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 |
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Author | Dongjun Chung, Hang J. Kim, Carter Allen |
Bioconductor views | Classification Clustering DifferentialExpression GeneExpression Genetics GenomeWideAssociation MultipleComparison Preprocessing SNP Software StatisticalMethod |
Maintainer | Dongjun Chung <dongjun.chung@gmail.com> |
License | GPL (>= 2) |
Version | 1.2.0 |
URL | https://github.com/dongjunchung/GGPA/ |
Package repository | View on Bioconductor |
Installation |
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