GPA-package {GPA} | R Documentation

## GPA (Genetic analysis incorporating Pleiotropy and Annotation)

### Description

This package provides functions for fitting GPA, a statistical approach to prioritizing GWAS results by integrating pleiotropy information and annotation data, along with ShinyGPA, a visualization toolkit to investigate the pleiotropic architecture using GWAS results.

### Details

Package: | GPA
Type: | Package
Version: | 0.99.9
Date: | 2020-04-14
License: | GPL (>= 2)
LazyLoad: | yes

This package contains a main class, `GPA`, which represents GPA model fit.

This package contains four main methods for the GPA framework (Chung et al., 2014), `GPA`, `assoc`, `pTest`, and `aTest`. `GPA` method fits the GPA model and `assoc` method implements association mapping. `pTest` and `aTest` methods implement hypothesis testing for pleiotropy and annotation enrichment, respectively.

This package contains two main methods for the ShinyGPA visualization toolkit (Kortemeier et al., 2017), `fitAll` and `shinyGPA`. `fitAll` function generates all the intermediate results needed to run `shinyGPA`. `shinyGPA` opens the ShinyGPA interface, which takes the results generated from `fitAll` as input.

### Author(s)

Dongjun Chung, Emma Kortemeier

Maintainer: Dongjun Chung <dongjun.chung@gmail.com>

### 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)

Kortemeier E, Ramos PS, Hunt KJ, Kim HJ, Hardiman G, and Chung D (2018), "ShinyGPA: An interactive and dynamic visualization toolkit for genetic studies," PLOS One, 13(1): e0190949.

### See Also

`GPA`, `assoc`, `pTest`, `aTest`, `GPA`, `fitAll`, `shinyGPA`.

### Examples

    ## Not run:
    # simulation setting

    nBin <- 1000
    pi1 <- 0.2
    common <- 0.5
    betaAlpha <- c( 0.6, 0.6 )
    annP <- c( 0.2, 0.4, 0.4, 0.4 )
    seed <- 12345

    # simulation setting

    nCommon <- round( pi1 * common * nBin )
    nUniq <- round( pi1 * ( 1 - common ) * nBin )
    nBg <- nBin - 2 * nUniq - nCommon

    # M * K matrix of GWAS p-value

    set.seed( seed )

    pvec1 <- c( rbeta( nCommon, betaAlpha[1], 1 ), rbeta( nUniq, betaAlpha[1], 1 ),
    	runif( nUniq ), runif( nBg ) )
    pvec2 <- c( rbeta( nCommon, betaAlpha[2], 1 ), runif( nUniq ),
    	rbeta( nUniq, betaAlpha[2], 1 ), runif( nBg ) )
    pmat <- cbind( pvec1, pvec2 )

    # M * D matrix of annotation

    ann <- c(
    	sample( c(1,0), nCommon, replace=TRUE, prob = c( annP[4], 1 - annP[4] ) ),
    	sample( c(1,0), nUniq, replace=TRUE, prob = c( annP[2], 1 - annP[2] ) ),
    	sample( c(1,0), nUniq, replace=TRUE, prob = c( annP[3], 1 - annP[3] ) ),
    	sample( c(1,0), nBg, replace=TRUE, prob = c( annP[1], 1 - annP[1] ) ) )

    # GPA without annotation data

    fit.GPA.noAnn <- GPA( pmat, NULL )
    cov.GPA.noAnn <- cov( fit.GPA.noAnn )

    # GPA with annotation data

    fit.GPA.wAnn <- GPA( pmat, ann )
    cov.GPA.wAnn <- cov( fit.GPA.wAnn )

    # GPA under pleiotropy H0

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

    # association mapping

    assoc.GPA.wAnn <- assoc( fit.GPA.wAnn, FDR=0.05, fdrControl="global" )

    # hypothesis testing for pleiotropy

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

    # hypothesis testing for annotation enrichment

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

    # 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 <- 0.4
    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 )

    # Fit GPA for all possible pairs of GWAS datasets

    out <- fitAll( pmat )

    # Run the ShinyGPA app using the ouput from fitAll()

    shinyGPA(out)

    ## End(Not run)

[Package GPA version 1.4.0 Index]