Diversity analysis and structural modeling for some traits in wheat genotypes

Abstract

Wheat is the most important grain crop in the world which provide people with almost 50% of the required calories [1]. Breeding programs aim to increase the selection efficiency by assessing more genetic variations among wheat genotypes [2], this can be studied through different methods of multivariate analysis such as principle component and cluster analysis. Principle component analysis is used to reduce the large number of traits to a limited number which represents the majority of the existent variation [3]. Al-Otayk [4] applied principle component analysis to study the variation in wheat germplasm, their results showed remarkable variation among them. Categorize germplasm in many groups depending on their variation is applied by Cluster analysis [5]. Cluster analysis was applied by Devesh et al. [1] depending on the agronomic traits of various wheat trait. Poudel et al. [6] estimated the diversity between wheat genotypes, their results showed that wheat genotypes were clustered in various main and sub main clusters. Sahu et al. [7] declared that correlation is used to just to illustrate relation between traits, but not for prediction of any trait, whereas path analysis considers as an efficient method for confirming the correlation depending on the effects and reasons of these effects and to eliminate any false effect. Abd El-Mohsen [8] mentioned that prediction of grain yield via other traits can be applied by regression analysis. The objectives of this investigation were to: (i) evaluate the magnitude of potential diversity between exotic and local wheat genotypes by using principal component analysis and cluster analysis, (ii) study the nature of structural modeling between grain yield and other traits via Regression and path analysis, (iii) define the superior genotypes regarding grain yield in various locations to be used in breeding programs.