Corrected coverage estimate using Z-scores and MAFs

Corrected coverage estimate using Z-scores and mafs

corrcov(z, f, N0, N1, Sigma, thr, W = 0.2, nrep = 1000,
  pp0min = 0.001)

Arguments

z

Marginal Z-scores
f

Minor allele frequencies
N0

Number of controls
N1

Number of cases
Sigma

SNP correlation matrix
thr

Minimum threshold for fine-mapping experiment
W

Prior for the standard deviation of the effect size parameter, beta (default 0.2)
nrep

The number of simulated posterior probability systems to consider for the corrected coverage estimate (default 1000)
pp0min

Only average over SNPs with pp0 > pp0min

Value

Corrected coverage estimate

Details

This function only requires the marginal summary statistics from GWAS

Examples


set.seed(1)
nsnps = 100
N0 = 5000
N1 = 5000
z_scores <- rnorm(nsnps, 0, 3) # simulate a vector of Z-scores

## generate example LD matrix
library(mvtnorm)
nsamples = 1000

simx <- function(nsnps, nsamples, S, maf=0.1) {
    mu <- rep(0,nsnps)
    rawvars <- rmvnorm(n=nsamples, mean=mu, sigma=S)
    pvars <- pnorm(rawvars)
    x <- qbinom(1-pvars, 1, maf)
}

S <- (1 - (abs(outer(1:nsnps,1:nsnps,`-`))/nsnps))^4
X <- simx(nsnps,nsamples,S)
LD <- cor2(X)
maf <- colMeans(X)

corrcov(z = z_scores, f = maf, N0, N1, Sigma = LD, thr = 0.95)
#> [1] 0.996061