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Evaluating the Performance of Fine-Mapping Strategies at Common Variant GWAS Loci

Overview of attention for article published in PLoS Genetics, September 2015
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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 (89th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

twitter
31 tweeters

Citations

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19 Dimensions

Readers on

mendeley
115 Mendeley
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2 CiteULike
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Title
Evaluating the Performance of Fine-Mapping Strategies at Common Variant GWAS Loci
Published in
PLoS Genetics, September 2015
DOI 10.1371/journal.pgen.1005535
Pubmed ID
Authors

Martijn van de Bunt, Adrian Cortes, Matthew A. Brown, Andrew P. Morris, Mark I. McCarthy

Abstract

The growing availability of high-quality genomic annotation has increased the potential for mechanistic insights when the specific variants driving common genome-wide association signals are accurately localized. A range of fine-mapping strategies have been advocated, and specific successes reported, but the overall performance of such approaches, in the face of the extensive linkage disequilibrium that characterizes the human genome, is not well understood. Using simulations based on sequence data from the 1000 Genomes Project, we quantify the extent to which fine-mapping, here conducted using an approximate Bayesian approach, can be expected to lead to useful improvements in causal variant localization. We show that resolution is highly variable between loci, and that performance is severely degraded as the statistical power to detect association is reduced. We confirm that, where causal variants are shared between ancestry groups, further improvements in performance can be obtained in a trans-ethnic fine-mapping design. Finally, using empirical data from a recently published genome-wide association study for ankylosing spondylitis, we provide empirical confirmation of the behaviour of the approximate Bayesian approach and demonstrate that seven of twenty-six loci can be fine-mapped to fewer than ten variants.

Twitter Demographics

The data shown below were collected from the profiles of 31 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 4%
United Kingdom 5 4%
Israel 1 <1%
Nigeria 1 <1%
Australia 1 <1%
Finland 1 <1%
Spain 1 <1%
Netherlands 1 <1%
Unknown 99 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 33 29%
Researcher 33 29%
Student > Master 15 13%
Student > Bachelor 9 8%
Professor 7 6%
Other 18 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 52 45%
Biochemistry, Genetics and Molecular Biology 25 22%
Unspecified 17 15%
Medicine and Dentistry 13 11%
Mathematics 3 3%
Other 5 4%

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 September 2015.
All research outputs
#800,758
of 11,498,175 outputs
Outputs from PLoS Genetics
#1,166
of 5,992 outputs
Outputs of similar age
#25,769
of 242,594 outputs
Outputs of similar age from PLoS Genetics
#42
of 216 outputs
Altmetric has tracked 11,498,175 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,992 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.2. This one has done well, scoring higher than 80% 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 242,594 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 89% of its contemporaries.
We're also able to compare this research output to 216 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.