Compute per-annotation standard deviation, MAF-restricted

Computes the standard deviation of each annotation column in the target annotation files, restricted to SNPs above a MAF cutoff via PLINK `.frq` files. Required for internal consistency with polyfun's regression, which operates on MAF > cutoff SNPs by default.

Usage

computeSldscAnnotSd(sldscData, mafCutoff = 0.05, annotCols = NULL)

Arguments

sldscData

An SldscData object (its annot and frq slots supply the annotation values and MAF, respectively).

mafCutoff

Numeric, default `0.05`. Requires frq data when > 0.

annotCols

Character or integer vector, default NULL. Annotation columns to compute sd for. If NULL, all annotation columns are used.

Value

Named numeric vector of \(sd_C\) values, one per annotation.

Examples

mkRun <- function(cats) {
  n <- length(cats)
  list(categories = cats, tau = setNames(rep(1e-7, n), cats),
    tauSe = setNames(rep(3e-8, n), cats),
    enrichment = setNames(rep(2, n), cats),
    enrichmentSe = setNames(rep(0.4, n), cats),
    enrichmentP = setNames(rep(0.01, n), cats),
    propH2 = setNames(rep(0.2, n), cats),
    propSnps = setNames(rep(0.1, n), cats), h2g = 0.3,
    tauBlocks = matrix(1e-7, 10, n, dimnames = list(NULL, cats)),
    nBlocks = 10L)
}
annot <- data.frame(CHR = c(1, 1, 1, 2, 2, 2), SNP = paste0("rs", 1:6),
  annot_A = c(1, 0, 1, 0, 1, 0), annot_B = c(2.1, 1.8, 2.5, 1.9, 2.3, 2))
frq <- data.frame(CHR = c(1, 1, 1, 2, 2, 2), SNP = paste0("rs", 1:6),
  MAF = rep(0.2, 6))
mkTrait <- function() {
  list(single = list(mkRun(c("annot_A_0", "baselineLD_0")),
    mkRun(c("annot_B_0", "baselineLD_0"))),
    joint = mkRun(c("annot_A_0", "annot_B_0", "baselineLD_0")))
}
traits <- setNames(list(mkTrait(), mkTrait()), c("traitX", "traitY"))
sd <- SldscData(annot = annot, frq = frq, traits = traits)
computeSldscAnnotSd(sldscData = sd)
#>   annot_A   annot_B 
#> 0.5773503 0.2886751