Package: GGPA
Type: Package
Title: graph-GPA: A graphical model for prioritizing GWAS results and
        investigating pleiotropic architecture
Version: 1.16.0
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
git_url: https://git.bioconductor.org/packages/GGPA
git_branch: RELEASE_3_19
git_last_commit: 2a670b3
git_last_commit_date: 2024-04-30
Repository: Bioconductor 3.19
Date/Publication: 2024-04-30
Packaged: 2024-05-01 03:37:21 UTC; biocbuild
Built: R 4.4.0; x86_64-apple-darwin20; 2024-05-01 11:59:15 UTC; unix
