Advertisement

Theoretical and Applied Genetics

, Volume 100, Issue 6, pp 918–925 | Cite as

Assessment of genetic diversity within and among germplasm accessions in cultivated sorghum using microsatellite markers

  • Y. Djè
  • M. Heuertz
  • C. Lefèbvre
  • X. Vekemans
Original Paper

Abstract 

Microsatellite markers are increasingly being used in crop plants to discriminate among genotypes and as tools in marker-assisted selection. Here we evaluated the use of microsatellite markers to quantify the genetic diversity within as well as among accessions sampled from the world germplasm collection of sorghum. Considerable variation was found at the five microsatellite loci analysed, with an average number of alleles per locus equal to 2.4 within accessions and 19.2 in the overall sample of 25 accessions. The collection of sorghum appeared highly structured genetically with about 70% of the total genetic diversity occurring among accessions. However, differentiation among morphologically defined races of sorghum, or among geographic origins, accounted for less than 15% of the total genetic diversity. Our results are in global agreement with those obtained previously with allozyme markers. We were also able to show that microsatellite data are useful in identifying individual accessions with a high relative contribution to the overall allelic diversity of the collection.

Key words Core collection Genetic diversity Germplasm Microsatellites Sorghum SSR 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Y. Djè
    • 1
  • M. Heuertz
    • 2
  • C. Lefèbvre
    • 1
  • X. Vekemans
    • 1
  1. 1.Université Libre de Bruxelles, Laboratoire de Génétique et d’Ecologie végétales, 1850 chaussée de Wavre, B-1160 Brussels, Belgium e-mail: xvekema@ulb.ac.beBE
  2. 2.CREBS Research Unit Centre de recherche public, 162a avenue de la faı¨encerie, L-1511 Luxembourg, LuxemburgLU

Personalised recommendations