Forming Breeding Groups

Overview

nprcgenekeepr can assemble candidate animals into one or more breeding groups that keep close relatives apart while spreading genetic diversity across the colony, following the approach of Vinson and Raboin (2015). Groups can be formed three ways:

unconstrained – members are drawn subject only to a kinship ceiling;
harem – each group is seeded with a single male and filled with other animals;
target sex ratio – each group is filled toward a chosen females-per-male ratio.

This article walks the whole pipeline – from a raw studbook to formed groups – entirely from R, using the examplePedigree data set that ships with the package. The Shiny app (runModularApp()) drives the same functions through a point-and-click interface; here we script them directly.

Setup

library(nprcgenekeepr)
set_seed(1L)

Group formation makes random draws – which group to fill next, which animal to add, which males to use as harem sires – so two runs differ unless the seed is fixed. groupAddAssign() does not set a seed internally, so we call set_seed() once, up front, to make everything below reproducible. (set_seed() is a thin, version-stable wrapper around set.seed().)

From studbook to candidate list

Breeding-group formation needs two ingredients: a list of candidate animals and a kinship matrix giving the relatedness of every candidate to every other. Both are derived from a quality-controlled pedigree.

breederPed <- qcStudbook(examplePedigree,
  minParentAge  = 2.0,
  reportChanges = FALSE,
  reportErrors  = FALSE
)

qcStudbook() validates and standardizes the studbook (checking parentage, sexes, dates, and minimum parent age). Next we mark the focal population – here, the non-founders still in the colony (those with at least one known parent) – and trim the pedigree to those animals plus the ancestors needed to compute their kinships.

focalAnimals <- breederPed$id[!(is.na(breederPed$sire) &
  is.na(breederPed$dam)) &
  is.na(breederPed$exit)]
breederPed <- setPopulation(ped = breederPed, ids = focalAnimals)
trimmedPed <- trimPedigree(focalAnimals, breederPed)

length(focalAnimals)
#> [1] 327
dim(trimmedPed)
#> [1] 704  13

reportGV() runs the genetic value analysis and – the part we need here – returns the kinship matrix for the trimmed pedigree.

trimmedGeneticValue <- reportGV(trimmedPed,
  guIter   = 50, # genome-uniqueness iterations; use >= 1000 in practice
  guThresh = 3,
  byID     = TRUE,
  updateProgress = NULL
)
kmat <- trimmedGeneticValue[["kinship"]]
dim(kmat)
#> [1] 327 327

The small guIter above keeps this article quick to render. For real analyses use guIter >= 1000; the genome-uniqueness estimates stabilize only with many iterations.

Finally, choose the candidates – here, animals born before 2013 that are still in the colony.

candidates <- trimmedPed$id[trimmedPed$birth < as.Date("2013-01-01") &
  !is.na(trimmedPed$birth) &
  is.na(trimmedPed$exit)]
length(candidates)
#> [1] 280

Harem groups

With harem = TRUE, groupAddAssign() seeds each of the numGp groups with one eligible male and then fills it with other animals, never placing two animals whose kinship exceeds threshold (default 0.015625, i.e. 1/64 – the kinship of second cousins) in the same group.

haremGrp <- groupAddAssign(
  kmat = kmat,
  ped  = trimmedPed,
  candidates = candidates,
  iter  = 10, # group-search iterations; use >= 1000 in practice
  numGp = 6,
  harem = TRUE
)

The result is a list with two elements, group and score. group holds one character vector of animal IDs per group, followed by a final element collecting any candidates left unplaced (a lone NA when every candidate was assigned):

names(haremGrp)
#> [1] "group" "score"
lengths(haremGrp$group) # size of each group; the last is the unplaced pool
#> [1] 30 33 32 28 39 28  1
haremGrp$group[[1]]     # the first harem group
#>  [1] "J1R2EW" "3DTD2N" "FL170P" "465ERA" "99BMJW" "K3TNHP" "DRXMW4" "ESUIAF"
#>  [9] "1CIRC9" "S056D5" "2F6J3U" "PI4VHT" "WLMGS1" "RVHVTZ" "3SKITJ" "AIHJ8Z"
#> [17] "3GECJJ" "TXZUKC" "5W621W" "B228Q6" "MTCAIG" "FB5L3N" "13B1QL" "B1WVCN"
#> [25] "SCFSBF" "5EDLL7" "KZY6PD" "WNEAS6" "AZ3L0D" "0IIAEN"

Groups with a target sex ratio

Passing a non-zero sexRatio (females per male) fills each group toward that ratio instead of using a single harem sire. Here we aim for nine females per male.

sexRatioGrp <- groupAddAssign(
  kmat = kmat,
  ped  = trimmedPed,
  candidates = candidates,
  iter     = 10,
  numGp    = 6,
  sexRatio = 9.0
)
lengths(sexRatioGrp$group)
#> [1]  35  25  24  30  31  29 106

harem and sexRatio are alternative strategies – use one or the other. Because the sex-ratio constraint caps how many animals of each sex a group can hold, more candidates may be left in the trailing unplaced element than in the harem case; raising iter lets the search place more of them.

Key arguments

Argument	Default	Meaning
`candidates`	–	character vector of animal IDs to place
`kmat`	–	kinship matrix from `reportGV()[["kinship"]]`
`ped`	–	the (trimmed) pedigree the candidates belong to
`numGp`	`1`	number of groups to form
`threshold`	`0.015625`	maximum within-group kinship (1/64 = second cousins)
`minAge`	`1.0`	minimum age (years) to be placed
`harem`	`FALSE`	seed each group with a single male
`sexRatio`	`0.0`	target females per male (0 = unconstrained)
`iter`	`1000`	group-search iterations (more = better groups)