Distribution of rose black spot (Diplocarpon rosae) genetic diversity in eastern North America using amplified fragment length polymorphism and implications for resistance screening

Vance M. Whitaker, Stan C. Hokanson, James Bradeen

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Black spot, incited by the fungus Diplocarpon rosae Wolf, is the most significant disease problem of landscape roses (Rosa hybrida L.) worldwide. The documented presence of pathogenic races necessitates that rose breeders screen germplasm with isolates that represent the range of D. rosae diversity for their target region. The objectives of this study were to characterize the genetic diversity of single-spore isolates from eastern North America and to examine their distribution according to geographic origin, host of origin, and race. Fifty isolates of D. rosae were collected from roses representing multiple horticultural classes in disparate locations across eastern North America and analyzed by amplified fragment length polymorphism. Considerable marker diversity among isolates was discovered, although phenetic and cladistic analyses revealed no significant clustering according to host of origin or race. Some clustering within collection locations suggested short-distance dispersal through asexual conidia. Lack of clustering resulting from geographic origin was consistent with movement of D. rosae on vegetatively propagated roses. Results suggest that field screening for black spot resistance in multiple locations may not be necessary; however, controlled inoculations with single-spore isolates representing known races is desirable as a result of the inherent limitations of field screening.

Original languageEnglish (US)
Pages (from-to)534-540
Number of pages7
JournalJournal of the American Society for Horticultural Science
Volume132
Issue number4
DOIs
StatePublished - Jul 2007

Keywords

  • AMOVA
  • Dendrogram
  • Fungal isolate
  • Jaccard's coefficient
  • Pathogenic race
  • Principal component analysis

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