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

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 comp...

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Autores principales: Tyler, Anna L., Lu, Wei, Hendrick, Justin J., Philip, Vivek M., Carter, Gregory W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3808451/
https://www.ncbi.nlm.nih.gov/pubmed/24204223
http://dx.doi.org/10.1371/journal.pcbi.1003270
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author Tyler, Anna L.
Lu, Wei
Hendrick, Justin J.
Philip, Vivek M.
Carter, Gregory W.
author_facet Tyler, Anna L.
Lu, Wei
Hendrick, Justin J.
Philip, Vivek M.
Carter, Gregory W.
author_sort Tyler, Anna L.
collection PubMed
description 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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spelling pubmed-38084512013-11-07 CAPE: An R Package for Combined Analysis of Pleiotropy and Epistasis Tyler, Anna L. Lu, Wei Hendrick, Justin J. Philip, Vivek M. Carter, Gregory W. PLoS Comput Biol Research Article 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. Public Library of Science 2013-10-24 /pmc/articles/PMC3808451/ /pubmed/24204223 http://dx.doi.org/10.1371/journal.pcbi.1003270 Text en © 2013 Tyler et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Tyler, Anna L.
Lu, Wei
Hendrick, Justin J.
Philip, Vivek M.
Carter, Gregory W.
CAPE: An R Package for Combined Analysis of Pleiotropy and Epistasis
title CAPE: An R Package for Combined Analysis of Pleiotropy and Epistasis
title_full CAPE: An R Package for Combined Analysis of Pleiotropy and Epistasis
title_fullStr CAPE: An R Package for Combined Analysis of Pleiotropy and Epistasis
title_full_unstemmed CAPE: An R Package for Combined Analysis of Pleiotropy and Epistasis
title_short CAPE: An R Package for Combined Analysis of Pleiotropy and Epistasis
title_sort cape: an r package for combined analysis of pleiotropy and epistasis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3808451/
https://www.ncbi.nlm.nih.gov/pubmed/24204223
http://dx.doi.org/10.1371/journal.pcbi.1003270
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