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Genome-Wide Association Studies In Plant Pathosystems: Toward an Ecological Genomics Approach

Overview of attention for article published in Frontiers in Plant Science, May 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

blogs
1 blog
twitter
9 X users
facebook
1 Facebook page
wikipedia
4 Wikipedia pages

Citations

dimensions_citation
132 Dimensions

Readers on

mendeley
261 Mendeley
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Title
Genome-Wide Association Studies In Plant Pathosystems: Toward an Ecological Genomics Approach
Published in
Frontiers in Plant Science, May 2017
DOI 10.3389/fpls.2017.00763
Pubmed ID
Authors

Claudia Bartoli, Fabrice Roux

Abstract

The emergence and re-emergence of plant pathogenic microorganisms are processes that imply perturbations in both host and pathogen ecological niches. Global change is largely assumed to drive the emergence of new etiological agents by altering the equilibrium of the ecological habitats which in turn places hosts more in contact with pathogen reservoirs. In this context, the number of epidemics is expected to increase dramatically in the next coming decades both in wild and crop plants. Under these considerations, the identification of the genetic variants underlying natural variation of resistance is a pre-requisite to estimate the adaptive potential of wild plant populations and to develop new breeding resistant cultivars. On the other hand, the prediction of pathogen's genetic determinants underlying disease emergence can help to identify plant resistance alleles. In the genomic era, whole genome sequencing combined with the development of statistical methods led to the emergence of Genome Wide Association (GWA) mapping, a powerful tool for detecting genomic regions associated with natural variation of disease resistance in both wild and cultivated plants. However, GWA mapping has been less employed for the detection of genetic variants associated with pathogenicity in microbes. Here, we reviewed GWA studies performed either in plants or in pathogenic microorganisms (bacteria, fungi and oomycetes). In addition, we highlighted the benefits and caveats of the emerging joint GWA mapping approach that allows for the simultaneous identification of genes interacting between genomes of both partners. Finally, based on co-evolutionary processes in wild populations, we highlighted a phenotyping-free joint GWA mapping approach as a promising tool for describing the molecular landscape underlying plant - microbe interactions.

X Demographics

X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 261 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 <1%
Unknown 260 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 55 21%
Student > Master 42 16%
Researcher 37 14%
Student > Bachelor 20 8%
Student > Doctoral Student 16 6%
Other 36 14%
Unknown 55 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 136 52%
Biochemistry, Genetics and Molecular Biology 35 13%
Environmental Science 5 2%
Nursing and Health Professions 3 1%
Social Sciences 2 <1%
Other 11 4%
Unknown 69 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 16. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 July 2024.
All research outputs
#2,207,710
of 24,820,264 outputs
Outputs from Frontiers in Plant Science
#908
of 23,688 outputs
Outputs of similar age
#40,915
of 318,854 outputs
Outputs of similar age from Frontiers in Plant Science
#34
of 595 outputs
Altmetric has tracked 24,820,264 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 23,688 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 318,854 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 595 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.