Random-effects meta-analysis of S-LDSC quantities across traits

Random-effects meta-analysis (DerSimonian-Laird, via metafor::rma) of one S-LDSC quantity for one annotation across multiple traits.

Usage

metaSldscRandom(
  perTraitEstimates,
  category,
  quantity = c("tauStar", "enrichment", "enrichstat")
)

Arguments

perTraitEstimates: Named list of per-trait results (each with a `summary` data frame).
category: Character. Annotation name to meta-analyze.
quantity: Character: `"tauStar"`, `"enrichment"`, or `"enrichstat"`.

Value

List with `mean`, `se`, `p`, `nTraits`, `traitsUsed`, `tau2`.

Details

Per-trait \(SE_i\) sources: - `quantity = "tauStar"`: jackknife SE from per-block \(\tau^*\). - `quantity = "enrichment"`: polyfun-reported `Enrichment_std_error`. - `quantity = "enrichstat"`: back-solved SE from polyfun's `Enrichment_p`.

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)
pp <- sldscPostprocessingPipeline(sd)
#> [sldsc] Computing M_ref...
#> [sldsc]   M_ref = 6 (MAF cutoff 0.05)
#> [sldsc] Computing per-annotation sd...
#> [sldsc]   sd computed for 2 annotation columns
#> [sldsc] Detecting binary vs continuous annotations...
#> [sldsc] Auto-detected 2 target categories
#> [sldsc] Detected 1 baseline annotations: baselineLD_0
#> [sldsc] Standardizing 2 traits...
#> [sldsc] Running random-effects meta across traits...
metaSldscRandom(pp$per_trait, category = "annot_A_0",
  quantity = "enrichment")
#> $mean
#> [1] NA
#> 
#> $se
#> [1] NA
#> 
#> $p
#> [1] NA
#> 
#> $nTraits
#> [1] 0
#> 
#> $traitsUsed
#> character(0)
#> 
#> $tau2
#> [1] NA
#>