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Batch bivariate genetic/environmental correlations with FDR correction

Source: R/herit_bivar.R
herit_bivar_batch.Rd

Iterates herit_bivar() over a set of trait pairs and applies Benjamini-Hochberg FDR correction separately within each of the three p-value families (rhoP, rhoG, rhoE), matching parse_and_fdr.py's post-processing of SOLAR output.

Usage

herit_bivar_batch(
  pairs,
  grm,
  data,
  id_col = "IID",
  transform = TRUE,
  min_n = 80L,
  .progress = TRUE
)

Arguments

pairs

A two-column character matrix or data frame of trait name pairs (column 1 = trait1, column 2 = trait2), or a list of length-2 character vectors.

grm

Numeric matrix: additive genetic relationship matrix, as returned by build_grm().

data

Data frame containing id_col, trait1, and trait2.

id_col

Name of the individual ID column in data. Default "IID".

transform

Logical. Apply int_transform() to both traits before fitting. Default TRUE.

min_n

Minimum number of individuals contributing to the analysis sample required (an individual counts if at least one of trait1, trait2 is observed – see Details). Default 80L.

.progress

Logical. Show a cli progress bar. Default TRUE.

Value

A data frame with one row per successfully fitted pair and columns trait1, trait2, n, n_complete, h2_1, h2_2, rhoG, rhoE, rhoP, p_rhoG, p_rhoE, p_rhoP, q_rhoG, q_rhoE, q_rhoP (BH-adjusted). Failed / skipped pairs are silently omitted.

See also

herit_bivar()