GGPA-class {GGPA} | R Documentation

## Class "GGPA"

### 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 ))

[Package GGPA version 1.6.0 Index]