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An Expanded View of Complex Traits: From Polygenic to Omnigenic

Overview of attention for article published in Cell, June 2017
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778

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 (99th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Citations

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1196 Dimensions

Readers on

mendeley
2657 Mendeley
citeulike
18 CiteULike
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Title
An Expanded View of Complex Traits: From Polygenic to Omnigenic
Published in
Cell, June 2017
DOI 10.1016/j.cell.2017.05.038
Pubmed ID
Authors

Evan A. Boyle, Yang I. Li, Jonathan K. Pritchard

Abstract

A central goal of genetics is to understand the links between genetic variation and disease. Intuitively, one might expect disease-causing variants to cluster into key pathways that drive disease etiology. But for complex traits, association signals tend to be spread across most of the genome-including near many genes without an obvious connection to disease. We propose that gene regulatory networks are sufficiently interconnected such that all genes expressed in disease-relevant cells are liable to affect the functions of core disease-related genes and that most heritability can be explained by effects on genes outside core pathways. We refer to this hypothesis as an "omnigenic" model.

Twitter Demographics

The data shown below were collected from the profiles of 851 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 2,657 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 14 <1%
United Kingdom 4 <1%
Spain 3 <1%
Sweden 2 <1%
France 2 <1%
Italy 2 <1%
Netherlands 1 <1%
Hong Kong 1 <1%
Norway 1 <1%
Other 9 <1%
Unknown 2618 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 713 27%
Researcher 589 22%
Student > Master 273 10%
Student > Bachelor 235 9%
Student > Doctoral Student 133 5%
Other 456 17%
Unknown 258 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 856 32%
Biochemistry, Genetics and Molecular Biology 742 28%
Medicine and Dentistry 174 7%
Neuroscience 114 4%
Computer Science 98 4%
Other 296 11%
Unknown 377 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 778. 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 11 January 2021.
All research outputs
#11,637
of 16,649,729 outputs
Outputs from Cell
#99
of 15,467 outputs
Outputs of similar age
#358
of 274,363 outputs
Outputs of similar age from Cell
#6
of 136 outputs
Altmetric has tracked 16,649,729 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 15,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 40.5. This one has done particularly well, scoring higher than 99% 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 274,363 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 99% of its contemporaries.
We're also able to compare this research output to 136 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 96% of its contemporaries.