# Corrected coverage estimate using estimated effect sizes and their standard errors (fixing nvar) Obtain corrected coverage estimate using estimated effect sizes and their standard errors (limiting simulations used for estimation to those with correct nvar) ```r corrcov_nvar_bhat( bhat, V, N0, N1, Sigma, nvar, thr, W = 0.2, nrep = 10000, 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 - `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 ## Returns 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 ```r set.seed(1) nsnps <- 100 N0 <- 5000 # number of controls N1 <- 5000 # 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_nvar_bhat(bhat = bhats, V = varbeta, N0, N1, Sigma = LD, thr = 0.95, nvar = 1, nrep = 1000) # note that nrep should be at least the default value (nrep = 10000) but is # lower here for speed of computation ``` ## Author(s) Anna Hutchinson

corrcov_nvar_bhat function