GGPA-package {GGPA} | R Documentation

## graph-GPA

### Description

This package provides functions for fitting graph-GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy.

### Details

Package: | GGPA
Type: | Package
Version: | 0.99.11
Date: | 2018-01-15
License: | GPL (>= 2)
LazyLoad: | yes

This package contains a main class, `GGPA`, which represents graph-GPA model fit. This package contains four main methods, `GGPA`, `assoc`, and `plot`. `GGPA` method fits the graph-GPA model and `assoc` method implements association mapping. `plot` method provides a graph representing genetic relationship among phenotypes.

### Author(s)

Hang J. Kim and Dongjun Chung

Maintainer: Hang J. Kim <hang.kim@uc.edu>, Dongjun Chung <dongjun.chung@gmail.com>

### References

Chung D, Kim H, and Zhao H (2016), "graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture," 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.

### See Also

`GGPA`, `assoc`, `plot`, `GGPA`.

### Examples

    # load simulation data

    data(simulation)

    # fit graph-GPA model

    fit <- GGPA( simulation$pmat, nBurnin=200, nMain=200 )
    fit

    # fit graph-GPA model using a prior phenotype graph
    # as an example, edge 6-7 added & edge 2-3 removed in pgraph

    pgraph <- matrix( 0, ncol(simulation$pmat), ncol(simulation$pmat) )
    pgraph[1,2] <- pgraph[1,3] <- pgraph[6,7] <- pgraph[4,5] <- 1
    fit.pg <- GGPA( simulation$pmat, pgraph, nBurnin=200, nMain=200 )
    fit.pg

    # association mapping for each phenotype

    head(assoc( fit, FDR=0.1, fdrControl="global" ))

    # hypothesis testing for 1st and 2nd phenotype pair

    head(assoc( fit, FDR=0.1, fdrControl="global", i=1, j=2 ))

    # plot phenotype graph

    plot(fit)
    plot(fit.pg)

[Package GGPA version 1.2.0 Index]