corrcov_bhat {corrcoverage}

Corrected coverage estimate using estimated effect sizes and their standard errors

Description

Corrected coverage estimate using estimated effect sizes and their standard errors

Usage

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

Details

This function only requires the marginal summary statistics from GWAS

Value

Corrected coverage estimate

Author(s)

Anna Hutchinson

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)