aSPU (version 1.50)

aSPUsD: Sum of Powered Score (SPUs) tests and adaptive SPU (aSPUs) test for single trait - SNP set association with GWAS summary statistics (distribution based).

Description

It gives p-values of the SPUs tests and aSPUs test with GWAS summary statistics.

Usage

aSPUsD(Zs, corrSNP, Ps = FALSE)

Arguments

Zs

Z-scores for each SNPs. It could be P-values if the Ps option is TRUE.

corrSNP

Correlation matirx of the SNPs to be tested; estimated from a reference panel (based on the same set of the reference alleles as used in calculating Z-scores).

Ps

TRUE if input is p-value, FALSE if input is Z-scores. The default is FALSE.

Value

pvs : p-values for the SPUsD and aSPUsD tests.

References

Gongjun Xu, Lifeng Lin, Peng Wei and Wei Pan (2016) An adaptive two-sample test for high-dimensional means, Biometrika (2016) 103 (3): 609-624. Il-Youp Kwak, Wei Pan (2015) Adaptive Gene- and Pathway-Trait Association Testing with GWAS Summary Statistics, Bioinformatics, 32(8), 1178-1184

See Also

aSPUs aSPU

Examples

# NOT RUN {
data(kegg9)
## example analysis using aSPUM test.
g <- kegg9$gene.info[1,1]  #  SOAT1 
## Take snps mapped on gene "SOAT1" from the information of gene.info and snp.info. 
snps <- which( ( kegg9$snp.info[,2] == kegg9$gene.info[kegg9$gene.info[,1] == g, 2] ) &
                 (kegg9$snp.info[,3] > kegg9$gene.info[kegg9$gene.info[,1] == g, 3] ) &
                 (kegg9$snp.info[,3] < kegg9$gene.info[kegg9$gene.info[,1] == g, 4] )  )
## Take subsets
newP <- kegg9$nP[snps] ;
ldsub <- kegg9$ldmatrix[snps, snps];
## Get p-value for gene SOAT1. Read vignette for details.
out <- aSPUsD(newP, corrSNP=ldsub, Ps=TRUE)

out

# }