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International Journal of Pure & Applied Bioscience (IJPAB)
Year : 2018, Volume : 6, Issue : 3
First page : (181) Last page : (190)
Article doi: :

Genetic Diversity by Multivariate Analysis Using R Software

Immad Ahmad Shah1, Imran Khan2*, Shakeel A Mir3, M. S. Pukhta4, Zahoor A Dar5, Ajaz Lone6
1,2,3,4Division of Agricultural Statistics, SKUAST-K, Shalimar, 190025, J&K, India
5,6 Dry Land Agricultural Research Station, SKUAST-K, Budgam
*Corresponding Author E-mail:
Received: 15.05.2018 | Revised: 22.06.2018 | Accepted: 28.06.2018


The present investigation was conducted to study the genetic divergence pattern using Multivariate analysis techniques viz. Cluster Analysis (CA) and Principal Component Analysis (PCA). Cluster analysis identified and classified the accessions on the basis of the similarity of the characteristics into seven distinct clusters. The highest inter cluster distance was observed between Cluster I and Cluster III and lowest between Cluster II and Cluster V. The Principal Component Analysis revealed two principal components, PC I and PC II, and accounted for nearly 76.92% of the total variation. R software has been used to execute the above mentioned techniques of analysis.

Key words: Principal Component Analysis, Eigen Values/Vectors, Cluster Analysis, R Software, SAS Software

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Cite this article: Shah, I.A., Khan, I., Mir, S. A., Pukhta, M.S., Dar, Z.A., Lone, A., Genetic Diversity by Multivariate Analysis Using R Software, Int. J. Pure App. Biosci.6(3): 181-190 (2018). doi: