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  The genetic architecture of emerging fungicide resistance in populations of a global wheat pathogen

Pereira, D., McDonald, B. A., & Roll, D. (2020). The genetic architecture of emerging fungicide resistance in populations of a global wheat pathogen. Genome Biology and Evolution, 12(12), 2231-2244. doi:10.1093/gbe/evaa203.

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Pereira, Danilo1, Author           
McDonald, Bruce A.1, 2, Author           
Roll, Daniel, Author
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1External Organizations, ou_persistent22              
2Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_971545              

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 Abstract: Containing fungal diseases often depends on the application of fungicidal compounds. Fungicides can rapidly lose effectiveness due to the rise of resistant individuals in populations. However, the lack of knowledge about resistance mutations beyond known target genes challenges investigations into pathways to resistance. We used whole-genome sequencing data and association mapping to reveal the multilocus genetic architecture of fungicide resistance in a global panel of 159 isolates of Parastagonospora nodorum, an important fungal pathogen of wheat. We found significant differences in azole resistance among global field populations. The populations evolved distinctive combinations of resistance alleles which can interact when co-occurring in the same genetic background. We identified 34 significantly associated single nucleotide polymorphisms located in close proximity to genes associated with fungicide resistance in other fungi, including a major facilitator superfamily transporter. Using fungal colony growth rates and melanin production at different temperatures as fitness proxies, we found no evidence that resistance was constrained by genetic trade-offs. Our study demonstrates how genome-wide association studies of a global collection of pathogen strains can recapitulate the emergence of fungicide resistance. The distinct complement of resistance mutations found among populations illustrates how the evolutionary trajectory of fungicide adaptation can be complex and challenging to predict.

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Language(s): eng - English
 Dates: 2020-09-222020-09-282020-12
 Publication Status: Issued
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 Identifiers: DOI: 10.1093/gbe/evaa203
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Title: Genome Biology and Evolution
  Other : GBE
  Abbreviation : Genome Biol Evol
Source Genre: Journal
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Publ. Info: Oxford : Oxford Univ. Press
Pages: - Volume / Issue: 12 (12) Sequence Number: - Start / End Page: 2231 - 2244 Identifier: ISSN: 1759-6653
CoNE: https://pure.mpg.de/cone/journals/resource/1759-6653