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Calculate founder genome equivalents

Source: R/calcFG.R
calcFG.Rd

Part of the Genetic Value Analysis

Usage

calcFG(ped, alleles)

Arguments

ped

the pedigree information in datatable format. Pedigree (req. fields: id, sire, dam, gen, population). The pedigree must have no partial parentage (every animal has both parents known or both unknown); calcFG stops with an error otherwise.

alleles

dataframe contains an AlleleTable. This is a table of allele information produced by geneDrop().

Value

The founder genome equivalents, FG = 1 / sum( (p ^ 2) / r) where p is the vector of founder mean contributions to the current descendants and r is the mean number of founder alleles retained in the gene dropping experiment.

Returns NA with a warning when a contributing founder (p > 0) is retained in zero of the gene-drop iterations (r == 0): that term is p^2 / 0 = Inf, which would otherwise collapse FG silently to 0. Raise the number of iterations. See calcFGSE for the sampling standard error of FG.

References

Lacy RC. 1989. Analysis of founder representation in pedigrees: founder equivalents and founder genome equivalents. Zoo Biol 8:111-123.

See also

Other genetic value analysis: calcA(), calcFE(), calcFEFG(), calcFGSE(), calcGU(), calcGUSE(), calcGeneDiversity(), calcNeSexRatio(), calcNeVariance(), calcRetention()

Examples

## Example from Analysis of Founder Representation in Pedigrees: Founder
## Equivalents and Founder Genome Equivalents.
## Zoo Biology 8:111-123, (1989) by Robert C. Lacy

library(nprcgenekeepr)
ped <- data.frame(
  id = c("A", "B", "C", "D", "E", "F", "G"),
  sire = c(NA, NA, "A", "A", NA, "D", "D"),
  dam = c(NA, NA, "B", "B", NA, "E", "E"),
  stringsAsFactors = FALSE
)
ped["gen"] <- findGeneration(ped$id, ped$sire, ped$dam)
ped$population <- getGVPopulation(ped, NULL)
pedFactors <- data.frame(
  id = c("A", "B", "C", "D", "E", "F", "G"),
  sire = c(NA, NA, "A", "A", NA, "D", "D"),
  dam = c(NA, NA, "B", "B", NA, "E", "E"),
  stringsAsFactors = TRUE
)
pedFactors["gen"] <- findGeneration(
  pedFactors$id, pedFactors$sire,
  pedFactors$dam
)
pedFactors$population <- getGVPopulation(pedFactors, NULL)
alleles <- geneDrop(ped$id, ped$sire, ped$dam, ped$gen,
  genotype = NULL,
  n = 1000, updateProgress = NULL
)
allelesFactors <- geneDrop(pedFactors$id, pedFactors$sire, pedFactors$dam,
  pedFactors$gen,
  genotype = NULL, n = 1000,
  updateProgress = NULL
)
fg <- calcFG(ped, alleles)
fgFactors <- calcFG(pedFactors, allelesFactors)