graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture
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DESCRIPTION
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README.md
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README.md

graph-GPA

graph-GPA is a graphical model for prioritizing GWAS results and investigating pleiotropic architecture (Chung et al., 2017). 'GGPA' package provides user-friendly interface to fit graph-GPA models, implement association mapping, and generate a phenotype graph. From its version 0.9.5, it also allows to utilize a prior phenotype graph (Kim et al., 2017).

DDNet

DDNet is a web interface to infer the disease-disease network from the literature mining. Using DDNet, users can query diseases of interest, investigate relationship among these diseases visually and dynamically, and download various information including an adjacency matrix. This adjacency matrix can be used as a prior phenotype graph to guide the estimation of pleiotropic architecture. Please refer Kim et al. (2017) and the R package vignette for more details. DDNet is accessible from the following web address:

http://chunglab.io/ddnet/

Installation

To install the development version of GGPA, it's easiest to use the 'devtools' package. Note that GGPA depends on the 'Rcpp' package, which also requires appropriate setting of Rtools and Xcode for Windows and Mac OS/X, respectively.

#install.packages("devtools")
library(devtools)
install_github("dongjunchung/GGPA")

Usage

The R package vignette will provide a good start point for the genetic analysis using GGPA package, including the overview of GGPA package and the example command lines:

library(GGPA)
vignette("GGPA-example")

The following two help pages will also provide quick references for GGPA package and the example command lines:

package?GGPA
class?GGPA

References

Chung D, Kim H, and Zhao H (2017), "graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture," PLOS Computational Biology, 13(2): e1005388.

Kim H, Yu Z, Lawson A, Zhao H, and Chung D (2018), "Improving SNP prioritization and pleiotropic architecture estimation by incorporating prior knowledge using graph-GPA," Bioinformatics, bty061.