Author

W. Plumb

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Evolutionary Biology· 2 Plant Biology· 1

Signature Assays, Organisms & Methods

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Organism:plant (other)· 2 Approach:statistical model· 2 Assay:whole-genome sequencing· 2 Research Focus:cell type annotation· 1 Assay:DNA methylation· 1 Approach:experimental assay· 1 Organism:human· 1 Research Focus:polygenic risk· 1

Frequent Co-Authors

C. L. Metheringham· 2 shared L. Kelly· 2 shared R. A. Nichols· 2 shared R. Buggs· 2 shared D. Carey· 1 shared E. Lines· 1 shared G. Douglas· 1 shared J. J. Stocks· 1 shared

Activity by Year

2019-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 26 mutual co-author links

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2 shared papersEvolutionary Biology

C. L. Metheringham

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Genetic barcodes for ash (Fraxinus) species and generation of new wide hybrids

W. Plumb, L. Kelly, J. Mullender, R. Powell, L. Csiba, M. N. Gorriz, D. Carey, M. Mason, W. Crowther, J. Koch, G. Douglas, R. Buggs

This paper develops a genetic barcode system for ash species and generates wide hybrids to transfer resistance traits against pests and pathogens.

PLANTS, PEOPLE, PLANET · Jul 21, 2025Peer-reviewedPlant Biology

Rapid polygenic adaptation in a wild population of ash trees under a novel fungal epidemic

C. L. Metheringham, W. Plumb, W. Flynn, J. Stocks, L. Kelly, M. Nemesio Gorriz, S. Grieve, J. Moat, E. Lines, R. Buggs, R. A. Nichols

The paper shows rapid polygenic adaptation in ash trees due to a fungal epidemic, with a 31% juvenile population decline estimated through simulations.

bioRxiv · Aug 1, 2022Evolutionary Biology

Genomic basis of European ash tree resistance to ash dieback fungus

J. J. Stocks, C. L. Metheringham, W. Plumb, S. Lee, L. J. Kelly, R. A. Nichols, R. J. A. Buggs

The paper identifies SNPs associated with ash dieback resistance in European ash trees and develops a genomic prediction model with over 90% accuracy.

bioRxiv · May 2, 2019Evolutionary Biology