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The continuum of causality in human genetic disorders

Overview of attention for article published in Genome Biology, November 2016
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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 (94th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

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49 X users
wikipedia
1 Wikipedia page

Citations

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113 Dimensions

Readers on

mendeley
162 Mendeley
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1 CiteULike
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Title
The continuum of causality in human genetic disorders
Published in
Genome Biology, November 2016
DOI 10.1186/s13059-016-1107-9
Pubmed ID
Authors

Nicholas Katsanis

Abstract

Studies of human genetic disorders have traditionally followed a reductionist paradigm. Traits are defined as Mendelian or complex based on family pedigree and population data, whereas alleles are deemed rare, common, benign, or deleterious based on their population frequencies. The availability of exome and genome data, as well as gene and allele discovery for various conditions, is beginning to challenge classic definitions of genetic causality. Here, I discuss recent advances in our understanding of the overlap between rare and complex diseases and the context-dependent effect of both rare and common alleles that underscores the need for revising the traditional categorizations of genetic traits.

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 162 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 1%
Brazil 1 <1%
Italy 1 <1%
Canada 1 <1%
United States 1 <1%
Unknown 156 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 39 24%
Student > Ph. D. Student 35 22%
Student > Master 14 9%
Student > Bachelor 14 9%
Professor 8 5%
Other 23 14%
Unknown 29 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 54 33%
Agricultural and Biological Sciences 31 19%
Medicine and Dentistry 25 15%
Computer Science 4 2%
Engineering 4 2%
Other 11 7%
Unknown 33 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 33. 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 03 April 2020.
All research outputs
#1,207,126
of 25,452,734 outputs
Outputs from Genome Biology
#905
of 4,476 outputs
Outputs of similar age
#24,152
of 418,295 outputs
Outputs of similar age from Genome Biology
#24
of 61 outputs
Altmetric has tracked 25,452,734 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 4,476 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has done well, scoring higher than 79% 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 418,295 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 94% of its contemporaries.
We're also able to compare this research output to 61 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 62% of its contemporaries.