aSPU (version 1.50)

MTaSPUs: The SPU and aSPU tests for multiple traits - single SNP association with GWAS summary statistics.

Description

SNP based adaptive association test for multiple phenotypes with GWAS summary statistics.

Usage

MTaSPUs(Z, v, B, pow, transform = FALSE, Ps = FALSE)

Arguments

Z

matrix of summary Z-scores, SNPs in rows and traits in columns. Or a vector of summary Z-scores for a single snp

v

estimated correlation matrix based on the summary Z-scores (output of estcov)

B

number of Monte Carlo samples simulated to compute p-values, the maximum number of MC simulations is 1e8

pow

power used in SPU test. A vector of the powers.

transform

if TRUE, the inference is made on transformed Z

Ps

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

Value

compute p-values for SPU(gamma) i.e. pow=1:8, and infinity aSPU, based on the minimum p-values over SPU(power) each row for single SNP

References

Junghi Kim, Yun Bai and Wei Pan (2015) An Adaptive Association Test for Multiple Phenotypes with GWAS Summary Statistics, Genetic Epidemiology, 8:651-663

See Also

minP estcov

Examples

# NOT RUN {
# -- n.snp: number of SNPs
# -- n.trait: number of traits
# -- n.subject: number of subjects

n.snp <- 100
n.traits <- 10
n.subjects <- 1000
traits <- matrix(rnorm(n.subjects*n.traits), n.subjects, n.traits)
v <- cov(traits)
allZ <- rmvnorm(n.snp, Sigma=v)
colnames(allZ) <- paste("trait", 1:n.traits, sep="")
rownames(allZ) <- paste("snp", 1:n.snp, sep="")


r <- estcov(allZ)
MTaSPUs(Z = allZ, v = r, B = 100, pow = c(1:4, Inf), transform = FALSE)
MTaSPUs(Z = allZ[1,], v = r, B = 100, pow = c(1:4, Inf), transform = FALSE)
minP(Zi= allZ[1,], r = r)

# }