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Vol. 12, Issue 1 (2023)

Genetic diversity studies using Principal Component Analysis and D2 statistics in little millet (Panicum sumatrense L.) genotypes

Author(s):
Patel Ujjaval N, Harshal E Patil and Dela GJ
Abstract:
Little millet improvement is possible only if the information on genetic diversity among existing genotypes is available. Principal component analysis and Mahalanobis D2 analysis were used to analyse 50 little millet genotypes based on ninteen quantifiable traits. The whole variance was divided into nineteen major principal components using PCA, with the top two PCs with eigenvalues >1 accounting for 92.14 per cent of the total variation. From the analysis of the first two PCs and Mahalanobis D2 analysis, it was confirmed that genotypes viz., WV 303, WV 275, WV 278, WV 281, WV 292, WV 299, WV 263, WV 296 and WV 259 were scattered apart in all four quadrates of thebi-plot and fall in clusters with high inter-cluster distance representing maximum genetic variation. As a result, 50 little millet genotypes had genetic and phenotypic differences that could be used to improve the little millet by simple selection and crossing of potential parents.
Pages: 1106-1111  |  253 Views  181 Downloads


The Pharma Innovation Journal
How to cite this article:
Patel Ujjaval N, Harshal E Patil, Dela GJ. Genetic diversity studies using Principal Component Analysis and D2 statistics in little millet (Panicum sumatrense L.) genotypes. Pharma Innovation 2023;12(1):1106-1111.

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