GGPA-package: graph-GPA

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

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:	0.9.6
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 <[e-mail]>, Dongjun Chung <[e-mail]>

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 (2017), "Improving SNP prioritization and pleiotropic architecture estimation by incorporating prior knowledge using graph-GPA."

See Also

GGPA, assoc, plot, GGPA.

Examples

library(GGPA)

# 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)

dongjunchung/GGPA documentation built on March 1, 2020, 3:43 a.m.