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Genetic diversity of rice mutant genotypes using multivariate methods | ||
| Journal of Plant Physiology and Breeding | ||
| مقاله 10، دوره 8، شماره 1، شهریور 2018، صفحه 111-124 اصل مقاله (319.52 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22034/jppb.2018.9513 | ||
| نویسنده | ||
| Peyman Sharifi | ||
| Department of Agronomy and Plant Breeding, Rasht Branch, Islamic Azad University, Rasht, Iran | ||
| چکیده | ||
| Genetic diversity among 64 rice genotypes including 56 M5 mutants and 8 check varieties was studied using multivariate analysis. The experimental materials were evaluated during growing season of 2013-14 at the experimental field of Rice Research Institute of Iran (RRII), Rasht, Iran. The field experiment was arranged in a randomized complete block design with three replications. With respect to the positive and significant partial regression coefficients and direct effects of number of panicles per plant and number of spikelet per panicle, it could be stated that increasing the amount of these traits will cause an increase in grain yield. The dendrogram from cluster analysis divided all 64 rice genotypes into three main groups. Maximum distance existed between clusters II and III, therefore the genotypes selected from these clusters could be used in hybridization programs. The first principal component included plant height, internode length, number of panicles per plant, panicle length, panicle weight, number of filled grains, grain productivity, 100 grain weight, grain width and grain yield. Second principal component included days to flowering, number of panicles per plant and number of unfilled grains. Two-dimensional plot based on the first two principal components indicated the existence of differences among rice genotypes under study. The presence of vast diversity among 64 rice genotypes by cluster analysis was also confirmed partly by the three-dimensional graph of three principal components. In conclusion, the studied genotypes represent a rich source of genetic diversity and could be useful in rice breeding programs. The crosses G53 × G39 and G62 × G11 will be useful for hybridization, because the parental genotypes were identified as being most divergent. | ||
| کلیدواژهها | ||
| Cluster analysis؛ Genetic diversity؛ Mutant؛ Principal component analysis؛ Regression analysis؛ Rice | ||
| مراجع | ||
|
Adebisi MA, Okelola FS, Ajala MO, Kehinde TO, Daniel IO and Ajani OO, 2013. Evaluation of variations in seed vigour characters of west African rice (Oryza sativa L.) genotypes using multivariate technique. American Journal of Plant Sciences 4: 356-363.
Ahmadikhah A, Nasrollanejad S and Alishah O, 2008. Quantitative studies for investigating variation and its effect on heterosis of rice. International Journal of Plant Production 2: 297-308.
Akter A, Hasan MJ, Paul A, Motalib AK and Hossain MK, 2009. Selection of parent for improvement of restorer line in rice (Oryza sativa L.). Saarc Journal of Agriculture 7: 43-50.
Bose LK and Pradhan SK, 2005. Genetic divergence in deep water rice genotypes. Journal of Central European Agriculture 6: 635-640.
Chaturvedi HP and Maurya DM, 2005. Genetic divergence analysis in rice (Oryza sativa L.). Advances in Plant Science 18(1): 349-353.
Chauhan VS and Singh BB, 1982. Heterosis and genetic variability in relation to genetic divergence in soybean. Indian Journal of Genetics and Plant Breeding 42(2): 324-328.
Cowen NM and Frey KJ, 1987. Relationships between three measures of genetic distance and breeding methods in oat (Avena sativa L.). Genome 29: 97-106.
Domingo C, Andrés F and Talón M, 2007. Rice cv. Bahia mutagenized population: a new resource for rice breeding in the Mediterranean basin. Spanish Journal of Agricultural Research 5(3): 341-347.
Elyasi S, Abdollahi S and Mollasadeghi V, 2014. Cluster analysis of 24 genotypes of modified rice according to qualitative and quantitative traits. Bulletin of Environment, Pharmacology and Life Sciences 3(8): 109-111.
Garg P, Pandey DP and Kaushik RP, 2011. Genetic divergence for yield and quality traits in rice (Oryza sativa L.). Journal of Rice Research 4: 1-5.
Habib SH, Bashar MK, Khalequzzaman M, Ahmed MS and Rashid ESMH, 2005. Genetic analysis and morphophysiological selection criteria for traditional biroin Bangladesh rice germplasms. Journal of Biological Sciences 5(3): 315-318.
Jeffers JNR, 1967. Two case studies in the application of principal component analysis. Applied Statistics 16: 225-236.
Jobson JD, 1992. Applied Multivariate Data Analysis. Volume II: Categorical and Multivariate Methods. Springer Verlag, New York, USA.
Khalequzzaman M, Islam MZ, Akter K and Bashar MK, 2008. Genetic diversity in local rainfed lowland rice (Oryza sativa L.) in Bangladesh. Bangladesh Journal of Plant Breeding and Genetics 21(1): 49-54.
Khatun MT, Hanafi MM, Yusop MR, Wong MY, Salleh FM and Ferdous J, 2015. Genetic variation, heritability and diversity analysis of upland rice (Oryza sativa L.) genotypes based on quantitative traits. BioMed Research International, pp. 1-7. http://dx.doi.org/10.1155/2015/290861.
Kiani G, 2012. Diversity assessment among some restorer lines using agronomic traits in rice (Oryza sativa L.). Biharean Biologist 6: 1-4.
Kiani G, 2013. Heritability and diversity analysis of quantitative traits in rice. Agriculturae Conspectus Scientificus 78(2): 113-117.
Kim MS, Baek SA, Park SY, Baek SH, Lee SM, Ha SH, Lee YT, Choi J, Im KH and Kim JK, 2016. Comparison of the grain composition in resveratrol-enriched and glufosinate-tolerant rice (Oryza sativa) to conventional rice using univariate and multivariate analysis. Journal of Food Composition and Analysis 52: 58-67.
Lasalita-Zapico FC, Namocatcat JA and Carino-Turner JL, 2010. Genetic diversity analysis of traditional upland rice cultivars in Kihan, Malapatan, Sarangani Province, Philippines using morphometric markers. Philippine Journal of Science 139(2): 177-180.
Leilah AA and Al-Khateeb SA, 2005. Statistical analysis of wheat yield under drought conditions. Journal of Arid Environment 61: 483-496.
Li G, Zhang J, Yang C, Song Y, Zheng C, Wang S, Liu Z and Ding Y, 2014. Optimal yield-related attributes of irrigated rice for high yield potential based on path analysis and stability analysis. The Crop Journal 2: 235-243.
Mahalanobis PC, 1936. On the generalized distance in statistics. Proceedings of the National Institute of Science 2: 49-55.
Mazid MS, Rafii MY, Hanafi MM, Rahim HA and Latif MA, 2013. Genetic variation, heritability, divergence and biomass accumulation of rice genotypes resistant to bacterial blight revealed by quantitative traits and ISSR markers. Physiologia Plantarum 149(3): 432-447.
Minitab, 2010. Minitab 16 Statistical Software. Minitab Inc., State College, Pennsylvania, USA.
Moghaddam M, Mohammadi SA and Aghaei M, 1994. Introduction to Multivariate Statistical Methods. Pishtaze Elm Press, Tabriz, Iran (In Persian).
Peeters JP and Martinelli JA, 1989. Hierarchical cluster analysis as a tool to manage variation in germplasm collections. Theoretical and Applied Genetic 78: 42-48.
Rabara RC, Ferrer MC, Diaz CL, Newingham MCV and Romero GO, 2014. Phenotypic diversity of farmers’ traditional rice varieties in the Philippines. Agronomy 4: 217-241.
Rahman MM, Rasul MG, Bashar MK, Syed MA and Islam MR, 2011. Parent selection for transplanted Aman rice breeding by morphological, physiological and molecular diversity analysis. Libyan Agriculture Research Center Journal International 2: 26-28.
Sabesan T, Saravanan K and Anandan A, 2009. Genetic divergence analysis for certain yield and quality traits in rice (Oryza sativa L.) grown in irrigated saline low land of Annamalainagar, south India. Journal of Central European Agriculture 10: 405-410.
Sabouri H, Rabiei B and Fazlalipour M, 2008. Use of selection indices based on multivariate analysis for improving grain yield in rice. Rice Science 15(4): 303-310.
Sarawgi, AK and Bisne R, 2007. Studies on genetic divergence of aromatic rice germplasm for agro- morphological and quality characters. Oryza 44(1): 74-76.
SES, 2002. Standard Evaluation System for Rice. IRRI, Philippines.
Sharifi P, Dehghani H, Mumeni A and Moghaddam M, 2013. Genetic relations of some of rice agronomic traits with grain yield using multivariate statistical methods. Iranian Journal of Field Crop Science, 44(1):179-169 (In Persian with English abstract).
Singh RS and Bains SS, 1968. Genetic divergence for ginning outturn and its components in upland cotton (Gosipium hirsutum L.) varieties. Indian Journal of Genetics 28: 262-268.
SPSS, 2007. SPSS 17. SPSS users guide. SPSS Inc., Chicago, IL, USA.
Verma VS and Mehta RK, 1976. Genetic divergence in lucerne. Journal of Maharastra Agricultural University 1: 23-28.
Worede F, Sreewongchai T, Phumichai C and Sripichitt P, 2014. Multivariate analysis of genetic diversity among some rice genotypes using morpho-agronomic traits. Journal of Plant Sciences 9(1): 14-24.
Yan W and Kang MS, 2003. GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists and Agronomists. CRC Press, Boca Raton, FL, USA.
Zhang CH, Li JZ, Zhu Z, Zhang YD, Zhao L and Wang CL, 2010. Cluster analysis on Japonica rice (Oryza sativa L.) with good eating quality based on SSR markers and phenotypic traits. Rice Science 17(2): 111-121.
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