GGPA
graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture
Bioconductor version: 3.23 · Package version: 1.24.0
Other Bioconductor versions
devel is the development version; release is the current stable one.
3.24 (devel), 3.23 (release)
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.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("GGPA")Details
| Maintainer | Dongjun Chung <dongjun.chung@gmail.com> |
| Author | Dongjun Chung, Hang J. Kim, Carter Allen |
| License | GPL (>= 2) |
| URL | https://github.com/dongjunchung/GGPA/ |
| System Requirements | GNU make |
| Source branch | RELEASE_3_23 |
| Build report | Bioconductor build system, r-universe |
| biocViews | Classification, Clustering, DifferentialExpression, GeneExpression, Genetics, GenomeWideAssociation, MultipleComparison, Preprocessing, SNP, Software, StatisticalMethod |
| Package Short Url | https://bioconductor.org/packages/GGPA/ |
Citation
From within R, enter citation("GGPA"):
Dongjun Chung, Hang J. Kim, Carter Allen. GGPA: graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture. doi:10.18129/B9.bioc.GGPA, R package version 1.24.0, https://bioconductor.org/packages/GGPA.
Generated from the package metadata; it may differ from the package's own citation.
Documentation
Download
Follow the installation instructions to use this package in your R session.
| Source package | GGPA_1.24.0.tar.gz |
| Windows binary (x86_64) | GGPA_1.24.0.zip |
| macOS binary (arm64) | GGPA_1.24.0.tgz |
| macOS binary (x86_64) | GGPA_1.24.0.tgz |
Dependencies
Depends: R (>= 4.0.0), stats, methods, graphics, GGally, network, sna, scales, matrixStats
Imports: Rcpp (>= 0.11.3)
LinkingTo: Rcpp, RcppArmadillo
Suggests: BiocStyle