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Research Article Open access CC BY 4.0

Genetic Diversity Based on Morphological Traits of 19 Maize Genotypes Using Principal Component Analysis and GT Biplot

A. M. M. Al-Naggar, M. M. Shafik, R. Y. M. Musa

Annual Research & Review in Biology · pp. 68–85 · Published 14 Apr 2020

10.9734/arrb/2020/v35i230191

Abstract

Among the phenotypic, biochemical, and molecular methods employed in assessment of genetic diversity, the phenotypic method has proven efficient for the assessment, description and classification of germplasm collections to enhance their use in maize breeding. The objectives of the present study were: (i) to assess the extent of genetic diversity in a collection of Egyptian commercial maize hybrids and populations, through field evaluation under water and N stressed and non-stressed conditions, using morphological data based on Principle Component Analysis (PCA), (ii) to measure the genetic distance among these genotypes using UPGMA cluster analysis and (iii) to assess the relationship between grain yield and yield-related traits of maize genotypes using GT-biplot analysis. A two-year field experiment was conducted in a split-split plot design with 3 replications, where 2 irrigation regimes, three N rates and 19 maize genotypes occupied the main plots, sub plots and sub-sub plots, respectively. The germplasm was assessed for 21 agronomic traits. Highly significant differences (P ≤ 0.01) were observed among the maize hybrids and populations for all measured traits.  Results of the GT biplot in the present study indicated that high values of 100-Kernel weight, ears/plant, kernels/plant, kernels/row, plant height, nitrogen use efficiency, nitrogen utilization efficiency, and grain nitrogen content and short ASI could be considered reliable secondary traits for improving grain yield under stressed and non-stressed conditions. The highest genetic distance was found between G9 (SC-2055) and each of G15 (American Early Dent), G18 (Midland) or G19 (Ried Type). The Agglomerative Hierarchical Clustering based on phenotypic data assigned the maize genotypes into five groups. The different groups obtained can be useful for deriving the inbred lines with diverse features and diversifying the heterotic pools.

Phenotypic data PCA GT-biplot maize collections clustering.

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