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September 10, 2025

Genetic Diversity Based on Cluster and Principal Component Analyses for Yield and its Contributing Characters in Wheat (Triticum aestivum L.)

MVMISHRA V.K TSTeejveer SinghIndian Institute of Rice Research BBBibas B.K.Navsari Agricultural University

Key Points

Cluster analysis grouped 24 wheat genotypes into four major clusters, showing significant divergence.
The second cluster with two genotypes demonstrated better yield potential compared to the fourth cluster.
Principal component analysis revealed that four principal components explained 89.68% of total variability.
Future breeding programs in wheat can utilize these findings to select for higher yield varieties.

Abstract

An experiment was carried out to assess genetic diversity by cluster and principal component analysis (PCA) for yield and its nine contributing characters in 24 bread wheat genotypes at BHU Agricultural Research Farm during Rabi 2010-11. The cluster analysis grouped all the 24 wheat genotypes into four major clusters. Extreme divergence was observed among clusters. Second cluster with two genotypes (WH 542 and ATTILA) had better yield potential as compared with fourth cluster which had also two genotypes (WL 711 and MALAVIYA 206). The result of PCA revealed that all the 4 principal components (PC1, PC2, PC3 and PC 4) contributed 89.68% of the total variability and accounted with values of 45.38, 20.69, 12.43 and 11.17 respectively. The third principal component had high positive component loading for all variables except spike length and grain spike-1. The result of present study could be exploited in planning and execution of future breeding programme in wheat.

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