recipe bioconductor-ggpa

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



GPL (>= 2)



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 bioconductor-ggpa

(downloads) docker_bioconductor-ggpa



depends libblas:


depends libgcc-ng:


depends liblapack:


depends libstdcxx-ng:


depends r-base:


depends r-ggally:

depends r-matrixstats:

depends r-network:

depends r-rcpp:


depends r-rcpparmadillo:

depends r-scales:

depends r-sna:



You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-ggpa

and update with::

   mamba update bioconductor-ggpa

To create a new environment, run:

mamba create --name myenvname bioconductor-ggpa

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `bioconductor-ggpa/tags`_ for valid values for ``<tag>``)

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