Reference-panel SNP count (the M_ref used to standardise tau*)

`M_ref` is the number of SNPs in the REFERENCE PANEL over which heritability is partitioned in the sLDSC model (`h2(C) = sum_(j in M_ref) a_C(j) sum_(C') tau_(C') a_(C')(j)`). It is panel-defined and is **not** the regression SNP set (HapMap3 ~1M) nor any HM3-subsetted target output:

`mafCutoff > 0` (Gazal/Finucane convention): count MAF > cutoff SNPs across all `.frq` files (the same set polyfun's `.l2.M_5_50` sums).
`mafCutoff == 0` (all-M variant): count ALL SNPs across all `.frq` files (the same set polyfun's `.l2.M` sums).

When no frq data is present, `mafCutoff == 0` falls back to the number of annotation rows; `mafCutoff > 0` errors (a MAF-restricted count needs frq).

Usage

computeSldscMRef(sldscData, mafCutoff = 0.05)

Arguments

sldscData: An SldscData object (its frq slot is the reference-panel SNP set).
mafCutoff: Numeric, default `0.05`.

Value

Scalar integer.

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)
computeSldscMRef(sldscData = sd)
#> [1] 6