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Nilesh Talekar Rahul Singh S.P. Singh Satyendra Mankesh Kumar

Abstract

Rice is the richest source of starch and carbohydrates but is deficient in major micronutrients such as iron and zinc. Slight enrichment with these micronutrients could help combat malnutrition. For a successful plant breeding program, genetic variability is crucial. Thus, the research aimed to analyze the description of statistics and transgressive segregation among the nutritional and agronomical traits in the F2 rice population. In this context, 190 progenies from the F2 population and parents were sown in Kharif 2020. Ten agronomical and two nutritional traits (grain iron and zinc content) were recorded from each genotype of the F2 population. All the recorded data were subjected to descriptive analysis and transgressive segregants were recorded for grain iron and zinc content. Descriptive analysis revealed positive skewness for the number of effective tillers per plant (0.998), grain length-breadth ratio (0.256), thousand-grain weight (0.875), grain zinc content (0.232), and grain yield per plant (1.460). Negative skewness was recorded for days to fifty per cent flowering (-2.805), plant height (-0.396), panicle length (-0.150), grain breadth (-0.335), and grain iron content (-0.356). The number of filled grains per panicle, grain length breadth ratio, grain zinc, and iron content exhibited the platykurtic nature of the distribution curve. Concerning transgressive segregants of nutritional traits, ten were observed for grain zinc content and thirty for grain iron content in the F2 rice population. These transgressive segregants for grain zinc and iron content might be used for developing advanced breeding lines, and skewness and kurtosis provide necessary genetic information for gene interaction.


 

Article Details

Article Details

Keywords

Kurtosis, Micronutrients, Rice, Skewness , Transgressive segregants

References
Baisakh, N., Yabes, J., Gutierrez, A., Mangu, V., Ma, P., Famoso, A., & Pereira, A. (2020). Genetic mapping identifies consistent quantitative trait loci for yield traits of rice under greenhouse drought conditions. Genes, 11(1), 62. doi:10.3390/genes11010062
Bashir, K., Nagasaka, S., Itai, R. N., Kobayashi, T., Takahashi, M., Nakanishi, H., … Nishizawa, N. K. (2007). Expression and enzyme activity of glutathione reductase is upregulated by Fe-deficiency in graminaceous plants. Plant Molecular Biology, 65(3), 277–284. doi:10.1007/s11103-007-9216-1
Bhat, R., Singh, A. K., Salgotra, R. K., Sharma, M., Bagati, S., Hangloo, S., … Mushtaq, M. (2018). Statistical description, genetic variability, heritability and genetic advance assessment for various agronomical traits in F2 population of rice (Oryza sativa L). Oryza Sativa L.). Journal of Pharmacognosy and Phytochemistry, 7(3), 985–992.
Calayugan, M., Formantes, A. K., & Amparado, A. (2020). Genetic Analysis of Agronomic Traits and Grain Iron and Zinc Concentrations in a Doubled Haploid Population of Rice (Oryza sativa L).  Sci Rep, 10, 2283 (2020). https://doi.org/10.1038/s41598-020-59184-z.
Choo, T. M., & Reinbergs, E. (1982). Analysis of skewness and kurtosis for detecting gene interaction in a double haploid population. Crop Science, 22, 231–235.
Fisher, R. A. (2021). Statistical tables for biological, agricultural, and medical research. Hassell Street Press.
Kasanaboina Krishna, Y. C., Mohan, L., Krishna, G., & Parimala, R. (2022). Multivariate analysis-based prediction of phenotypic diversity associated with yield and yield component traits in germplasm lines of rice (Oryza sativa L). Electronic Journal of Plant Breeding, 13(3), 764–771.
Kiran, K. K. (2012). Genetic variability for grain yield, its components and inheritance of resistance to BPH in two F2 populations of rice. Oryza Sativa L.). M. Sc. (Agri.) Thesis, Univ. Agril. Sci. Bangalore
Korada, M., & Majhi, P. K. (2020). Studies on character association and path analysis studies for yield, grain quality and nutritional traits in F2 population of rice (Oryza sativa L). Electronic Journal of Plant Breeding, 11(03), 969–975.
Kumar, J., Jain, S., & Jain, R. K. (2014). Linkage Mapping for Grain Iron and Zinc Content in F2 Population Derived from the Cross between PAU201 and Palman 579 in Rice (Oryza sativa L). Cereal Research Communications, 42(3), 389–400.
Li, H., Pan, Z., He, S., Jia, Y., Geng, X., Chen, B., … Du, X. (2021). QTL mapping of agronomic and economic traits for four F2 populations of upland cotton. Journal of Cotton Research, 4(1). doi:10.1186/s42397-020-00076-y
Mallimar, M., Surendra, P., Patil, B., Satish, T. N., & Jogi, M. (2017). Study the Inheritance of Iron and Zinc in Segregating Population of Rice (Oryza sativa L).  Indian Journal of Pure & Applied Biosciences, 5(5), 888–892.
Midya, A., Saren, B. K., Dey, J. K., Maitra, S., Praharaj, S., Gaikwad, D. J., et al. (2021). Crop establishment methods and integrated nutrient management improve: part II. nutrient uptake and use efficiency and soil health in rice (Oryza sativa l.) field in the lower indo-gangetic plain, India. Agronomy 11 (9), 1894. doi: 10.3390/agronomy11091894
Nirubana V, Vanniarajan C, Aananthi N, Banumathy S, Thiyageshwari S, Ramalingam J (2019) Variability and frequency distribution studies in F2 segregating population of rice with phosphorous starvation tolerance Gene (OsPSTOL 1) introgressed. Int. J. Curr. Microbiol. App. Sci, 8(9), 2620–2628. https://doi.org/10.20546/ ijcmas.201 9.80 9.303
Raza, A., Saher, M.S., Farwa, A. and Ahmad, K.R.S. (2019). Genetic diversity analysis of Brassica species using PCR-based SSR markers. Gesunde Pflanzen, 71(1),1-7
Rani, C. S., Anandakumar, C. R., Raveendran, M., Subramanian, K. S., & Robin, S. (2016). Genetic variability studies and multivariate analysis in F2 segregating populations involving medicinal rice (Oryza sativa L). Int. J. Agril. Sci, 8(15), 1733–1735.
Reddy, M. A., Francies, R. M., Joseph, J., & Kumar, P. S. (2019). Screening of F2 population under higher iron toxic levels of hydroponics in rice. International Journal of Current Microbiology and Applied Sciences, 8(1), 28–36. doi:10.20546/ijcmas.2019.801.004
Robson, D. S. (1956). Applications of the k 4 statistic to genetic variance component analyses. Biometrics, 12(4), 433. doi:10.2307/3001682
Savitha, P., & Kumari, U. (2015). Studies on skewness, kurtosis, and parent progeny regression for yield and its related traits in segregating generations of rice. Oryza, 52(2), 80–86.
Sheshaiah, S., Dushyantha Kumar, B. M., Gangaprasad, S., Gowda, T. H., Hosagoudar, G. N., & Shashidhar, H. E. (2018). Studies on variability and frequency distribution of yield and yield-related traits in F2 population of rice (Oryza sativa L.). International Journal of Current Microbiology and Applied Sciences, 7(09), 2048–2052. doi:10.20546/ijcmas.2018.709.249
Shaikh J Mohiuddin, A., Haque Md, M., Haque, T., & Biswas, P. S. (2020). Genetic Analysis Reveals a Major Effect QTL Associated with High Grain Zinc Content in Rice (Oryza sativa L).  Plant Breeding and Biotechnology, 8(4), 327–340.
Shreffler, J., & Huecker, M. R. (2021). Exploratory data analysis: Frequencies, descriptive statistics, histograms, and boxplots. Accessed October 26, 2021. Retrieved from https://pubmed.ncbi.nlm.nih.gov/32491502/
Singh, S. K., Habde, S., Singh, D. K., Khaire, A., Mounika, K., & Majhi, P. K. (2020). Studies on character association and path analysis studies for yield, grain quality, and nutritional traits in F2 population of rice (Oryza sativa L). Electronic Journal of Plant Breeding, 11(3), 969–975.
Singh, U., & Praharaj, U. (2017). Practical manual Chemical Analysis of Soil and Plant Samples. ICAR-Indian Institute of Pulses Research Kanpur (pp. 48–51). Uttar Pradesh- 208 024, India.
Swamy, B. P. M., Kaladhar, K., Anuradha, K., Batchu, A. K., Longvah, T., & Sarla, N. (2018). QTL analysis for grain iron and zinc concentrations in two O. nivara derived backcross populations. Rice Science, 25(4), 197–207. doi:10.1016/j.rsci.2018.06.003
Wattoo, J. I., Liaqat, S., Mubeen, H., Ashfaq, M., Shahid, M. N., Farooq, A., … Arif, M. (2019). Genetic mapping of grain nutritional profile in rice using basmati derived segregating population revealed by SSRs. International Journal of Agriculture and Biology, 21, 929–935.
Section
Research Articles

How to Cite

Revealing the higher degree statistics and transgressive segregation pattern of nutritional and agronomical traits in the segregating population derived from Samba Mahsuri and Sathi of rice (Oryza sativa). (2025). Journal of Applied and Natural Science, 17(2), 545-553. https://doi.org/10.31018/jans.v17i2.6095