GGPA {GGPA} | R Documentation

## Fit graph-GPA model

### Description

Fit graph-GPA model.

### Usage

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

### Arguments

`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.

### Details

`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.

### Value

Construct `GGPA` class object.

### Author(s)

Hang J. Kim and 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

`assoc`, `GGPA`.

### Examples

    # 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)

[Package GGPA version 1.2.0 Index]