PRINCIPAL COMPONENT AND CLUSTER ANALYSIS AS A TOOL IN THE ASSESSMENT OF DIFFERENT WHEAT SPECIES AND THEIR GENOTYPES
Article Sidebar
Main Article Content
Abstract
Determination of genetic relationships and determination of best criteria for selection of lines to be included in future breeding program is an invaluable aid in crop improvement. Various statistical techniques have been used to study diversity among different genotypes. Among these techniques multivariate is most frequently used one for the genetic association of genotypes. The present study was undertaken to screen genetic variation among 95 genotypes of wheat for their morphoagronomic traits. Five quantitative traits: plant height, number of fertile tillers, spike length, number of grains per spike, weight of grain per spike were evaluated by PCA and two-way cluster analysis. Three main principal components were determined explaining 85.75% of the total variation among the genotypes. 37.71% of the variation is explained by PC1 which reflects number of grains per spike, weight of grain per spike. PC2 and PC3 explained 28.19% and 19.85% of the total variance, mostly in relation to number of fertile tillers and the plant height, respectively. Biplot graph revealed strongest positive association between number of grains per spike and weight of grain per spike and negative association between spike length and number of fertile tillers. Two-way cluster analysis resulted with a dendrogram with one solely separated genotype, inferior for most of traits and four main clusters of genotypes defined with wide genetic diversity especially between the groups within the first cluster. From the findings of present study genotypes with high values for specific traits can be directly recommended for general cultivation or to be used in future breeding programs.
Downloads
Article Details
References
Bhandari, H.R., Bhanu. A. N,. & Srivastava, K. (2017) Assessment of genetic diversity in crop plants - an overview. Adv Plants Agric Res,7(3), 279-286.
Day, A. (2012): Package “heatmap.plus”: Heatmap with more sensible behavior. http://cran.r-project.org/web/packages/heatmap.plus/heatmap.plus.pdf
Dos Santos, T. M., Ganança, F., Slaski, J. J., & Pinheiro de Carvalho Miguel, A. A. (2009). Morphological characterization of wheat genetic resources from the Island of Madeira, Portugal. Genetic Resources Crop Evolution, 56, 363-375.
Goel, S., Humaraswamy, H.H., Grewal, S., Singh, K., Jaat, S.R., & Singh, K.N. (2015). Morphological and agronomical characterization of common wheat landraces (Triticum aestivum L) collected from different regions of India. International Journal of Current Research and Academic Review, 11 (3), 14 - 23.
Govindaraj, M., Vetriventhan, M., & Srinivasan M. (2015). Importance of genetic diversity assessment in crop plants and its recent advances: an overview of its analytical perspectives. Genet Res Int, 97, 431-487.
Khan, M.A., A. Anjum, M.A. Bhat, B.A. Padder, Z.A. Mir, Kamaludin (2015): Multivariate analysis for morphological diversity of bread wheat (Triticum aestivum L.) germplasm lines in Kashmir valley. Journal of Science 5 (6),372-376.
Manasievska S.S., С. (2006). Polimorfizam na glutenini, glijadini i komponenti na prinos kaj hibridi pcenica od vidovite Triticum aestivum, Triticum durum i Triticum dicoccum [Polymorphism on glutenin, gliadin and yield components in wheat hybrids of Triticum aestivum, Triticum durum and Triticum dicoccum], Doktorska disertacija,Univerzitet Sv. Kiril i Metodij vo Skopje,Fakultet za Zemjodelski nauki i hrana – Skopje.
Maqbool, R., Sajjad, M., Khaliq, I., Rehman, A., Khan, S. A., & Khan, H. S. (2010). Morphological diversity and trait association in bread wheat (Triticum aestivum L.). American-Eurasian Journal of Agriculture and Environment Sciences, 8(2), 216-224.
Mishra, C.N., Tiwari V., Satish, V., Gupta, A., Kumar, I. & Sharma D (2015): Genetic diversity and genotypes by trait analysis for agromorphological traits of wheat (Triticum aestivum L.). SABRAO J. Breed. Genet., 47(1): 21-28.
Mohammadi S.A., & Prasanna B.M. (2003). Analysis of genetic diversity in crop plants: salient statical tools and considerations. Crop Sci 43, 1235-1248.
Revelle W. (2014): Package “psych”: Procedures for Psychological, Psychometric, and Personality Research. http://cran.r-project.org/web/packages/psych/psych.pdf
Roldan-Ruiz, I., Van Euwijk, T.J., Gilliland, P., Dubreuil, C., Dillman, J., Lallemand, M. & De Loose, C.P. (2001): A comparative study of molecular and morphological methods of describing relationships between perennial ryegrass (Lolium perenne L.) varieties. Theor. Appl. Genet., 103,1138-1150.
Sharma, S., Schulthess, A. W., Bassi, F. M., Badaeva, E. D., Neumann, K., Graner, A., Özkan, H., Werner, P., Knüpffer, H., & Kilian, B. (2021). Introducing Beneficial Alleles from Plant Genetic Resources into the Wheat Germplasm. Biology, 10(10), 982.
Singh, S., Jighly, A., & Sehgal, D. (2021). Direct introgression of untapped diversity into elite wheat lines. Nat Food 2, 819–827 .
Smýkal, P., Horáèek, R. Dostálová, M. & Hýbl, J. (2008). Variety discrimination in pea (Pisum sativum L.) by molecular, biochemical and morphological markers. J. Appl. Genet. 49(2),155-166.
Sonmezoğlu, O.A., Bozmaz, B., Yildirim, A., Kandemir, N., & Aydin, N. (2012). Genetic characterization of Turkish bread wheat landraces based on microsatellite markers and morphological characters. Turk J Biol, 36, 589-597.
Suzuki, R., & Shimodaria H. (2013): Package “pvclust”: Hierarchical Clustering with P-Values via Multiscale Bootstrap Resambling http://www.is.titech.ac.jp/shimo/prog/pvclust.pdf.
Yan, W., & Kang, M.S. (2003). GGE biplot analysis: A graphical tool for breeders, geneticists and agronomists. CRC Press, Boca Raton, FL, USA. Yan, W., & Rajcan I.R. (2002). Biplot analysis of test sites and trait relations of soybean in Ontario. Can. J. Plant Sci, 42,11–20.