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Two-way mixed-effects methods for joint association analysis using both host and pathogen genomes

Overview of attention for article published in Proceedings of the National Academy of Sciences of the United States of America, May 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

blogs
1 blog
twitter
49 X users

Citations

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60 Dimensions

Readers on

mendeley
131 Mendeley
citeulike
2 CiteULike
Title
Two-way mixed-effects methods for joint association analysis using both host and pathogen genomes
Published in
Proceedings of the National Academy of Sciences of the United States of America, May 2018
DOI 10.1073/pnas.1710980115
Pubmed ID
Authors

Miaoyan Wang, Fabrice Roux, Claudia Bartoli, Carine Huard-Chauveau, Christopher Meyer, Hana Lee, Dominique Roby, Mary Sara McPeek, Joy Bergelson

Abstract

Infectious diseases are often affected by specific pairings of hosts and pathogens and therefore by both of their genomes. The integration of a pair of genomes into genome-wide association mapping can provide an exquisitely detailed view of the genetic landscape of complex traits. We present a statistical method, ATOMM (Analysis with a Two-Organism Mixed Model), that maps a trait of interest to a pair of genomes simultaneously; this method makes use of whole-genome sequence data for both host and pathogen organisms. ATOMM uses a two-way mixed-effect model to test for genetic associations and cross-species genetic interactions while accounting for sample structure including interactions between the genetic backgrounds of the two organisms. We demonstrate the applicability of ATOMM to a joint association study of quantitative disease resistance (QDR) in the Arabidopsis thaliana-Xanthomonas arboricola pathosystem. Our method uncovers a clear host-strain specificity in QDR and provides a powerful approach to identify genetic variants on both genomes that contribute to phenotypic variation.

X Demographics

X Demographics

The data shown below were collected from the profiles of 49 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 131 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 131 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 23%
Researcher 29 22%
Student > Master 14 11%
Student > Bachelor 13 10%
Student > Doctoral Student 9 7%
Other 17 13%
Unknown 19 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 63 48%
Biochemistry, Genetics and Molecular Biology 21 16%
Environmental Science 5 4%
Engineering 4 3%
Mathematics 3 2%
Other 11 8%
Unknown 24 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 35. 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 30 June 2023.
All research outputs
#1,133,911
of 25,335,657 outputs
Outputs from Proceedings of the National Academy of Sciences of the United States of America
#16,836
of 102,776 outputs
Outputs of similar age
#24,481
of 338,190 outputs
Outputs of similar age from Proceedings of the National Academy of Sciences of the United States of America
#337
of 1,001 outputs
Altmetric has tracked 25,335,657 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 102,776 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 39.3. This one has done well, scoring higher than 83% 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 338,190 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 1,001 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.