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sLDSC Postprocessing Pipeline

Source: R/sldscPostprocessingPipeline.R
sldscPostprocessingPipeline.Rd

Postprocess polyfun's per-trait sLDSC outputs (already loaded into an SldscData object) into a single results object with per-trait tau*, EnrichStat with back-solved jackknife SE, and a DerSimonian-Laird random-effects meta-analysis across traits. All file I/O is done up front by the reader functions (readSldscAnnot, readSldscFrq, readSldscTrait); this pipeline is pure computation over the in-memory SldscData.

Usage

sldscPostprocessingPipeline(
  sldscData,
  mafCutoff = 0.05,
  targetCategories = NULL,
  targetLabels = NULL
)

Arguments

sldscData

An SldscData object bundling the annotation table, the reference-panel allele frequencies, and the per-trait single/joint polyfun runs.

mafCutoff

Numeric MAF cutoff applied via the object's frq table. Default 0.05. Set to 0 to opt out (requires frq data when > 0).

targetCategories

Optional character vector of target annotation names to retain. Auto-detected from the joint run (or first single run) when NULL.

targetLabels

Optional display names, same length / order as targetCategories, applied to every output column / tau* block colname.

Value

A list with per_trait (per-trait standardised tables), meta tables (tauStar, enrichment, enrichstat), and a params record of the call options.

See also

SldscData, readSldscAnnot, readSldscFrq, readSldscTrait

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)
sldscPostprocessingPipeline(sldscData = sd)
}