Corrected coverage estimate using Z-scores and MAFs (fixing nvar)

Obtain corrected coverage estimate using Z-scores and mafs (limiting simulations used for estimation to those with correct nvar)

corrcov_nvar(z, f, N0, N1, Sigma, nvar, thr, W = 0.2, nrep = 10000,
  pp0min = 0.001)

Arguments

z	Marginal Z-scores
f	Minor allele frequencies
N0	Number of controls
N1	Number of cases
Sigma	SNP correlation matrix
nvar	The number of variants that simulated credible sets used for estimation should contain
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 (nrep = 10000 default due to trimming)
pp0min	Only average over SNPs with pp0 > pp0min

Value

Corrected coverage estimate

Details

This function requires the marginal summary statistics from GWAS and an nvar value. It should only be used when nvar is very low (<3) and there is some evidence to suggest that only simulated credible sets with this nvar value should be used to derive the corrected coverage estimate.

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_nvar(z = z_scores, f = maf, N0, N1, Sigma = LD, thr = 0.95, nvar = 1, nrep = 100)
#> [1] 1

# note that nrep should be at least the default value (nrep = 10000) but is
# lower here for speed of computation