assoc {GGPA} | R Documentation

## Association mapping

### Description

Association mapping.

### Usage

    assoc( object, ... )
    ## S4 method for signature 'GGPA'
    assoc( object, FDR=0.05, fdrControl="global", i=NULL, j=NULL )

### Arguments

`object` | A GGPA model fit as obtained by GGPA().
`FDR` | The desired FDR level.
`fdrControl` | Method to control FDR. Possible values are "global" (global FDR control) and "local" (local FDR control). Default is "global".
`i` | Index for the first phenotype used in association mapping. See the details about how users can specify the pattern.
`j` | Index for the second phenotype used in association mapping. See the details about how users can specify the pattern.
`...` | Other parameters to be passed through to generic `assoc`.

### Details

`assoc` uses the direct posterior probability approach of Newton et al. (2004) to control global FDR in association mapping.

By default (i.e., `i=NULL, j=NULL`), `assoc` implements association mapping for each phenotype. If users are interested in identifying SNPs associated with a pair of phenotypes, users can specify indices of phenotypes of interest using the arguments `i` and `j`. Note that both `i` and `j` should be either NULL or numeric.

### Value

If `i=NULL, j=NULL`, returns a binary matrix indicating association of SNPs for each phenotype, where its rows and columns match those of input p-value matrix for function `GGPA`. Otherwise, returns a binary vector indicating association of SNPs for i-th and j-th phenotype pair.

### 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 (2017), "Improving SNP prioritization and pleiotropic architecture estimation by incorporating prior knowledge using graph-GPA."

Newton MA, Noueiry A, Sarkar D, and Ahlquist P (2004), "Detecting differential gene expression with a semiparametric hierarchical mixture method," Biostatistics, Vol. 5, pp. 155-176.

### See Also

`GGPA`, `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 ))

[Package GGPA version 1.4.0 Index]