Standardize tau and compute EnrichStat for one polyfun run

Applies the Gazal standardization \(\tau^*_C = \tau_C \cdot sd_C \cdot M_{ref} / h^2_g\) to the point and to each jackknife block. For `mode = "single"`, additionally computes EnrichStat and back-solves its standard error from polyfun's reported `Enrichment_p` using \(|Z| = \Phi^{-1}(1 - p/2)\).

Usage

standardizeSldscTrait(
  sldscData,
  trait,
  mode = c("single", "joint"),
  idx = NULL,
  sdAnnot,
  MRef,
  targetCategories = NULL
)

Arguments

sldscData

An SldscData object (the run is pulled from it via getTraitRun).

trait

Character. Trait name (a key of the SldscData traits list).

mode

Character: `"single"` or `"joint"`.

idx

Integer or NULL. For `mode = "single"`, which of the trait's single-target runs to standardize.

sdAnnot

Named numeric vector from computeSldscAnnotSd.

MRef

Scalar from computeSldscMRef.

targetCategories

Character vector or NULL. If NULL, intersects the run's `categories` with `names(sdAnnot)`.

Value

A list with `summary` (data frame), `tau_star_blocks` (matrix), `h2g`, `nBlocks`, `mode`.

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")))
}
sd <- SldscData(annot = annot, frq = frq,
  traits = setNames(list(mkTrait(), mkTrait()), c("traitX", "traitY")))
sdAnnot <- computeSldscAnnotSd(sd)
MRef <- computeSldscMRef(sd)
standardizeSldscTrait(sd, "traitX", mode = "single", idx = 1,
  sdAnnot = sdAnnot, MRef = MRef, targetCategories = "annot_A_0")
#> Warning: standardizeSldscTrait: zero/NA sd for some targets; tau* will be NA/0.
#> $summary
#> # A tibble: 1 × 10
#>   target         tau tauSe tauStar tauStarSe enrichment enrichmentSe enrichmentP
#>   <chr>        <dbl> <dbl>   <dbl>     <dbl>      <dbl>        <dbl>       <dbl>
#> 1 annot_A_0     1e-7  3e-8      NA        NA          2          0.4        0.01
#> # ℹ 2 more variables: enrichstat <dbl>, enrichstatSe <dbl>
#> 
#> $tau_star_blocks
#>       annot_A_0
#>  [1,]        NA
#>  [2,]        NA
#>  [3,]        NA
#>  [4,]        NA
#>  [5,]        NA
#>  [6,]        NA
#>  [7,]        NA
#>  [8,]        NA
#>  [9,]        NA
#> [10,]        NA
#> 
#> $h2g
#> [1] 0.3
#> 
#> $nBlocks
#> [1] 10
#> 
#> $mode
#> [1] "single"
#>