Priyanka Verma S. K. Maurya Hridesh Yadav Ankit Panchbhaiya


The present investigation was carried out at Vegetable Research Centre, Pantnagar to estimate the ge-netic divergence using Mahalanobis D2 statistics for twelve characters on 35 genotypes of pointed gourd. Cluster analysis and principal component analysis (PCA) were used to identify the most discerning trait responsible for greater variability in the lines and on the basis of mean performance, genotypes were classified into different groups. Five principal components (PC) have been extracted using the mean performance of the genotypes and 83.23 per cent variation is yielded by the first five principal components, among them high mean positive value or higher weight age was obtained was obtained for days to first female flower anthesis and days to first fruit harvest among all the vectors, indicates that these traits are important component of genetic divergence in pointed gourd. Non- hierarchical Euclidean cluster analysis grouped the genotypes into seven clusters and the highest number of genotypes were found in cluster number IV i.e. eleven whereas maximum inter-cluster distance was found between the cluster III and VI i.e. 74.250, it indicates that a wide range of genetic divergence is present between the genotypes present among these two clusters. And as per contribution toward total divergence, traits like fruit yield per hectare and number of fruit per plant contributed 92.64% toward total divergence. The high diversity found in the genotypes showed its great potential for improving qualitative as well as quantitative traits in pointed gourd.


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Divergence, Non-hierarchical cluster, PCA, Principal component analysis, Pointed gourd

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Verma, P., Maurya, S. K., Yadav, H., & Panchbhaiya, A. (2017). Determination of genetic divergence in pointed gourd by principal component and non-hierarchical euclidean cluster analysis. Journal of Applied and Natural Science, 9(4), 2421–2426. https://doi.org/10.31018/jans.v9i4.1548
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