Define a trait with QTL and sampled effects in one call

Convenience wrapper that chains define_trait(), define_additive_effects(), and define_phenotype() for a single uncorrelated trait using random QTL placement. For correlated multi-trait simulations or custom QTL placement, use the functions individually with get_table("genome_meta") |> filter(...) |> define_additive_effects().

Usage

define_trait_simple(
  pop,
  trait_name,
  n_qtl,
  target_add_var,
  mean = 0,
  residual_var,
  type = "continuous",
  expressed_sex = "both",
  repeatable = FALSE,
  effect_distribution = "normal",
  scale_to_target = TRUE,
  seed = NULL,
  ...
)

Arguments

pop

A tidybreed_pop object.

trait_name

Character. Trait name; must be a valid SQL identifier.

n_qtl

Integer. Number of QTL to randomly select from genome_meta.

target_add_var

Numeric. Additive genetic variance target. Passed to define_trait().

mean

Numeric. Phenotypic population mean. Passed to define_phenotype() as the mean argument. Default 0.

residual_var

Numeric. Residual variance. Passed to define_phenotype().

type

Character. One of "continuous" (default), "count", "categorical". Passed to define_phenotype().

expressed_sex

Character. "both" (default), "M", or "F". Passed to define_phenotype().

repeatable

Logical. Whether the trait allows repeated records. Passed to define_phenotype(). Default FALSE.

effect_distribution

Character. "normal" (default) or "gamma". Passed to define_additive_effects().

scale_to_target

Logical. Passed to define_additive_effects(). Default TRUE.

seed

Optional integer for reproducibility (applied before QTL draw and effect sampling).

...

Additional arguments forwarded to define_phenotype() (e.g. prevalence, thresholds, cat_values, cat_names, min_value, max_value, store_liability).

Value

The modified tidybreed_pop (invisibly).

See also

define_trait(), define_additive_effects(), define_phenotype()

Examples

if (FALSE) { # \dontrun{
pop <- pop |>
  define_trait_simple(
    trait_name     = "ADG",
    n_qtl          = 100,
    target_add_var = 100,
    mean           = 850,
    residual_var   = 120
  )
} # }