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

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.

Package details

AuthorDongjun Chung, Hang J. Kim, Carter Allen
Bioconductor views Classification Clustering DifferentialExpression GeneExpression Genetics GenomeWideAssociation MultipleComparison Preprocessing SNP Software StatisticalMethod
MaintainerDongjun Chung <dongjun.chung@gmail.com>
LicenseGPL (>= 2)
Version1.2.0
URL https://github.com/dongjunchung/GGPA/
Package repositoryView on Bioconductor
Installation Install the latest version of this package by entering the following in R:
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("GGPA")

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GGPA documentation built on Nov. 8, 2020, 5:37 p.m.