GGPA-class: Class "GGPA"

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

Description

This class represents graph-GPA model fit.

Objects from the Class

Objects can be created by calls of the form new("GGPA", ...).

Slots

fit:

Object of class "list", representing the MCMC draws.

summary:

Object of class "list", representing the summary statistics.

setting:

Object of class "list", representing the setting for graph-GPA model fitting.

gwasPval:

Object of class "matrix", representing the p-value matrix from GWAS data.

pgraph:

Object of class "matrix", representing the prior phenotype graph.

Methods

show

signature(object = "GGPA"): provide brief summary of the object.

plot

signature(x = "GGPA", y = "missing", pCutoff = 0.5, betaCI = 0.95): plot a phenotype graph. Nodes i and j are connected if the posterior probability of E_ij > pCutoff and the posterior probability of beta_ij > betaCI.

fdr

signature(object = "GGPA", i=NULL, j=NULL): provide local FDR. By default (i.e., i=NULL, j=NULL), it returns a matrix of local FDR that a SNP is not associated with each phenotype (i.e., marginal FDR), where the order of columns is same as that in input GWAS data. If phenotype indices i and j are specified, a vector of corresponding local FDR is provided.

estimates

signature(object = "GGPA"): extract parameter estimates from graph-GPA model fit.

Author(s)

Hang J. Kim, Dongjun Chung

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.

Examples

showClass("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)

# Plot GGPA model fit
plot(fit)

head(fdr( fit ))
head(fdr( fit, i=1, j=2 ))
str(estimates( fit ))

GGPA documentation built on Nov. 8, 2020, 5:37 p.m.