assoc {GPA} | R Documentation

## Association mapping

### Description

Association mapping.

### Usage

    assoc( object, ... )
    ## S4 method for signature 'GPA'
    assoc( object, FDR=0.05, fdrControl="global", pattern=NULL )

### Arguments

`object` | GPA model fit.
`FDR` | FDR level.
`fdrControl` | Method to control FDR. Possible values are "global" (global FDR control) and "local" (local FDR control). Default is "global".
`pattern` | Pattern for association mapping. By default (i.e., `pattern=NULL`), `assoc` returns a binary matrix indicating association of SNPs for each phenotypes. If a pattern is specified, a corresponding binary vector is provided. 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.

Users can specify the pattern using 1 and * in `pattern` argument, where 1 and * indicate phenotypes of interest and phenotypes that are not of interest, respectively. For example, when there are three phenotypes, `pattern="111"` means a SNP associated with all of three phenotypes, while `pattern="11*"` means a SNP associated with the first two phenotypes (i.e., association with the third phenotype is ignored (averaged out)).

### Value

If `pattern=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 `GPA`. Otherwise, returns a binary vector indicating association of SNPs for the phenotype combination of interest.

### Author(s)

Dongjun Chung

### References

Chung D*, Yang C*, Li C, Gelernter J, and Zhao H (2014), "GPA: A statistical approach to prioritizing GWAS results by integrating pleiotropy information and annotation data," PLoS Genetics, 10: e1004787. (* joint first authors)

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

`GPA`, `GPA`.

### Examples

    # simulator function

    simulator <- function( risk.ind, nsnp=20000, alpha=0.6 ) {

      m <- length(risk.ind)

      p.sig <- rbeta( m, alpha, 1 )
      pvec <- runif(nsnp)
      pvec[ risk.ind ] <- p.sig

      return(pvec)
    }

    # run simulation

    set.seed(12345)
    nsnp <- 1000
    alpha <- 0.3
    pmat <- matrix( NA, nsnp, 5 )

    pmat[,1] <- simulator( c(1:200), nsnp=nsnp, alpha=alpha )
    pmat[,2] <- simulator( c(51:250), nsnp=nsnp, alpha=alpha )
    pmat[,3] <- simulator( c(401:600), nsnp=nsnp, alpha=alpha )
    pmat[,4] <- simulator( c(451:750), nsnp=nsnp, alpha=alpha )
    pmat[,5] <- simulator( c(801:1000), nsnp=nsnp, alpha=alpha )

    ann <- rbinom(n = nrow(pmat), size = 1, prob = 0.15)
    ann <- as.matrix(ann,ncol = 1)

    fit.GPA.wAnn <- GPA( pmat, ann , maxIter = 100 )
    cov.GPA.wAnn <- cov( fit.GPA.wAnn )
    assoc.GPA.wAnn <- assoc( fit.GPA.wAnn, FDR=0.05, fdrControl="global" )

[Package GPA version 1.4.0 Index]