Find PPs of SNPs from Z-scores

Posterior probabilities of causality from marginal Z-scores

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)

Value

Vector of posterior probabilities

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

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)
#> [1] 1
res
#>   [1] 1.215824e-10 2.631593e-11 4.426841e-10 1.068425e-06 3.646599e-11
#>   [6] 3.907039e-10 5.990011e-11 2.225321e-10 9.103733e-11 3.354997e-11
#>  [11] 3.654523e-07 4.079658e-11 1.142769e-10 2.433182e-02 4.793560e-09
#>  [16] 2.268153e-11 2.162347e-11 9.711446e-10 3.718626e-10 9.641228e-11
#>  [21] 7.894062e-10 2.884657e-10 2.116380e-11 4.568940e-04 1.138471e-10
#>  [26] 2.161833e-11 2.337438e-11 2.173040e-07 5.462879e-11 4.208674e-11
#>  [31] 5.567277e-08 2.085934e-11 3.839569e-11 2.046751e-11 6.941122e-08
#>  [36] 4.350603e-11 3.965515e-11 2.044962e-11 3.680986e-09 2.453875e-10
#>  [41] 2.374919e-11 2.690810e-11 1.700450e-10 7.664161e-11 1.561141e-10
#>  [46] 1.788622e-10 3.732358e-11 2.605765e-10 2.215170e-11 5.796648e-10
#>  [51] 4.163009e-11 1.037319e-10 3.518230e-11 4.952190e-09 1.361513e-07
#>  [56] 3.980564e-04 3.749134e-11 2.235304e-09 8.404665e-11 2.296256e-11
#>  [61] 9.625313e-01 2.183471e-11 1.618909e-10 2.176455e-11 2.232722e-10
#>  [66] 2.380888e-11 2.351687e-05 2.035492e-07 2.357808e-11 1.225476e-02
#>  [71] 5.726625e-11 1.826669e-10 1.054809e-10 8.917293e-10 1.758062e-08
#>  [76] 3.064738e-11 4.951979e-11 2.107186e-11 2.202085e-11 9.365495e-11
#>  [81] 8.599292e-11 2.418761e-11 8.041102e-09 4.251524e-07 9.610121e-11
#>  [86] 3.450227e-11 2.650920e-09 3.230306e-11 3.950781e-11 3.002871e-11
#>  [91] 7.542926e-11 1.089141e-08 6.683445e-09 1.774247e-10 9.769621e-07
#>  [96] 8.280724e-11 2.242496e-08 8.825774e-11 1.297898e-08 5.891902e-11