aTest {GPA} | R Documentation

## Hypothesis testing for annotation enrichment

### Description

Hypothesis testing for annotation enrichment.

### Usage

    aTest( fitWithoutAnn, fitWithAnn, vDigit=1000 )

### Arguments

`fitWithoutAnn` | GPA model fit without using annotation data.
`fitWithAnn` | GPA model fit with using annotation data.
`vDigit` | Number of digits for reporting parameter estimates and standard errors. For example, setting it to 1000 means printing out values up to three digits below zero.

### Details

`aTest` implements the hypothesis testing for annotation enrichment. It requires two GPA model fits, one fitted with using annotation data and one fitted without using annotation data, and evaluates annotation enrichment for risk-associated SNPs using the likelihood ratio test.

### Value

Returns a list with components:

`q` | q estimates.
`statistics` | Statistics of the test for annotation enrichment.
`pvalue` | p-value of the test for annotation enrichment.

### 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)

### See Also

`pTest`, `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)

    # GPA without annotation data

    fit.GPA.noAnn <- GPA( pmat, NULL, maxIter = 100 )

    # GPA with annotation data

    fit.GPA.wAnn <- GPA( pmat, ann, maxIter = 100 )

    # hypothesis testing for annotation enrichment

    test.annotation <- aTest( fit.GPA.noAnn, fit.GPA.wAnn )

[Package GPA version 1.6.0 Index]