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

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

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Geographical breakdown

Country Count As %
United States 3 4%
Moldova, Republic of 1 1%
Canada 1 1%
Unknown 79 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 40%
Researcher 21 25%
Professor > Associate Professor 7 8%
Student > Postgraduate 5 6%
Student > Bachelor 3 4%
Other 9 11%
Unknown 5 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 40 48%
Biochemistry, Genetics and Molecular Biology 10 12%
Medicine and Dentistry 6 7%
Mathematics 4 5%
Economics, Econometrics and Finance 3 4%
Other 10 12%
Unknown 11 13%