assoc: Association mapping

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

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" )

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