Applied Microbiology and Biotechnology

, Volume 72, Issue 3, pp 537–543 | Cite as

Analysis of genetic diversity in Ganoderma population with a novel molecular marker SRAP

  • Shu-Jing Sun
  • Wei Gao
  • Shu-Qian Lin
  • Jian Zhu
  • Bao-Gui XieEmail author
  • Zhi-Bin Lin
Applied Genetics and Molecular Biotechnology


Genetic marker technology designed to detect naturally occurring polymorphisms at the DNA level had become an invaluable and revolutionizing tool for both applied and basic studies of fungi. To eliminate the confusion on the taxonomy of Ganoderma strains, in this study, a collection of 31 accessions representative of morphotypes and some unclassified types was used for analyzing molecular diversity using a novel molecular marker sequence-related amplified polymorphism (SRAP). This collection included commercial cultivars and wild varieties that represented the great diversification of types from different countries and regions. The experimental results showed that 50 out of 95 combinations of primers turned out to be polymorphic, and 85 polymorphism bands were obtained using six combinations. Based on the appearances of markers, the genetic similarity coefficients were calculated, and genetic variations were observed (0∼1) among the 31 different Ganoderma strains. The group of Ganoderma lucidum showed significant differences from the group of Ganoderma sinense. Moreover, G. lucidum in China was also different from G. lucidum in Yugoslavia. At the same time, cluster analysis successfully categorized these 31 Ganoderma strains into five groups. These results revealed the genetic diversity of Ganoderma strains and their correlation with geographic environments. It also suggested SRAP marker could be used in the taxonomic analysis of fungi. To our knowledge, this is the first application of SRAP marker on the systematics of Ganoderma strains within basidiomycetes.


Ganoderma Genetic Diversity Analysis SRAP Marker Flammulina Velutipes TaKaRa Biotech 
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.



This work was financially supported by a grant from Science & Technology Department of Fujian Province, People’s Republic of China (2003EAT20021). We would like to sincerely thank Dr. Hu Zhu and Tianwen Wang of East China University of Science and Technology for their critical reading of the manuscript and useful suggestions.


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Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • Shu-Jing Sun
    • 1
  • Wei Gao
    • 1
  • Shu-Qian Lin
    • 2
  • Jian Zhu
    • 3
  • Bao-Gui Xie
    • 1
    Email author
  • Zhi-Bin Lin
    • 4
  1. 1.College of Life ScienceFujian Agriculture and Forestry UniversityFuzhouPeople’s Republic of China
  2. 2.Green Valley Institute of Bio-pharmaceutical TechnologyFuzhouPeople’s Republic of China
  3. 3.College of HorticultureFujian Agriculture and Forestry UniversityFuzhouPeople’s Republic of China
  4. 4.Department of Pharmacology, School of Basic Medical SciencePeking UniversityBeijingPeople’s Republic of China

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