Genetic Value Analysis

Overview

The genetic value analysis is the heart of nprcgenekeepr. It ranks the animals in a colony by how much each one contributes to the colony’s genetic diversity, so that managers can prioritize genetically valuable animals for breeding and retention (Vinson and Raboin 2015). The ranking rests on two complementary metrics:

mean kinship – the average kinship coefficient between an animal and all animals in the population (itself included). A low mean kinship is good: it means the animal is only distantly related to the rest of the colony, so its genes are under-represented and worth propagating.
genome uniqueness – the percentage of an animal’s alleles that are rare in the population (carried by no more than a few animals), estimated by a gene-drop simulation. A high genome uniqueness is good: it means the animal carries alleles that would be lost if it were not bred.

reportGV() computes both, ranks the population, and also returns colony-level diversity summaries. This article runs the whole analysis from R, using the examplePedigree data set that ships with the package. The Shiny app (runModularApp()) drives the same reportGV() through its Genetic Value Analysis tab; here we script it directly.

Setup

library(nprcgenekeepr)
set_seed(1L)

Genome uniqueness is estimated by a gene-drop simulation – founder alleles are dropped down the pedigree at random and the rare ones counted – so the genome uniqueness values (and the founder-genome-equivalent below) differ between runs unless the seed is fixed. reportGV() does not set a seed internally, so we call set_seed() once, up front, to make everything reproducible. (set_seed() is a thin, version-stable wrapper around set.seed().) Mean kinship is computed deterministically from the pedigree and does not depend on the seed.

From studbook to a population

The analysis needs a quality-controlled pedigree and a defined population of interest – the animals whose genetic value we want to rank.

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 choose the population of interest. Here it is the animals that have at least one known parent (so they are not founders) and are still in the colony (no exit date). We mark them with setPopulation() 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)  # animals in the population of interest
#> [1] 327
dim(trimmedPed)        # those animals plus the ancestors kinship needs
#> [1] 704  13

Running the analysis

gv <- reportGV(trimmedPed,
  guIter   = 50, # gene-drop iterations; use >= 1000 in practice
  guThresh = 1,  # an allele is "unique" if carried by <= this many animals
  byID     = TRUE
)
names(gv)
#>  [1] "report"          "kinship"         "gu"              "fe"             
#>  [5] "fg"              "maleFounders"    "femaleFounders"  "nMaleFounders"  
#>  [9] "nFemaleFounders" "total"

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.

reportGV() returns a list. The two elements you will use most are report (the ranked table) and kinship (the pairwise kinship matrix, which groupAddAssign() consumes when forming breeding groups – see the Forming Breeding Groups article). The rest summarize founder diversity (below).

The ranking report

report is a data frame with one row per animal in the population of interest, already sorted from most to least genetically valuable.

rpt <- gv[["report"]]
dim(rpt)
#> [1] 327  18
names(rpt)
#>  [1] "id"              "sex"             "age"             "birth"          
#>  [5] "exit"            "population"      "origin"          "sire"           
#>  [9] "dam"             "indivMeanKin"    "zScores"         "gu"             
#> [13] "guSE"            "totalOffspring"  "livingOffspring" "parentage"      
#> [17] "value"           "rank"
head(rpt[, c("id", "sex", "age", "indivMeanKin", "zScores", "gu",
  "value", "rank")], 8)
#>       id sex  age indivMeanKin    zScores gu      value rank
#> 1 1SPLS8   F  7.9  0.010663266 -0.9714998 79 High Value    1
#> 2 KZM9RB   M 30.1  0.003292539 -2.3044922 74 High Value    2
#> 3 WK89I9   F 21.1  0.012121545 -0.7077708 64 High Value    3
#> 4 CFD12A   M 20.8  0.011388376 -0.8403640 57 High Value    4
#> 5 01QRQ4   F 18.2  0.003727064 -2.2259085 54 High Value    5
#> 6 G25E3F   F  7.8  0.014794992 -0.2242792 52 High Value    6
#> 7 CLSVU6   F 23.9  0.009512525 -1.1796107 50 High Value    7
#> 8 50D77I   F 20.1  0.010806022 -0.9456824 50 High Value    8

The key columns:

Column	Meaning
`indivMeanKin`	the animal’s mean kinship (lower = less related to the colony = better)
`zScores`	mean kinship standardized across the population (mean 0, SD 1)
`gu`	genome uniqueness, as a percentage (higher = carries rarer alleles = better)
`totalOffspring` / `livingOffspring`	offspring counts
`value`	a `High Value` / `Low Value` (occasionally `Undetermined`) flag
`rank`	integer rank, 1 = most valuable

The rank and value columns come from the ranking scheme in orderReport() and rankSubjects(), which sorts the population into ordered tiers:

imported founders with no offspring;
animals with genome uniqueness above 10%, by descending genome uniqueness (ties broken by ascending mean kinship) – this is where carrying rare alleles earns a high rank;
the remaining animals whose standardized mean kinship is at or below 0.25 (i.e. no more related to the colony than about average), by ascending mean kinship – least related ranked highest;
everyone else, flagged Low Value, by ascending mean kinship.

Tiers 1–3 are flagged High Value. Because tier 2 ranks on genome uniqueness and tier 3 on mean kinship, the two metrics can disagree – and the top of the table shows it:

head(rpt[, c("id", "indivMeanKin", "gu", "rank")], 3)
#>       id indivMeanKin gu rank
#> 1 1SPLS8  0.010663266 79    1
#> 2 KZM9RB  0.003292539 74    2
#> 3 WK89I9  0.012121545 64    3

An animal need not have the very lowest mean kinship to rank first; carrying rare alleles (high genome uniqueness) can place it ahead of a slightly less related animal. That is the point of using both metrics rather than either one alone.

table(rpt$value)
#> 
#> High Value  Low Value 
#>        207        120

Colony-level diversity

Alongside the per-animal ranking, reportGV() reports two whole-colony measures of how much of the original founders’ diversity survives (Lacy 1989):

c(foundersKnown = gv$total,
  maleFounders  = gv$nMaleFounders,
  femaleFounders = gv$nFemaleFounders)
#>  foundersKnown   maleFounders femaleFounders 
#>             20              3             17
round(c(fe = gv$fe, fg = gv$fg), 2)
#>     fe     fg 
#> 109.67  47.62

Founder equivalents (fe = 1 / sum(p^2), where p is each founder’s proportional genetic contribution) is the effective number of founders – the number of equally contributing founders that would give the same diversity as the actual, unequal contributions.
Founder genome equivalents (fg) goes further, also accounting for the founder alleles lost to genetic drift over the generations (estimated from the same gene drop). Because some alleles are always lost, fg is smaller than fe.

Together they say, in effect, “the colony’s diversity is equivalent to this many ideal founders” – a smaller number signals a narrower gene pool.

Key arguments

Argument	Default	Meaning
`ped`	–	the (trimmed, QC’d) pedigree with a `population` column
`guIter`	`1000L`	gene-drop iterations (more = more stable genome uniqueness)
`guThresh`	`1L`	an allele is “unique” if carried by no more than this many animals
`pop`	`NULL`	animal IDs to treat as the population (default: the `population` column)
`byID`	`TRUE`	count an individual’s homozygous alleles once when scoring uniqueness