How to calculate the false discovery proportion from the p-value histogram?

Question

[Dan]

I read the textbook https://web.stanford.edu/class/bios221/book/06-chap.html about the false discovery proportion and the p-value histogram.

library("DESeq2")
library("airway")
data("airway")
aw   = DESeqDataSet(se = airway, design = ~ cell + dex)
aw   = DESeq(aw)
awde = as.data.frame(results(aw)) |> dplyr::filter(!is.na(pvalue))

alpha = binw = 0.025
pi0 = 2 * mean(awde$pvalue > 0.5)
ggplot(awde,
  aes(x = pvalue)) + geom_histogram(binwidth = binw, boundary = 0) +
  geom_hline(yintercept = pi0 * binw * nrow(awde), col = "blue") +
  geom_vline(xintercept = alpha, col = "red")

I do not understand why the false discovery proportion is calculated with

pi0 = 2 * mean(awde$pvalue > 0.5)
pi0 * binw * nrow(awde)

Thanks