Corrected coverage estimate using estimated effect sizes and their standard errors

Corrected coverage estimate using estimated effect sizes and their standard errors

corrcov_bhat(bhat, V, N0, N1, Sigma, thr, W = 0.2, nrep = 1000,
  pp0min = 0.001)

Arguments

bhat	Estimated effect sizes from single-SNP logistic regressions
V	Variance of estimated effect sizes
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 <- 1000 # number of controls
N1 <- 1000 # number of cases

## 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)

varbeta <- Var.data.cc(f = maf, N = N0 + N1, s = N1/(N0+N1))

bhats = rnorm(nsnps, 0, 0.2) # log OR

corrcov_bhat(bhat = bhats, V = varbeta, N0, N1, Sigma = LD, thr = 0.95)
#> [1] 0.997