Theoretical and Applied Genetics

, Volume 109, Issue 7, pp 1329–1336 | Cite as

Development and assessment of microarray-based DNA fingerprinting in Eucalyptus grandis

  • Sabine Lezar
  • A. A. Myburg
  • D. K. Berger
  • M. J. Wingfield
  • B. D. WingfieldEmail author
Original Paper


Development of improved Eucalyptus genotypes involves the routine identification of breeding stock and superior clones. Currently, microsatellites and random amplified polymorphic DNA markers are the most widely used DNA-based techniques for fingerprinting of these trees. While these techniques have provided rapid and powerful fingerprinting assays, they are constrained by their reliance on gel or capillary electrophoresis, and therefore, relatively low throughput of fragment analysis. In contrast, recently developed microarray technology holds the promise of parallel analysis of thousands of markers in plant genomes. The aim of this study was to develop a DNA fingerprinting chip for Eucalyptus grandis and to investigate its usefulness for fingerprinting of eucalypt trees. A prototype chip was prepared using a partial genomic library from total genomic DNA of 23 E. grandis trees, of which 22 were full siblings. A total of 384 cloned genomic fragments were individually amplified and arrayed onto glass slides. DNA fingerprints were obtained for 17 individuals by hybridizing labeled genome representations of the individual trees to the 384-element chip. Polymorphic DNA fragments were identified by evaluating the binary distribution of their background-corrected signal intensities across full-sib individuals. Among 384 DNA fragments on the chip, 104 (27%) were found to be polymorphic. Hybridization of these polymorphic fragments was highly repeatable (R2>0.91) within the E. grandis individuals, and they allowed us to identify all 17 full-sib individuals. Our results suggest that DNA microarrays can be used to effectively fingerprint large numbers of closely related Eucalyptus trees.


Eucalyptus Polymorphic Fragment Intensity Class Genome Representation Binary Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag 2004

Authors and Affiliations

  • Sabine Lezar
    • 1
  • A. A. Myburg
    • 1
  • D. K. Berger
    • 2
  • M. J. Wingfield
    • 3
  • B. D. Wingfield
    • 1
    Email author
  1. 1.Department of GeneticsUniversity of PretoriaPretoriaSouth Africa
  2. 2.Department of BotanyUniversity of PretoriaPretoriaSouth Africa
  3. 3.Forestry and Agricultural Biotechnology Institute (FABI)University of PretoriaPretoriaSouth Africa

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