Package: GGPA
Type: Package
Title: graph-GPA: A graphical model for prioritizing GWAS results and
        investigating pleiotropic architecture
Version: 1.24.0
Date: 2020-02-25
Author: Dongjun Chung, Hang J. Kim, Carter Allen
Maintainer: Dongjun Chung <dongjun.chung@gmail.com>
Description: 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.
License: GPL (>= 2)
URL: https://github.com/dongjunchung/GGPA/
Depends: R (>= 4.0.0), stats, methods, graphics, GGally, network, sna,
        scales, matrixStats
Suggests: BiocStyle
Imports: Rcpp (>= 0.11.3)
LinkingTo: Rcpp, RcppArmadillo
RcppModules: cGGPAmodule
NeedsCompilation: yes
biocViews: Software, StatisticalMethod, Classification,
        GenomeWideAssociation, SNP, Genetics, Clustering,
        MultipleComparison, Preprocessing, GeneExpression,
        DifferentialExpression
SystemRequirements: GNU make
Config/pak/sysreqs: make libicu-dev libssl-dev
Repository: https://bioc-release.r-universe.dev
Date/Publication: 2026-04-28 12:52:28 UTC
RemoteUrl: https://github.com/bioc/GGPA
RemoteRef: RELEASE_3_23
RemoteSha: 04f920158205f777468f5a2ff5a858d4c5e52358
Packaged: 2026-04-29 22:12:29 UTC; root
Built: R 4.6.0; x86_64-w64-mingw32; 2026-04-29 22:14:45 UTC; windows
Archs: x64
