w_reproductive_value {purgeR}

Reproductive value

Description

Computes the reproductive value following the method by Hunter et al. (2019). This is a measure of how well a gene originated in a set of 'reference' individuals is represented in a different set of 'target' individuals. By default, fitness is computed for individuals in the reference population, using all of their descendants as target. A generation-wise mode can also be enabled, to compute fitness contributions consecutively from one generation to the next.

Usage

w_reproductive_value(
  ped,
  reference,
  name_to,
  target = NULL,
  enable_correction = TRUE,
  generation_wise = FALSE
)

Arguments

ped

A dataframe containing the pedigree. Individual (id), maternal (dam), and paternal (sire) identities are mandatory columns.

reference

A string naming a column indicating whether individuals belong to the reference population or not. Column must be boolean or coercible to boolean type.

name_to

A string naming the new output column.

target

A string naming a column indicating whether individuals belong to the target population or not. Column must be boolean or coercible to boolean type. By default, all descendants of the reference population are used.

enable_correction

Correct reproductive values (enabled by default).

generation_wise

Assume that the reference population is a vector of integers indicating generation numbers. Reproductive values will be computed generation by generation independently (except for the last one).

Details

A reference population must be defined, which represents a set of individuals whose reproductive value is to be calculated. By default, genetic contributions to the rest of individuals in the pedigree is assumed, but a target population can also be defined, restricting the set of individuals accounted when computing the reproductive value. This could represent for example a cohort of alive individuals.

Value

The input dataframe, plus an additional column with reproductive values for the reference and target populations assumed.

References

• Hunter DC et al. 2019. Pedigree-based estimation of reproductive value. Journal of Heredity 10(4): 433-444.

Examples

library(dplyr)
library(magrittr)
# Pedigree used in Hunter et al. (2019)
id <- c("A1", "A2", "A3", "A4", "A5", "A6",
        "B1", "B2", "B3", "B4",
        "C1", "C2", "C3", "C4")
dam <- c("0", "0", "0", "0", "0", "0",
         "A2", "A2", "A2", "A4",
         "B2", "B2", "A4", "A6")
sire <- c("0", "0", "0", "0", "0", "0",
          "A1", "A1", "A1", "A5",
          "B1", "B3", "B3", "A5")
t <- c(0, 0, 0, 0, 0, 0,
       1, 1, 1, 1,
       2, 2, 2, 2)
ped <- tibble::tibble(id, dam, sire, t)
ped <- purgeR::ped_rename(ped, keep_names = TRUE) %>%
 dplyr::mutate(reference = ifelse(t == 1, TRUE, FALSE))
purgeR::w_reproductive_value(ped, reference = "reference", name_to = "R")