MTaSPUs {aSPU}

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

Author(s)

Junghi Kim, Yun Bai and Wei Pan

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

# -- 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)