International Journal of Chemical Studies
Vol. 6, Issue 1 (2018)
Assessing inter: Relationship of sesame genotypes and their traits using cluster analysis and principal component analysis
Author(s): Atul Singh, Rajani Bisen and Akanksha Tiwari
Abstract: Seventy five sesame genotypes were evaluated to assess the genetic divergence available in the sesame germplasm based on the Mahalanobis distance and cluster analysis for the identification of genetically diverse and agronomically superior accessions which may be further used in the hybridization programme. Clustering pattern indicated that majority of genotypes, i.e. 64 (85%) were genetically close to each other and grouped in 3 clusters, while apparent diversity was mainly noticed due to 11 genotypes (15%) distributed over 10 clusters. Maximum inter cluster divergence was observed between clusters XII and IV (108.84) followed by clusters XIII and IX (107.11) indicating that good recombinants can be realized by mating between the genotypes KMS-5-371, EC-303304-A, KMR-48-A, KMS-5-587, IS-491-A, NIC-16393 and IC-204063 in a definite fashion. The trait days to flower initiation (39.82) contributed maximum to genetic divergence followed by oil content (28.58%) and number of capsules/plant (13.12%). These three characters contributed more than 81% to the total genetic divergence in the genotypes studied. PCA yielded twelve PCs from the twelve agronomic traits of sesame out of which seven PCs showing about 83.193% variability. Results revealed that the genotypes viz., S-0644, KMS-5-361, T-1-A, NIC-16214, SI-1125, NIC-8080-A have highest PC values for characters number of capsules/plant, number of seeds/capsule, number of primary branches/plant, number of secondary branches/plant, 1000 seed weight and seed yield/plant. Thus, these genotypes might be used for development of new varieties of sesame.
Pages: 2151-2153 | 454 Views 9 Downloads
How to cite this article:
Atul Singh, Rajani Bisen, Akanksha Tiwari. Assessing inter: Relationship of sesame genotypes and their traits using cluster analysis and principal component analysis. Int J Chem Stud 2018;6(1):2151-2153.