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This is the development version of GGPA; for the stable release version, see GGPA.

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

Bioconductor version: Development (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>

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biocViews Classification, Clustering, DifferentialExpression, GeneExpression, Genetics, GenomeWideAssociation, MultipleComparison, Preprocessing, SNP, Software, StatisticalMethod
Version 1.15.0
In Bioconductor since BioC 3.11 (R-4.0) (4 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
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