ppfunc: Find PPs of SNPs from Z-scores

In corrcoverage: Correcting the Coverage of Credible Sets from Bayesian Genetic Fine Mapping

Description

Posterior probabilities of causality from marginal Z-scores

Usage

ppfunc(z, V, W = 0.2)

Arguments

z	Vector of marginal Z-scores
V	Variance of the estimated effect size (can be obtained using Var.beta.cc function)
W	Prior for the standard deviation of the effect size parameter, beta (W = 0.2 default)

Details

This function converts Z-scores to posterior probabilities of causality i.e. not including the null model of no genetic effects, so that the sum of the posterior probabilities for all variants is 1

Value

Vector of posterior probabilities

Examples

set.seed(1)
nsnps = 100
N0 = 5000
N1 = 5000
z_scores <- rnorm(nsnps, 0, 3)

## generate example LD matrix and MAFs
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)
maf <- colMeans(X)

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

res <- ppfunc(z = z_scores, V = varbeta)
sum(res)
res

corrcoverage documentation built on Dec. 7, 2019, 1:07 a.m.