GPA-class: Class "GPA"

In GPA: GPA (Genetic analysis incorporating Pleiotropy and Annotation)

Description

This class represents GPA model fit.

Details

When users use fdr method, 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)).

Objects from the Class

Objects can be created by calls of the form new("GPA", ...).

Slots

fit:

Object of class "list", representing the fitted GPA model.

setting:

Object of class "list", representing the setting for GPA model fitting.

gwasPval:

Object of class "matrix", representing the p-value matrix from GWAS data.

annMat:

Object of class "matrix", representing the annotation matrix.

Methods

show

signature(object = "GPA"): provide brief summary of the object.

print

signature(x = "GPA"): provide the matrix of posterior probability that a SNP belongs to each combination of association status.

fdr

signature(object = "GPA", pattern=NULL): provide local FDR. By default (i.e., pattern=NULL), it returns a matrix of local FDR that a SNP is not associated with each phenotype (i.e., marginal FDR), where the order of columns is same as that in input GWAS data. If a pattern is specified, a vector of corresponding local FDR is provided. See the details about how users can specify the pattern.

cov

signature(object = "GPA", silent=FALSE, vDigitEst=1000, vDigitSE=1000 ): provide the covariance matrix for parameter estimates of GPA model. If silent=TRUE, it suppresses the summary output. vDigitEst and vDigitSE control 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.

estimates

signature(object = "GPA"): extract parameter estimates from GPA model fit.

se

signature(object = "GPA"): extract standard errors for parameter estimates from GPA model fit.

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

GPA, pTest, aTest.

Examples

showClass("GPA")

# 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 )
fit.GPA.wAnn
pp.GPA.wAnn <- print( fit.GPA.wAnn )
fdr.GPA.wAnn <- fdr( fit.GPA.wAnn )
fdr11.GPA.wAnn <- fdr( fit.GPA.wAnn, pattern="11" )
fdr1..GPA.wAnn <- fdr( fit.GPA.wAnn, pattern="1*" )
cov.GPA.wAnn <- cov( fit.GPA.wAnn )
est.GPA.wAnn <- estimates( fit.GPA.wAnn )
se.GPA.wAnn <- se( fit.GPA.wAnn )

GPA documentation built on Nov. 8, 2020, 6:27 p.m.