pTest {GPA} | R Documentation

## Hypothesis testing for pleiotropy

### Description

Hypothesis testing for pleiotropy.

### Usage

    pTest( fit, fitH0, vDigit=1000 )

### Arguments

`fit` | Fit of the GPA model of interest.
`fitH0` | GPA model fit under the null hypothesis of pleiotropy test.
`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

`pTest` implements the hypothesis testing for pleiotropy. It requires two GPA model fits, one of interest and one under the null hypothesis (obtained by setting `pleiotropyH0=TRUE` when running `GPA` function), and evaluates genetical correlation among multiple phenotypes using the likelihood ratio test.

### Value

Returns a list with components:

`pi` | pi estimates.
`piSE` | Standard errors for pi estimates.
`statistics` | Statistics of the pleiotropy test.
`pvalue` | p-value of the pleiotropy test.

### 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

`aTest`, `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 )

    # GPA without annotation data

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

    # GPA under the null hypothesis of pleiotropy test

    fit.GPA.pleiotropy.H0 <- GPA( pmat, NULL, pleiotropyH0=TRUE, maxIter = 100 )

    # hypothesis testing for pleiotropy

    test.pleiotropy <- pTest( fit.GPA.noAnn, fit.GPA.pleiotropy.H0 )

[Package GPA version 1.2.0 Index]