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Cited by 2,128

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

P ATHOGEN P OPULATION G ENETICS, E VOLUTIONARY P OTENTIAL, AND D URABLE R ESISTANCE

McDonald, Bruce A., Linde, Celeste·2002·Annual Review of Phytopathology

Abstract

▪ Abstract We hypothesize that the evolutionary potential of a pathogen population is reflected in its population genetic structure. Pathogen populations with a high evolutionary potential are more likely to overcome genetic resistance than pathogen populations with a low evolutionary potential. We propose a flexible framework to predict the evolutionary potential of pathogen populations based on analysis of their genetic structure. According to this framework, pathogens that pose the greatest risk of breaking down resistance genes have a mixed reproduction system, a high potential for genotype flow, large effective population sizes, and high mutation rates. The lowest risk pathogens are those with strict asexual reproduction, low potential for gene flow, small effective population sizes, and low mutation rates. We present examples of high-risk and low-risk pathogens. We propose general guidelines for a rational approach to breed durable resistance according to the evolutionary potential of the pathogen.

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Crossref · 10.1146/annurev.phyto.40.120501.101443

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Metadata

Authors
McDonald, B. A., & Linde, C.
Venue
Annual Review of Phytopathology
Published
2002-09
Volume
40
Issue
1
Pages
349-379
ISSN
  • 0066-4286 (ISSN-L)
  • 1545-2107
OpenAlex
W2104484780
Cited by
2,128
References
1
FWCI
2.85
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: P ATHOGEN P OPULATION G ENETICS, E VOLUTIONARY P OTENTIAL, AND D URABLE R ESISTANCE. https://app.cochat.ai/papers/10.1146/annurev.phyto.40.120501.101443
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McDonald, B. A., & Linde, C. (2002). P ATHOGEN P OPULATION G ENETICS, E VOLUTIONARY P OTENTIAL, AND D URABLE R ESISTANCE. Annual Review of Phytopathology. https://doi.org/10.1146/annurev.phyto.40.120501.101443

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.