pvals_pp {corrcoverage}

Find PPs for SNPs and null model from P-values and MAFs

Description

Posterior probabilities of causality from P-values

Usage

pvals_pp(pvals, f, type, N, s, W = 0.2, p1 = 1e-04)

Arguments

pvals

P-values of SNPs

f

Minor allele frequencies

type

Type of experiment ('quant' or 'cc')

N

Total sample size

s

Proportion of cases (N1/N0+N1), ignored if type=='quant'

W

Prior for the standard deviation of the effect size parameter, beta (default 0.2)

p1

Prior probability a SNP is associated with the trait (default 1e-4)

Details

This function converts p-values to posterior probabilities of causality, including the null model of no genetic effect

Value

Posterior probabilities of null model (no genetic effect) and causality for each SNP

Author(s)

Anna Hutchinson

Examples

set.seed(1)
nsnps = 100
N0 = 5000
N1 = 5000
z_scores <- rnorm(nsnps, 0, 3)
p_values <- 2 * pnorm( - abs ( z_scores ) )

## 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)

res <- pvals_pp(pvals = p_values, f = maf, type = "cc", N = N0+N1, s = N1/(N0+N1))
sum(res)
res