This is the released version of GGPA; for the devel version, see GGPA.
graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architectureBioconductor version: Release (3.21)
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 (from within R, entercitation("GGPA")): Installation
To install this package, start R (version "4.5") 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.
DocumentationTo view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("GGPA")Follow Installation instructions to use this package in your R session.
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