Document Detail

Multivariate analysis of morphological variation in accessions of Aegilops crassa from Iran.
MedLine Citation:
PMID:  19070064     Owner:  NLM     Status:  MEDLINE    
Principal component and cluster analyses were used to evaluate the pattern of morphological variation in 122 accessions of Aegilops crassa for 14 quantitative characters. With the principal component analysis, the first five principal components with eigenvalues more than 1 contributed 69.5% of the variability amongst accessions, whereas PC6 to PC14 were less than unity. Plant height, stem diameter, spike length and number of spikelets per spike were the most important characters in the first principal component. The germplasm was grouped into five clusters using cluster analysis. Although each cluster had some specific characteristics of its own, but clusters were not clearly separated when plotted by the first two principal components. Mahalanobis distances (D2) determined that plant height, stem diameter, spike length, number of spikelets per spike, node width, seed length, seed width and flowering date characters as the most important characters in differentiating the accessions. The morphological variation of Aegilops crassa accessions obtained in this study provides useful information for the future collection and makes these genetic resources more accessible to breeders.
M Ranjbar; M R Naghavi; A Zali; M J Aghaei
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Pakistan journal of biological sciences: PJBS     Volume:  10     ISSN:  1028-8880     ISO Abbreviation:  Pak. J. Biol. Sci.     Publication Date:  2007 Apr 
Date Detail:
Created Date:  2008-12-16     Completed Date:  2009-01-05     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101247723     Medline TA:  Pak J Biol Sci     Country:  Pakistan    
Other Details:
Languages:  eng     Pagination:  1126-9     Citation Subset:  IM    
Department of Agronomy and Plant Breeding, Agricultural College, Tehran University, Karaj, Iran.
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MeSH Terms
Cluster Analysis
Genetic Variation*
Multivariate Analysis
Poaceae / anatomy & histology*,  genetics
Principal Component Analysis

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