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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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792

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 (97th percentile)

Citations

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Readers on

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3189 Mendeley
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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 788 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 3,189 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%
Italy 2 <1%
Sweden 2 <1%
Korea, Republic of 1 <1%
Switzerland 1 <1%
Hong Kong 1 <1%
Norway 1 <1%
Other 9 <1%
Unknown 3151 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 802 25%
Researcher 650 20%
Student > Master 334 10%
Student > Bachelor 288 9%
Student > Doctoral Student 153 5%
Other 514 16%
Unknown 448 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 926 29%
Biochemistry, Genetics and Molecular Biology 871 27%
Medicine and Dentistry 202 6%
Neuroscience 129 4%
Computer Science 105 3%
Other 371 12%
Unknown 585 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 792. 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 21 January 2023.
All research outputs
#20,516
of 23,427,600 outputs
Outputs from Cell
#163
of 16,723 outputs
Outputs of similar age
#418
of 317,545 outputs
Outputs of similar age from Cell
#5
of 136 outputs
Altmetric has tracked 23,427,600 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 16,723 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 54.9. 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 317,545 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 97% of its contemporaries.