GGPA-package {GGPA} | R Documentation |
This package provides functions for fitting graph-GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy.
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.
Hang J. Kim and Dongjun Chung
Maintainer: Hang J. Kim <hang.kim@uc.edu>, Dongjun Chung <dongjun.chung@gmail.com>
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.
# 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)