Batch heritability estimation over multiple traits

A convenience wrapper around herit_vc() that iterates over a vector of trait names, optionally across multiple covariate models, and returns a tidy data frame.

herit_batch(
  traits,
  grm,
  data,
  covs_list = NULL,
  id_col = "IID",
  min_n = 80L,
  ci_level = 0.95,
  .progress = TRUE
)

Arguments

traits

Character vector of trait column names in data.

grm

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

data

Data frame containing ID, trait, and covariate columns.

covs_list

A named list of covariate vectors, where each element defines one covariate model. If NULL, a single unadjusted model is run. Example: list(unadj = NULL, cov1 = c("age", "sex"), cov2 = c("age", "sex", "age2")).

id_col

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

min_n

Minimum sample size to attempt estimation. Default 80.

ci_level

Profile-likelihood CI level. Default 0.95.

.progress

Logical. Show a cli progress bar. Default TRUE.

Value

A data frame (tibble-compatible) with one row per successfully fitted model and columns: label, trait, covariates, n, h2, se, ci_lo, ci_hi, pval, sigma2_a, sigma2_e. Failed / skipped models are silently omitted.

See also

herit_vc(), build_grm(), plot_forest()

Examples

if (FALSE) { # \dontrun{
A    <- build_grm(my_pedigree, study_ids = my_data$IID)

res  <- herit_batch(
  traits     = c("bmi", "systolic_bp", "hdl"),
  grm        = A,
  data       = my_data,
  covs_list  = list(
    unadj = NULL,
    cov1  = c("age", "sex"),
    cov2  = c("age", "sex", "age2")
  )
)

# Significant adjusted models
subset(res, grepl("cov2", label) & pval < 0.05)
} # }