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Random-effects meta-analysis over a subset of sLDSC traits

Source: R/sldscWrapper.R
sldscSubsetMeta.Rd

Re-run the random-effects meta-analysis (DerSimonian-Laird, via metafor::rma) on a chosen subset of the per-trait standardised tables produced by sldscPostprocessingPipeline – no regression is re-run, only the already-standardised per-trait estimates are re-meta'd. Powers a "meta on a subset of traits" workflow: pick traits, pick target annotation categories, and get the per-category tau* / enrichment / enrichstat meta results back.

Usage

sldscSubsetMeta(postprocessResult, subsetTraits, targetCategories = NULL)

Arguments

postprocessResult

The list returned by sldscPostprocessingPipeline. Must carry a $per_trait element; $params$target_categories is used when targetCategories is NULL.

subsetTraits

Character vector of trait ids to meta over; every id must be present in postprocessResult$per_trait.

targetCategories

Optional character vector of target annotation names. Defaults to postprocessResult$params$target_categories.

Value

A list with tau_star_single, tau_star_joint, enrichment, and enrichstat; each is a per-category named list of metaSldscRandom results.

See also

sldscPostprocessingPipeline, metaSldscRandom

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...
sldscSubsetMeta(pp, subsetTraits = "traitX")
#> $tau_star_single
#> $tau_star_single$annot_A_0
#> $tau_star_single$annot_A_0$mean
#> [1] NA
#> 
#> $tau_star_single$annot_A_0$se
#> [1] NA
#> 
#> $tau_star_single$annot_A_0$p
#> [1] NA
#> 
#> $tau_star_single$annot_A_0$nTraits
#> [1] 0
#> 
#> $tau_star_single$annot_A_0$traitsUsed
#> character(0)
#> 
#> $tau_star_single$annot_A_0$tau2
#> [1] NA
#> 
#> 
#> $tau_star_single$annot_B_0
#> $tau_star_single$annot_B_0$mean
#> [1] NA
#> 
#> $tau_star_single$annot_B_0$se
#> [1] NA
#> 
#> $tau_star_single$annot_B_0$p
#> [1] NA
#> 
#> $tau_star_single$annot_B_0$nTraits
#> [1] 0
#> 
#> $tau_star_single$annot_B_0$traitsUsed
#> character(0)
#> 
#> $tau_star_single$annot_B_0$tau2
#> [1] NA
#> 
#> 
#> 
#> $tau_star_joint
#> $tau_star_joint$annot_A_0
#> $tau_star_joint$annot_A_0$mean
#> [1] NA
#> 
#> $tau_star_joint$annot_A_0$se
#> [1] NA
#> 
#> $tau_star_joint$annot_A_0$p
#> [1] NA
#> 
#> $tau_star_joint$annot_A_0$nTraits
#> [1] 0
#> 
#> $tau_star_joint$annot_A_0$traitsUsed
#> character(0)
#> 
#> $tau_star_joint$annot_A_0$tau2
#> [1] NA
#> 
#> 
#> $tau_star_joint$annot_B_0
#> $tau_star_joint$annot_B_0$mean
#> [1] NA
#> 
#> $tau_star_joint$annot_B_0$se
#> [1] NA
#> 
#> $tau_star_joint$annot_B_0$p
#> [1] NA
#> 
#> $tau_star_joint$annot_B_0$nTraits
#> [1] 0
#> 
#> $tau_star_joint$annot_B_0$traitsUsed
#> character(0)
#> 
#> $tau_star_joint$annot_B_0$tau2
#> [1] NA
#> 
#> 
#> 
#> $enrichment
#> $enrichment$annot_A_0
#> $enrichment$annot_A_0$mean
#> [1] NA
#> 
#> $enrichment$annot_A_0$se
#> [1] NA
#> 
#> $enrichment$annot_A_0$p
#> [1] NA
#> 
#> $enrichment$annot_A_0$nTraits
#> [1] 1
#> 
#> $enrichment$annot_A_0$traitsUsed
#> [1] "traitX"
#> 
#> $enrichment$annot_A_0$tau2
#> [1] NA
#> 
#> 
#> $enrichment$annot_B_0
#> $enrichment$annot_B_0$mean
#> [1] NA
#> 
#> $enrichment$annot_B_0$se
#> [1] NA
#> 
#> $enrichment$annot_B_0$p
#> [1] NA
#> 
#> $enrichment$annot_B_0$nTraits
#> [1] 1
#> 
#> $enrichment$annot_B_0$traitsUsed
#> [1] "traitX"
#> 
#> $enrichment$annot_B_0$tau2
#> [1] NA
#> 
#> 
#> 
#> $enrichstat
#> $enrichstat$annot_A_0
#> $enrichstat$annot_A_0$mean
#> [1] NA
#> 
#> $enrichstat$annot_A_0$se
#> [1] NA
#> 
#> $enrichstat$annot_A_0$p
#> [1] NA
#> 
#> $enrichstat$annot_A_0$nTraits
#> [1] 1
#> 
#> $enrichstat$annot_A_0$traitsUsed
#> [1] "traitX"
#> 
#> $enrichstat$annot_A_0$tau2
#> [1] NA
#> 
#> 
#> $enrichstat$annot_B_0
#> $enrichstat$annot_B_0$mean
#> [1] NA
#> 
#> $enrichstat$annot_B_0$se
#> [1] NA
#> 
#> $enrichstat$annot_B_0$p
#> [1] NA
#> 
#> $enrichstat$annot_B_0$nTraits
#> [1] 1
#> 
#> $enrichstat$annot_B_0$traitsUsed
#> [1] "traitX"
#> 
#> $enrichstat$annot_B_0$tau2
#> [1] NA
#> 
#> 
#>