Multivariate analysis in wheat germplasm captures variability for agro-morphological and physiological traits

Abstract

The present study was carried out with an objective to investige genetic diversity in set of diverse wheat genotypes during 2018-19 at the experimental farm of Indian Institute of Wheat and Barley Research, Karnal. Principal Component Analysis was carried out on this set of 440 genotypes to study variability for different agro-morphological and physiological traits. The coefficient of variation ranged from 7.20-38.30 for the measured traits. Yield per plot was positively related with tillers per m, spike length, grains per spike and 1000 grains weight. The first three principal components explained 45.66% of variation. Agglomerative clustering grouped the genotypes into six groups and had a cophenetic correlation coefficient of 0.319. Almost all the components explained some variation for each trait and hence can be further used in hybridization for creation of variability in the breeding programs to develop improved cultivars. This remarkable variation in the set can be accounted to the fact that these lines were a collection of germplasm from different wheat growing countries and also their specificity for different traits.

Deciphering Genetic Diversity in Advanced Wheat Lines (Triticum aestivum L.) for Yield and Other Yield Contributing Traits Across the Locations

“…The exploration of Principal Component Analysis (PCA) in wheat research has garnered considerable attention, with several studies delving into its application and implications.Piro et al [38], illustrated the potential application of rye chromatin introgression in wheat quality breeding, with the arabinoxylan content of wheat white flour, demonstrating the use of PCA in assessing quality traits in wheat breeding Kumar et al [39], found variability in yield contributing traits and physiological traits by PCA, suggesting that identified genotypes can be used for hybridization and improved cultivar development. Saleh et al [40] emphasized the use of PCA in assessing genetic variations among wheat genotypes to enhance selection efficiency in breeding programs.Moreover, Ahmad et al [41] conducted PCA to examine the suitability of wheat varieties for cookie-making quality, demonstrating the use of PCA in correlating physical and rheological parameters of wheat varieties.These studies collectively demonstrate the diverse applications of PCA in wheat research, including assessing wheat variety suitability for specific products, comparing wheat genotypes, and analyzing wheat flour refinement.…”