↓ Skip to main content

CAPE: An R Package for Combined Analysis of Pleiotropy and Epistasis

Overview of attention for article published in PLoS Computational Biology, October 2013
Altmetric Badge

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 (87th percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

Mentioned by

twitter
20 X users
facebook
1 Facebook page

Citations

dimensions_citation
29 Dimensions

Readers on

mendeley
82 Mendeley
citeulike
3 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
CAPE: An R Package for Combined Analysis of Pleiotropy and Epistasis
Published in
PLoS Computational Biology, October 2013
DOI 10.1371/journal.pcbi.1003270
Pubmed ID
Authors

Anna L. Tyler, Wei Lu, Justin J. Hendrick, Vivek M. Philip, Gregory W. Carter

Abstract

Contemporary genetic studies are revealing the genetic complexity of many traits in humans and model organisms. Two hallmarks of this complexity are epistasis, meaning gene-gene interaction, and pleiotropy, in which one gene affects multiple phenotypes. Understanding the genetic architecture of complex traits requires addressing these phenomena, but interpreting the biological significance of epistasis and pleiotropy is often difficult. While epistasis reveals dependencies between genetic variants, it is often unclear how the activity of one variant is specifically modifying the other. Epistasis found in one phenotypic context may disappear in another context, rendering the genetic interaction ambiguous. Pleiotropy can suggest either redundant phenotype measures or gene variants that affect multiple biological processes. Here we present an R package, R/cape, which addresses these interpretation ambiguities by implementing a novel method to generate predictive and interpretable genetic networks that influence quantitative phenotypes. R/cape integrates information from multiple related phenotypes to constrain models of epistasis, thereby enhancing the detection of interactions that simultaneously describe all phenotypes. The networks inferred by R/cape are readily interpretable in terms of directed influences that indicate suppressive and enhancing effects of individual genetic variants on other variants, which in turn account for the variance in quantitative traits. We demonstrate the utility of R/cape by analyzing a mouse backcross, thereby discovering novel epistatic interactions influencing phenotypes related to obesity and diabetes. R/cape is an easy-to-use, platform-independent R package and can be applied to data from both genetic screens and a variety of segregating populations including backcrosses, intercrosses, and natural populations. The package is freely available under the GPL-3 license at http://cran.r-project.org/web/packages/cape.

X Demographics

X Demographics

The data shown below were collected from the profiles of 20 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 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 4%
Moldova, Republic of 1 1%
Canada 1 1%
Unknown 77 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 39%
Researcher 21 26%
Professor > Associate Professor 7 9%
Student > Postgraduate 5 6%
Student > Bachelor 3 4%
Other 9 11%
Unknown 5 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 40 49%
Biochemistry, Genetics and Molecular Biology 10 12%
Medicine and Dentistry 6 7%
Economics, Econometrics and Finance 3 4%
Environmental Science 2 2%
Other 10 12%
Unknown 11 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 28 November 2020.
All research outputs
#3,062,006
of 25,374,917 outputs
Outputs from PLoS Computational Biology
#2,722
of 8,960 outputs
Outputs of similar age
#27,548
of 224,524 outputs
Outputs of similar age from PLoS Computational Biology
#44
of 143 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 69% 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 224,524 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 87% of its contemporaries.
We're also able to compare this research output to 143 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 69% of its contemporaries.