Toy example

library(susieR)
library(h2geneR)
library(matrixStats)

# a minimal example from susieR website
set.seed(1)
n    <- 1000
p    <- 500
beta <- rep(0, p)
beta[c(1, 2, 300, 400)] <- 1
X   <- matrix(rnorm(n * p), nrow = n, ncol = p)
y   <- X %*% beta + rnorm(n)
susie_fit <- susie(X, y, L = 10)

# calculate LD matrix
ld <- cov(X)

# build annotation matrix
annot <- matrix(F, nrow = p, ncol = 2)
colnames(annot) <- c("gene1", "gene2")
annot[c(1, 2), "gene1"] <- T
annot[c(300), "gene2"] <- T

# run h2gene
res <- h2gene(susie_fit, ld = ld, annot = annot)

# summarize h2gene results
print("Mean of heritability estimates across posterior samples")

## [1] "Mean of heritability estimates across posterior samples"

print(colMeans(res$hsq) / c(var(y)))

##     gene1     gene2 
## 0.4124906 0.1867927

print("Standard deviations")

## [1] "Standard deviations"

print(colSds(res$hsq) / c(var(y)))

## [1] 0.01698157 0.01135150