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

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/GGPA.R

Fit graph-GPA model.

1	GGPA( gwasPval, pgraph=NULL, nBurnin=10000, nMain=40000, lbPval=1e-10, verbose=1 )

`gwasPval`	p-value matrix from GWAS data, where row and column correspond to SNP and phenotype, respectively.
`pgraph`	A binary matrix representing the prior phenotype graph, where its rows and columns match the columns of `gwasPval`.
`nBurnin`	Number of burn-in iterations. Default is 10000.
`nMain`	Number of main MCMC iterations. Default is 40000.
`lbPval`	Lower bound for GWAS p-value. Any GWAS p-values smaller than `lbPval` are set to `lbPval`. Default is 1e-10.
`verbose`	Amount of progress report during the fitting procedure. Possible values are 0 (minimal output), 1, 2, or 3 (maximal output). Default is 1.

GGPA fits the graph-GPA model. It requires to provide GWAS p-value to gwasPval. If a phenotype graph is provided in pgraph, it is utilized to guide the phenotype graph estimation. Based on this GGPA fit, assoc implements association mapping and plot provides a phenotype graph.

Construct GGPA class object.

Hang J. Kim and Dongjun Chung

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.

assoc, GGPA.

# Load the included simulation data
data(simulation)

# fit GGPA model with 200 iterations and a burn-in of 200 iterations
# Note that we recommend more than 200 iterations in practice
fit <- GGPA( simulation$pmat, nMain = 200, nBurnin = 200)


# Association mapping with FDR of 0.1 and global control
head(assoc( fit, FDR=0.1, fdrControl="global" ))

# We may specift i = 1 and j = 2 if we are interested in that specific phenotype
head(assoc( fit, FDR=0.1, fdrControl="global", i=1, j=2 )) 

# plot the GGPA model fit
plot(fit)