Multivariate Analysis of Morpho-Physiological Traits Reveals Differential Drought Tolerance Potential of Bread Wheat Genotypes at the Seedling Stage

Abstract

Drought is one of the foremost environmental stresses that can severely limit crop growth and productivity by disrupting various physiological processes. In this study, the drought tolerance potential of 127 diverse bread wheat genotypes was evaluated by imposing polyethylene glycol (PEG)-induced drought followed by multivariate analysis of several growth-related attributes. Results showed significant variations in the mean values of different morpho-physiological traits due to PEG-induced drought effects. Correlation analysis revealed that most of the studied traits were significantly correlated among them. The robust hierarchical co-clustering indicated that all the genotypes were clustered into four major groups, with cluster 4 (26 genotypes) being, in general, drought-tolerant followed by cluster 1 (19 genotypes) whereas, cluster 2 (55 genotypes) and 3 (27 genotypes) being drought-sensitive. Linear discriminant analysis (LDA) confirmed that around 90% of the genotypes were correctly assigned to clusters. Squared distance (D2) analysis indicated that the clusters differed significantly from each other. Principal component analysis (PCA) and genotype by trait biplot analysis showed that the first three components accounted for 71.6% of the total variation, with principal component (PC) 1 accounting for 35.4%, PC2 for 24.6% and PC3 for 11.6% of the total variation. Both PCA and LDA revealed that dry weights, tissue water content, cell membrane stability, leaf relative water content, root-shoot weight ratio and seedling vigor index played the most important discriminatory roles in explaining drought tolerance variations among 127 wheat genotypes. Our results conclude that the drought-tolerant and -sensitive wheat genotypes identified in this study would offer valuable genetic tools for further improvement of wheat productivity in arid and semi-arid regions during this time of unpredictable climate change.

Keywords

• wheat
• drought
• robust hierarchical co-cluster
• principal component analysis
• linear discriminant analysis