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Characterisation of barley landraces from Syria and Jordan for resistance to rhynchosporium and identification of diagnostic markers for Rrs1Rh4.

Mark E LooseleyLucie L GriffeBianca BüttnerKathryn M WrightMicha M BayerMax CoulterJean-Noël ThauvinJill Middlefell-WilliamsMarta MalukAleksandra OkpoNicola KettlesPeter WernerEd ByrneAnna O Avrova
Published in: TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik (2020)
Diagnostic markers for Rrs1Rh4 have been identified by testing for associations between SNPs within the Rrs1 interval in 150 barley genotypes and their resistance to Rhynchosporium commune isolates recognised by lines containing Rrs1. Rhynchosporium or barley scald, caused by the destructive fungal pathogen Rhynchosporium commune, is one of the most economically important diseases of barley in the world. Barley landraces from Syria and Jordan demonstrated high resistance to rhynchosporium in the field. Genotyping of a wide range of barley cultivars and landraces, including known sources of different Rrs1 genes/alleles, across the Rrs1 interval, followed by association analysis of this genotypic data with resistance phenotypes to R. commune isolates recognised by Rrs1, allowed the identification of diagnostic markers for Rrs1Rh4. These markers are specific to Rrs1Rh4 and do not detect other Rrs1 genes/alleles. The Rrs1Rh4 diagnostic markers represent a resource that can be exploited by breeders for the sustainable deployment of varietal resistance in new cultivars. Thirteen out of the 55 most resistant Syrian and Jordanian landraces were shown to contain markers specific to Rrs1Rh4. One of these lines came from Jordan, with the remaining 12 lines from different locations in Syria. One of the Syrian landraces containing Rrs1Rh4 was also shown to have Rrs2. The remaining landraces that performed well against rhynchosporium in the field are likely to contain other resistance genes and represent an important novel resource yet to be exploited by European breeders.
Keyphrases
  • genome wide
  • dna methylation
  • electronic health record
  • transcription factor
  • deep learning
  • artificial intelligence
  • genome wide identification