GGPA

DOI: 10.18129/B9.bioc.GGPA    

graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture

Bioconductor version: Release (3.11)

Genome-wide association studies (GWAS) is a widely used tool for identification of genetic variants associated with phenotypes and diseases, though complex diseases featuring many genetic variants with small effects present difficulties for traditional these studies. By leveraging pleiotropy, the statistical power of a single GWAS can be increased. This package provides functions for fitting graph-GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy. 'GGPA' package provides user-friendly interface to fit graph-GPA models, implement association mapping, and generate a phenotype graph.

Author: Dongjun Chung, Hang J. Kim, Carter Allen

Maintainer: Dongjun Chung <dongjun.chung at gmail.com>

Citation (from within R, enter citation("GGPA")):

Installation

To install this package, start R (version "4.0") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("GGPA")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("GGPA")

 

PDF R Script GGPA
PDF   Reference Manual
Text   NEWS

Details

biocViews Classification, Clustering, DifferentialExpression, GeneExpression, Genetics, GenomeWideAssociation, MultipleComparison, Preprocessing, SNP, Software, StatisticalMethod
Version 1.0.0
In Bioconductor since BioC 3.11 (R-4.0) (< 6 months)
License GPL (>= 2)
Depends R (>= 4.0.0), stats, methods, graphics, GGally, network, sna, scales, matrixStats
Imports Rcpp (>= 0.11.3)
LinkingTo Rcpp, RcppArmadillo
Suggests BiocStyle
SystemRequirements GNU make
Enhances
URL https://github.com/dongjunchung/GGPA/
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report  

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package GGPA_1.0.0.tar.gz
Windows Binary GGPA_1.0.0.zip (32- & 64-bit)
macOS 10.13 (High Sierra) GGPA_1.0.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/GGPA
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/GGPA
Package Short Url https://bioconductor.org/packages/GGPA/
Package Downloads Report Download Stats
Old Source Packages for BioC 3.11 Source Archive

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