GGPA
graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture
Bioconductor version: Release (3.17)
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("GGPA")):
Installation
To install this package, start R (version "4.3") 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 | R Script | |
| Reference Manual | ||
| NEWS | Text |
Details
| biocViews | Classification, Clustering, DifferentialExpression, GeneExpression, Genetics, GenomeWideAssociation, MultipleComparison, Preprocessing, SNP, Software, StatisticalMethod |
| Version | 1.12.1 |
| In Bioconductor since | BioC 3.11 (R-4.0) (3.5 years) |
| License | GPL (>= 2) |
| Depends | R (>= 4.0.0), stats, methods, graphics, GGally, network, sna, scales, matrixStats |
| Imports | Rcpp (>= 0.11.3) |
| Linking To | Rcpp, RcppArmadillo |
| Suggests | BiocStyle |
| System Requirements | GNU make |
| Enhances | |
| URL | https://github.com/dongjunchung/GGPA/ |
See More
| 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_1.12.1.tar.gz |
| Windows Binary | GGPA_1.12.1.zip |
| macOS Binary (x86_64) | GGPA_1.12.1.tgz |
| macOS Binary (arm64) | GGPA_1.12.1.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 |
| Old Source Packages for BioC 3.17 | Source Archive |