Diversity analysis and structural modeling for some traits in wheat genotypes

Abstract

This investigation was carried out during 2018/2019 season in three locations Homs, Al-Swaida and Tartous belongs to the General Commission for Scientific Agricultural Research in Syria, using 17 Italian, Syrian and Ethiopian wheat genotypes to estimate the potential diversity by principle component and cluster analysis, and to study the structural modeling between grain yield and other traits to define best traits as predictors and selection indexes of grain yield, and to determine the superior genotypes in grain yield. Results indicated a remarkable variation of 74% due only to the first four principle components with Eigen value > 1. PC Biplot showed that Tartous was the best location, and the genotype SD09 was superior in grain yield per plant followed by SH5 and IP39. Structural modeling results revealed that the total and fertile tillers number per plant were the best predictors for grain yield per plant, while fertile tillers per plant with grain weight per spike could be used as selection indexes of wheat grain yield because they had positive strong direct effect on grain yield per plant. Cluster analysis results confirmed the need to assess more various genotypes from different origins.

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. The use of PCA in these studies highlights its effectiveness in providing valuable insights into the composition, properties, and genetic diversity of wheat, thereby contributing to advancements in wheat breeding and product development.…”