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Approaches for establishing the function of regulatory genetic variants involved in disease

Overview of attention for article published in Genome Medicine, October 2014
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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 (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

blogs
1 blog
twitter
26 X users

Citations

dimensions_citation
36 Dimensions

Readers on

mendeley
191 Mendeley
citeulike
7 CiteULike
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Title
Approaches for establishing the function of regulatory genetic variants involved in disease
Published in
Genome Medicine, October 2014
DOI 10.1186/s13073-014-0092-4
Pubmed ID
Authors

Julian Charles Knight

Abstract

The diversity of regulatory genetic variants and their mechanisms of action reflect the complexity and context-specificity of gene regulation. Regulatory variants are important in human disease and defining such variants and establishing mechanism is crucial to the interpretation of disease-association studies. This review describes approaches for identifying and functionally characterizing regulatory variants, illustrated using examples from common diseases. Insights from recent advances in resolving the functional epigenomic regulatory landscape in which variants act are highlighted, showing how this has enabled functional annotation of variants and the generation of hypotheses about mechanism of action. The utility of quantitative trait mapping at the transcript, protein and metabolite level to define association of specific genes with particular variants and further inform disease associations are reviewed. Establishing mechanism of action is an essential step in resolving functional regulatory variants, and this review describes how this is being facilitated by new methods for analyzing allele-specific expression, mapping chromatin interactions and advances in genome editing. Finally, integrative approaches are discussed together with examples highlighting how defining the mechanism of action of regulatory variants and identifying specific modulated genes can maximize the translational utility of genome-wide association studies to understand the pathogenesis of diseases and discover new drug targets or opportunities to repurpose existing drugs to treat them.

X Demographics

X Demographics

The data shown below were collected from the profiles of 26 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 191 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 3%
United Kingdom 2 1%
Italy 1 <1%
South Africa 1 <1%
Norway 1 <1%
Canada 1 <1%
Netherlands 1 <1%
Spain 1 <1%
Argentina 1 <1%
Other 0 0%
Unknown 177 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 60 31%
Researcher 35 18%
Student > Bachelor 19 10%
Student > Master 12 6%
Student > Doctoral Student 10 5%
Other 35 18%
Unknown 20 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 84 44%
Biochemistry, Genetics and Molecular Biology 46 24%
Medicine and Dentistry 17 9%
Computer Science 7 4%
Neuroscience 4 2%
Other 6 3%
Unknown 27 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 20. 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 January 2015.
All research outputs
#1,711,859
of 24,220,739 outputs
Outputs from Genome Medicine
#375
of 1,497 outputs
Outputs of similar age
#20,037
of 265,258 outputs
Outputs of similar age from Genome Medicine
#11
of 57 outputs
Altmetric has tracked 24,220,739 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,497 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.6. This one has gotten more attention than average, scoring higher than 74% 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 265,258 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 57 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.