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Characterizing genetic interactions in human disease association studies using statistical epistasis networks

Overview of attention for article published in BMC Bioinformatics, January 2011
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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 (85th percentile)

Mentioned by

twitter
20 tweeters

Citations

dimensions_citation
89 Dimensions

Readers on

mendeley
121 Mendeley
citeulike
7 CiteULike
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Title
Characterizing genetic interactions in human disease association studies using statistical epistasis networks
Published in
BMC Bioinformatics, January 2011
DOI 10.1186/1471-2105-12-364
Pubmed ID
Authors

Ting Hu, Nicholas A Sinnott-Armstrong, Jeff W Kiralis, Angeline S Andrew, Margaret R Karagas, Jason H Moore

Abstract

Epistasis is recognized ubiquitous in the genetic architecture of complex traits such as disease susceptibility. Experimental studies in model organisms have revealed extensive evidence of biological interactions among genes. Meanwhile, statistical and computational studies in human populations have suggested non-additive effects of genetic variation on complex traits. Although these studies form a baseline for understanding the genetic architecture of complex traits, to date they have only considered interactions among a small number of genetic variants. Our goal here is to use network science to determine the extent to which non-additive interactions exist beyond small subsets of genetic variants. We infer statistical epistasis networks to characterize the global space of pairwise interactions among approximately 1500 Single Nucleotide Polymorphisms (SNPs) spanning nearly 500 cancer susceptibility genes in a large population-based study of bladder cancer.

Twitter Demographics

The data shown below were collected from the profiles of 20 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 121 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
Germany 4 3%
United Kingdom 3 2%
Switzerland 2 2%
Norway 1 <1%
France 1 <1%
Ireland 1 <1%
Moldova, Republic of 1 <1%
Italy 1 <1%
Other 3 2%
Unknown 100 83%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 45 37%
Researcher 29 24%
Professor > Associate Professor 12 10%
Student > Master 11 9%
Professor 5 4%
Other 12 10%
Unknown 7 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 53 44%
Computer Science 22 18%
Biochemistry, Genetics and Molecular Biology 12 10%
Medicine and Dentistry 10 8%
Mathematics 7 6%
Other 6 5%
Unknown 11 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 26 April 2019.
All research outputs
#1,935,745
of 16,202,841 outputs
Outputs from BMC Bioinformatics
#746
of 5,867 outputs
Outputs of similar age
#22,439
of 222,080 outputs
Outputs of similar age from BMC Bioinformatics
#51
of 346 outputs
Altmetric has tracked 16,202,841 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,867 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done well, scoring higher than 87% 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 222,080 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 346 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.