GGPA

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

Bioconductor version: Release (3.19)

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 "4.4") and enter:


if (!require("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")

GGPA	PDF	R Script
Reference Manual	PDF
NEWS	Text

Need some help? Ask on the Bioconductor Support site!

Details

biocViews	Classification, Clustering, DifferentialExpression, GeneExpression, Genetics, GenomeWideAssociation, MultipleComparison, Preprocessing, SNP, Software, StatisticalMethod
Version	1.16.0
In Bioconductor since	BioC 3.11 (R-4.0) (4.5 years)
License	GPL (>= 2)
Depends	R (>= 4.0.0), stats, methods, graphics, GGally, network, sna, scales, matrixStats
Imports	Rcpp (>= 0.11.3)
System Requirements	GNU make
URL	https://github.com/dongjunchung/GGPA/

Suggests	BiocStyle
Linking To	Rcpp, RcppArmadillo
Enhances
Depends On Me
Imports Me
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Build Report	Build Report

Package Archives

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

Source Package	GGPA_1.16.0.tar.gz
Windows Binary (x86_64)	GGPA_1.16.0.zip
macOS Binary (x86_64)	GGPA_1.16.0.tgz
macOS Binary (arm64)	GGPA_1.16.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/GGPA
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/GGPA
Bioc Package Browser	https://code.bioconductor.org/browse/GGPA/
Package Short Url	https://bioconductor.org/packages/GGPA/
Package Downloads Report	Download Stats