Evidence map›Paper›PMID 24204223›Full record

ArticlePLoS computational biology2013

CAPE: an R package for combined analysis of pleiotropy and epistasis.

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

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
2.1field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 33 citations in OpenAlex.

  1. Trial
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Testing PleiotropyG3 (Bethesda, Md.) · 2019
    Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Review
  14. Article
  15. Systems Genetic Analysis in GeneNetwork.org.Current protocols in neuroscience · 2017
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Anna L TylerThe Jackson Laboratory, Bar Harbor, Maine, United States of America.
Wei Lu
Justin J Hendrick
Vivek M Philip
Gregory W Carter
Jackson Laboratory · US

Funding

Shared Resource ManagementP30CA034196 · NCI · JACKSON LABORATORY · PI Jeffrey Hsu-Min Chuang · 1985 to 2026
$61.9M
UNC and PERLEGEN, IncP50GM076468 · NIGMS · JACKSON LABORATORY · PI PETKOV, PETKO M · 2006 to 2015
$31.0M
Inference and Testing of Quantitative Models of Genetic InteractionK25GM079404 · NIGMS · JACKSON LABORATORY · PI CARTER, GREGORY W · 2007 to 2011
$545k
Howard Hughes Medical InstituteNCI NIH HHS CA034196NCI NIH HHS P30 CA034196NIGMS NIH HHS K25 GM079404NIGMS NIH HHS P50 GM076468
6 · The paper itself

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.

Indexed as

Models, GeneticSoftwareAlgorithmsAnimalsComputational BiologyDiabetes Mellitus, Type 2Disease Models, AnimalEpistasis, GeneticGenetic PleiotropyLinear ModelsMiceObesity

Identifiers

PMID24204223
PMCPMC3808451
OpenAlexW2019477162

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.