Phenotypic Diversity, Correlation and Principal Component Analysis in a Mutagenized M2F2 Population of Hybrid Rice (Oryza sativa) cv. Arize 6444 Gold
45 / 28
Authors
-
Arjun Kumar Agarwal
BIRSA AGRICULTURAL UNIVERSITY
-
Manigopa Chakraborty
-
Nutan Verma
-
Manoj Kumar Barnwal
-
Sumit Kumar Mishra
-
Shivam Mishra
-
Ankita Mishra
-
Raushan Kumar
-
Fatima Araish
-
Radhe Shyam Yadav
Keywords:
Correlation analysis, M2 F2 population, Mutagenesis, Principal component analysis, RiceAbstract
This study evaluated phenotypic variability, frequency distribution, trait associations, and principal components in an M2F2
population developed from mutagen-treated seeds of hybrid rice (Oryza sativa L.) Arize 6444 Gold. A total of 100 plants were assessed
for ten quantitative traits during Kharif 2024. Descriptive statistics revealed substantial variability for key yield-related traits,
particularly grain yield, filled grains per panicle, and spikelet fertility, with near-normal frequency distributions indicating good scope
for selection. Principal component analysis identified four components explaining 61.46% of total variation, with PC1 largely
associated with panicle architecture and grain-related traits. Correlation analysis showed strong positive associations among primary
branches, secondary branches, filled grains, and spikelet fertility, while grain yield itself showed weak direct correlations with most
component traits owing to their compensatory relationships. Overall, the results identify panicle architecture and grain formation
traits as reliable indirect selection criteria for improving yield in early segregating generations of rice.
Downloads
References
1. Ahloowalia BS, M Maluszynski and K Nichterlein (2004) Global impact of mutation-derived varieties. Euphytica. 135(2): 187- 204.
2. Amri-Tiliouine W, M Laouar, A Abdelguerfi, J Jankowicz-Cieslak, L Jankuloski and BJ Till (2018) Genetic variability induced by gamma rays and preliminary results of low-cost TILLING on M2 generation of chickpea (Cicer arietinum L.). Front. Plant Sci. 9: 1568.
3. Bin Rahman ANMR and J Zhang (2023) Trends in rice research; 2030 and beyond. Food Energy Secur.12(2): e390.
4. Falconer DS and TFC Mackay (1996) Introduction to Quantitative th Genetics, 4 edn. Addison Wesley Longman, Harlow.
5. Jasmin SA, PH Prasanth, S Ramchander, PD Kumar, N Devasena, R Naveenkumar, S Thankappan and NBJ Kingsly (2024) Assessment of variability parameters and diversity of panicle architectural traits associated with yield in rice (Oryza sativa L.). Plant Sci. Today.11(1): 109-118.
6. Jolliffe IT and J Cadima (2016) Principal component analysis; a review and recent developments. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 374(2065): 20150202.
7. Kaiser HF (1960) The application of electronic computers to factor analysis. Educ. Psychol. Meas. 20(1): 141-151.
8. Liu K, K Zhang, Y Zhang, J Cui, Z Li, J Huang, S Li, J Zhang, S Deng, Y Zhang, J Huang, L Ren, Y Chu, H Zhao and H Chen (2024) Optimizing the total spikelets increased grain yield in rice. Agronomy.14(1): 152.
9. Luz VK, SFS Silveira, GM Fonseca, EL Groli, RG Figueiredo, D Baretta, MM Kopp, AM Magalhães Júnior, LC Maia and AC Oliveira (2016) Identification of variability for agronomically important traits in rice mutant families. Bragantia. 75(1): 41- 50.
10. Mba C, R Afza, S Bado and SM Jain (2010) Induced mutagenesis in plants using physical and chemical agents. In: MR Davey and P Anthony (eds) Plant cell culture: essential methods. Wiley, Chichester, pp 111-130.
11. Oladosu Y, MY Rafii, N Abdullah, M Abdul Malek, HA Rahim, G Hussin, M Abdul Latif and I Kareem (2014) Genetic variability and selection criteria in rice mutant lines as revealed by quantitative traits. Sci. World J.2014: 190531.
12. Oladosu Y, MY Rafii, N Abdullah, G Hussin, A Ramli, HA Rahim, G Miah and M Usman (2016) Principle and application of plant mutagenesis in crop improvement; a review. Biotechnol. Equip.30(1): 1-16.
13. Posit Team (2024) RStudio; integrated development environment for R. Posit Software, PBC, Boston, MA. https://www.posit.co/
14. R Core Team (2024) R; a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
15. Ray DK, ND Mueller, PC West and JA Foley (2013) Yield trends are insufficient to double global crop production by 2050. PLoS One.8(6): e66428.
16. Riaz A, T Bibi, T Raza, M Sabar and M Akhter (2018) Evaluation of rice (Oryza sativa L.) advance uniform lines using multivariate analysis. Afr. J. Plant Sci.12(11): 284-289.
17. Sao R, PK Sahu, RS Patel, BK Das, L Jankuloski and D Sharma (2022) Genetic improvement in plant architecture, maturity duration and agronomic traits of three traditional rice landraces through gamma ray-based induced mutagenesis. Plants.11(24): 3448.
18. Sushmitharaj DV, P Arunachalam, C Vanniarajan, J Souframanien and E Subramanian (2019) Comparative efficiency of irradiation and its recurrent mutation on induced variability and population characteristics in rice (Oryza sativa L.). Electron. J. Plant Breed.10(2): 542-551.
19. Sushmitharaj DV, P Arunachalam, C Vanniarajan, J Souframanien and E Subramanian (2020) Multivariate analysis in rice (Oryza sativa L.) mutant families from Anna (R) 4 cultivar. Indian J. Agric. Res.54(6): 769-774.
20. Tejaswini KLY, S Manukonda, R Kumar, PVR Rao, M Lal Ahamed and SK Raju (2018) Application of principal component analysis for rice F5 families characterization and evaluation. Emergent Life Sci. Res.4(1): 72-84.
21. Yuvaraja A, P Gokulvigneswaran, S Suriya Praba and V Nirubana (2019) An assessment of early generation genetic variability and correlation for yield and quality traits in rice (Oryza sativaL.). Oryza.56(4).
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Indian Journal of Plant Genetic Resources
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors are required to submite copyright certificate in original.