1d:T5ed,# Corrected coverage estimate using estimated effect sizes and their standard errors ```r 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 ## Returns Corrected coverage estimate ## Details This function only requires the marginal summary statistics from GWAS ## Examples ```r 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) ``` ## Author(s) Anna Hutchinson1e:T49a,M7.49933 0.25C3.49635 0.25 0.25 3.49593 0.25 7.50024C0.25 10.703 2.32715 13.4206 5.2081 14.3797C5.57084 14.446 5.70302 14.2222 5.70302 14.0299C5.70302 13.8576 5.69679 13.4019 5.69323 12.797C3.67661 13.235 3.25112 11.825 3.25112 11.825C2.92132 10.9874 2.44599 10.7644 2.44599 10.7644C1.78773 10.3149 2.49584 10.3238 2.49584 10.3238C3.22353 10.375 3.60629 11.0711 3.60629 11.0711C4.25298 12.1788 5.30335 11.8588 5.71638 11.6732C5.78225 11.205 5.96962 10.8854 6.17658 10.7043C4.56675 10.5209 2.87415 9.89918 2.87415 7.

corrcov_bhat function