Iyanar K, Arunachalam P, Nadarajan N, Pillai MA, Geetha S (2019) Assessment of genetic diversity in aromatic rice (Oryza sativa L.) germplasm using PCA and cluster analysis. Electron J Plant Breed 10(3):1095–1104. https://doi.org/10.5958/0975-928X.2019.00140.6


Singh A, Rami E, Upadhyay P, Gangawane AK (2023) The Impact of Climate Change on Crop Production and Combat Strategies. Climate Change and Sustainable Development. CRC. DOI: https://doi.org/10.1201/9781003205548-6


Singh A, Singh DK, Singh SK, Singh VK, Kumar A (2025) Multivariate stability analysis to select elite rice (Oryza sativa L.) genotypes for grain yield, zinc and Iron. Sci Rep 15:39586. https://doi.org/10.1038/s41598-025-11748-7


Singh SK, Sirohi V, Bisane RD, Krishna VA, Poudel AP, Singh A (2026) Deciphering genetic variability, correlation and path analysis for yield and yield related traits in early rice (Oryza sativa) genotypes under the direct-seeded rice (DSR) system. Agric Res. https://doi.org/10.1007/s40003-025-00917-9


Thang NB (2022) Genetic divergence of cultivated rice varieties in north vietnam for grain quality traits using D2 cluster analysis. Viet J Agrl Sci 5(2):1435–1444. https://doi.org/10.31817/vjas.2022.5.2.01


Tiwari S, Singh Y, Upadhyay PK, Koutu GK (2022) Principal component analysis and genetic divergence studies for yield and quality-related attributes of rice restorer lines. Ind J Genet Plant breed 82(01):94–98. https://doi.org/10.31742/IJGPB.82.1.13




 


Author Information


Department of Genetics and Plant Breeding, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, India