Corrected coverage estimate of the causal variant in the credible set

Corrected coverage estimate of the causal variant in the credible set

corrected_cov(pp0, mu, V, Sigma, thr, W = 0.2, nrep = 1000,
  pp0min = 0.001)

Arguments

pp0	Posterior probabilities of SNPs
mu	The true effect at the CV (estimate using corrcoverage::est_mu function)
V	Variance of the estimated effect size (can be obtained using coloc::Var.beta.cc function)
Sigma	SNP correlation matrix
thr	Minimum threshold for fine-mapping experiment
W	Prior for the standard deviation of the effect size parameter, beta (W=0.2 default)
nrep	Number of posterior probability systems to simulate for each variant considered causal (nrep = 1000 default)
pp0min	Only average over SNPs with pp0 > pp0min

Value

Corrected coverage estimate

Details

Requires an estimate of the true effect at the CV (e.g. use maximum absolute z-score or output from corrcoverage::est_mu function)

Examples

set.seed(1)
nsnps <- 100
N0 <- 5000
N1 <- 5000

## 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)

## generate V (variance of estimated effect sizes)
varbeta <- Var.data.cc(f = maf, N = 5000, s = 0.5)

pp <- rnorm(nsnps, 0.2, 0.05)
pp <- pp/sum(pp)

corrected_cov(pp0 = pp, mu = 4, V = varbeta, Sigma = LD, thr = 0.95, nrep = 100)
#> [1] 0.9924851