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Genome-enabled analysis of population dynamics and virulence associated loci in the oat crown rust fungus Puccinia coronata f. sp. avenae

MPG-Autoren
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Pereira,  Danilo
Max Planck Fellow Group Environmental Genomics (Stukenbrock), Max Planck Institute for Evolutionary Biology, Max Planck Society;

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Stukenbrock,  Eva H.       
Max Planck Fellow Group Environmental Genomics (Stukenbrock), Max Planck Institute for Evolutionary Biology, Max Planck Society;

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mpmi-09-23-0126-fi.pdf
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Zitation

Hewitt, T. C., Henningsen, E. C., Pereira, D., McElroy, K., Nazareno, E. S., Dugyala, S., et al. (2024). Genome-enabled analysis of population dynamics and virulence associated loci in the oat crown rust fungus Puccinia coronata f. sp. avenae. Molecular Plant-Microbe Interactions, 37(3), 290-303. doi:10.1101/2023.09.18.557855.


Zitierlink: https://hdl.handle.net/21.11116/0000-000D-BAA6-6
Zusammenfassung
Puccinia coronata f. sp. avenae (Pca) is an important fungal pathogen causing crown rust that impacts oat production worldwide. Genetic resistance for crop protection against Pca is often overcome by the rapid virulence evolution of the pathogen. This study investigated the factors shaping adaptive evolution of Pca using pathogen populations from distinct geographic regions within the USA and South Africa (SA). Phenotypic and genome-wide sequencing data of these diverse Pca collections, including 217 isolates, uncovered phylogenetic relationships and established distinct genetic composition between populations from northern and southern regions from the USA and SA. The population dynamics of Pca involve a bidirectional movement of inoculum between northern and southern regions of the USA and contributions from clonality and sexuality. The population from SA is solely clonal. A genome-wide association study (GWAS) employing a haplotype-resolved Pca reference genome was used to define eleven virulence-associated loci corresponding to twenty-five oat differential lines. These regions were screened to determine candidate Avr effector genes. Overall, the GWAS results allowed us to identify the underlying genetic traits controlling pathogen recognition in an oat differential set used in the USA to assign pathogen races (pathotypes). Key GWAS findings support complex genetic interactions in several oat lines suggesting allelism among resistance genes or redundancy of genes included in the differential set, multiple resistance genes recognising genetically linked Avr effector genes, or potentially epistatic relationships. A careful evaluation of the composition of the oat differential set accompanied by the development or implementation of molecular markers is recommended.