Package 'nprcgenekeepr'

Description

This allows kin to be used with setdiff when there are no relatives otherwise an error would occur because kin[['animal_with_no_relative']] would not be found. See the following: in groupAddAssign

Usage

addAnimalsWithNoRelative(kin, candidates)

Arguments

Details

available[[i]] <- setdiff(available[[i]], kin[[id]])

Value

The named list of high-kinship relatives (one element per animal Id, each value a character vector of that Id's high-kinship relatives) with an added element set to NA for each candidate that has no relative.

Examples

library(nprcgenekeepr)
qcPed <- nprcgenekeepr::qcPed
ped <- qcStudbook(qcPed,
  minSireAge = 2, minDamAge = 2, reportChanges = FALSE,
  reportErrors = FALSE
)
kmat <- kinship(ped$id, ped$sire, ped$dam, ped$gen, sparse = FALSE)
currentGroups <- list(1L)
currentGroups[[1]] <- examplePedigree$id[1:3]
candidates <- examplePedigree$id[examplePedigree$status == "ALIVE"]
threshold <- 0.015625
kin <- getAnimalsWithHighKinship(kmat, ped, threshold, currentGroups,
  ignore = list(c("F", "F")), minAge = 1.0
)
# Filtering out candidates related to current group members
conflicts <- unique(c(
  unlist(kin[unlist(currentGroups)]),
  unlist(currentGroups)
))
candidates <- setdiff(candidates, conflicts)
kin <- addAnimalsWithNoRelative(kin, candidates)
length(kin) # should be 591
kin[["0DAV0I"]] # should have 34 IDs

Description

Uses the ped dataframe, which has full complement of parents and the uPed dataframe, which has all uninformative parents removed to add back single parents to the uPed dataframe where one parent is known. The parents are added back to the pedigree as an ID record with NA for both sire and dam of the added back ID.

Usage

addBackSecondParents(uPed, ped)

Arguments

Value

A pedigree with single parents added. It is a data.table once any record is added and a plain dataframe when none is.

Examples

examplePedigree <- nprcgenekeepr::examplePedigree
breederPed <- qcStudbook(examplePedigree,
  minSireAge = 2, minDamAge = 2,
  reportChanges = FALSE,
  reportErrors = FALSE
)
probands <- breederPed$id[!(is.na(breederPed$sire) &
  is.na(breederPed$dam)) &
  is.na(breederPed$exit)]
ped <- getProbandPedigree(probands, breederPed)
nrow(ped)
p <- removeUninformativeFounders(ped)
nrow(p)
p <- addBackSecondParents(p, ped)
nrow(p)

Description

Assumes genotype has been opened by checkGenotypeFile

Usage

addGenotype(ped, genotype)

Arguments

Details

The two allele columns are coerced to character internally so the name-keyed allele dictionary is both built and indexed by allele label. This keeps the integer encoding consistent even when the allele columns are supplied as factors (a factor would otherwise be indexed by its integer codes). The allele columns are taken by position (columns 2 and 3 of genotype), not by name. The result is the output of a full outer merge() on id, so genotype ids that are absent from ped are added as extra rows.

Value

A plain data.frame (not a special pedigree class) with the rows of ped plus integer columns first and second holding the allele codes (numbered from 10001).

Examples

library(nprcgenekeepr)
rhesusPedigree <- nprcgenekeepr::rhesusPedigree
rhesusGenotypes <- nprcgenekeepr::rhesusGenotypes
pedWithGenotypes <- addGenotype(
  ped = rhesusPedigree,
  genotype = rhesusGenotypes
)

Description

Add ego records with NA parent IDs

Usage

addIdRecords(ids, fullPed, partialPed)

Arguments

Value

Pedigree with Ego records added having NAs for parent IDs. It is a data.table once any record is added and a plain dataframe when none is.

Examples

uPedOne <- data.frame(
  id = c("d1", "s2", "d2", "o1", "o2", "o3", "o4"),
  sire = c("s0", "s4", NA, "s1", "s1", "s2", "s2"),
  dam = c("d0", "d4", NA, "d1", "d2", "d2", "d2"),
  sex = c("F", "M", "F", "F", "F", "F", "M"),
  stringsAsFactors = FALSE
)
pedOne <- data.frame(
  id = c("s1", "d1", "s2", "d2", "o1", "o2", "o3", "o4"),
  sire = c(NA, "s0", "s4", NA, "s1", "s1", "s2", "s2"),
  dam = c(NA, "d0", "d4", NA, "d1", "d2", "d2", "d2"),
  sex = c("M", "F", "M", "F", "F", "F", "F", "M"),
  stringsAsFactors = FALSE
)
pedOne[!pedOne$id %in% uPedOne$id, ]
newPed <- addIdRecords(ids = "s1", pedOne, uPedOne)
pedOne[!pedOne$id %in% newPed$id, ]
newPed[newPed$id == "s1", ]

Description

Pedigree curation function Given a pedigree, find any IDs listed in the "sire" or "dam" columns that lack their own line entry and generate one.

Usage

addParents(ped)

Arguments

Details

This must be run after addUIds since the IDs made there are used by addParents

Value

An updated pedigree with entries added as necessary. Entries have the id and sex specified; all remaining columns except recordStatus are filled with NA. A recordStatus column is added (replacing any existing one) with "original" for the existing records and "added" for the new ones.

Examples

pedTwo <- data.frame(
  id = c("d1", "s2", "d2", "o1", "o2", "o3", "o4"),
  sire = c(NA, NA, NA, "s1", "s1", "s2", "s2"),
  dam = c(NA, NA, NA, "d1", "d2", "d2", "d2"),
  sex = c("F", "M", "F", "F", "F", "F", "M"),
  stringsAsFactors = FALSE
)
newPed <- addParents(pedTwo)
newPed

Description

Build a group data frame with ID, sex, and age

Usage

addSexAndAgeToGroup(ids, ped)

Arguments

Details

An empty ids vector yields a zero-row data frame that still contains all three columns (ids, sex, age), with sex an empty factor, so the returned schema does not depend on the number of ids supplied.

Value

A data frame with columns ids, sex and age (current age). Every id must occur exactly once in ped$id; an id that is missing from or duplicated in ped causes an error rather than an NA.

Examples

library(nprcgenekeepr)
data("qcBreeders")
data("qcPed")
df <- addSexAndAgeToGroup(ids = qcBreeders, ped = qcPed)
head(df)

Description

This must be run prior to addParents since the IDs made herein are used by addParents

Usage

addUIds(ped, format = getAutoIdFormat())

Arguments

Details

The generated placeholder IDs default to the form Unnnn (a leading "U" plus a zero-padded integer), so they are alphanumeric and never contain a period ("."), honoring the ID rule enforced at data input by qcStudbook. The format is configurable via setAutoIdFormat (default "U%04d"). A candidate ID that is already in the pedigree is skipped, so a placeholder never duplicates the ID of a real animal.

Value

The updated pedigree in which each missing sire or dam of a single-parent record is replaced by a generated placeholder ID.

Examples

pedTwo <- data.frame(
  id = c("s1", "d1", "s2", "d2", "o1", "o2", "o3", "o4"),
  sire = c(NA, "s0", "s4", NA, "s1", "s1", "s2", "s2"),
  dam = c("d0", "d0", "d4", NA, "d1", "d2", "d2", "d2"),
  sex = c("M", "F", "M", "F", "F", "F", "F", "M"),
  stringsAsFactors = FALSE
)
newPed <- addUIds(pedTwo)
newPed[newPed$id == "s1", ]
pedThree <-
  data.frame(
    id = c("s1", "d1", "s2", "d2", "o1", "o2", "o3", "o4"),
    sire = c("s0", "s0", "s4", NA, "s1", "s1", "s2", "s2"),
    dam = c(NA, "d0", "d4", NA, "d1", "d2", "d2", "d2"),
    sex = c("M", "F", "M", "F", "F", "F", "F", "M"),
    stringsAsFactors = FALSE
  )
newPed <- addUIds(pedThree)
newPed[newPed$id == "s1", ]

Description

Part of Genetic Value Analysis

Usage

alleleFreq(alleles, ids = NULL)

Arguments

Details

If ids are provided, the function will only count the unique alleles for an individual (homozygous alleles will be counted as 1).

Value

A data.frame with columns allele (a factor) and freq. This is a table of allele counts within the population.

Examples

library(nprcgenekeepr)
data("ped1Alleles")
ids <- ped1Alleles$id
alleles <- ped1Alleles[, !(names(ped1Alleles) %in% c("id", "parent"))]
aF <- alleleFreq(alleles[[1]], ids = NULL)
aF[aF$freq >= 10, ]

Description

Pure merge for the longitudinal colony snapshot workflow (issue #167): takes a validated snapshot history (or NULL / a zero-row history for a first snapshot) and one new snapshot row, and returns the merged, date-ordered history. It writes nothing — script users persist the result themselves (e.g. write.csv(..., row.names = FALSE)), and the Shiny application writes only through a user-initiated download.

Usage

appendColonySnapshot(history, snapshot)

Arguments

Details

The new snapshot must carry the same schemaVersion as the history, and its (snapshotDate, membershipRule) pair must not already be present — the pair identifies a snapshot uniquely. The same date under a different membership rule is legal.

Value

The merged history, ordered by snapshotDate.

Examples

history <- checkSnapshotHistory(readSnapshotHistory(system.file(
  "extdata", "examples", "example_snapshot_history.csv",
  package = "nprcgenekeepr"
)))
snapshot <- history[3L, ]
snapshot$snapshotDate <- as.Date("2026-07-15")
appendColonySnapshot(history, snapshot)

Description

Writes pairwise kinship coefficients from outside information into a computed kinship matrix, replacing the pedigree-derived value for the named pairs. Each (id1, id2, kinship) row sets both kmat[id1, id2] and its symmetric twin kmat[id2, id1]; all other cells are unchanged. This is a direct cell replacement – it does not propagate to descendant rows. A successful call reports the number of overrides applied with a message().

Usage

applyKinshipOverrides(kmat, overrides)

Arguments

Details

kmat must be a dense, symmetric, id-named base R matrix (the object kinship returns); a sparse Matrix object is out of contract. The function is strict: it stop()s on an id absent from the matrix and on a value above the exact positive-semi-definiteness bound sqrt(kmat[id1, id1] * kmat[id2, id2]). Soft, run-preserving handling of ids outside the analysis set is the caller's responsibility – reportGV warn-drops non-member ids before calling this. This function's own algorithm – writing outside-information overrides onto an already-computed matrix – is never applied inside kinship itself (it has several callers, including two simulations that must not take current-kinship overrides). This is a different kind of adjustment than kinship's own twinRelations parameter: a declared MZ-twin pair's genetic identity is a structural pedigree fact, like sire/dam/sex, not outside information overriding a pedigree-derived computation – both simulations pass a known twin pair through unchanged, so twin identity needs no shielding of the kind overrides require. See docs/planning/twin-relations-kinship-computation-plan.md §2.3.

Value

kmat with the override cells replaced (symmetric).

Examples

ped <- nprcgenekeepr::qcPed
kmat <- kinship(ped$id, ped$sire, ped$dam, ped$gen)
overrides <- data.frame(
  id1 = ped$id[1], id2 = ped$id[2], kinship = 0.25,
  stringsAsFactors = FALSE
)
kmat <- applyKinshipOverrides(kmat, overrides)

Description

Server function for the main GeneKeepR Shiny application. Initializes all modules and manages shared reactive state between them.

Usage

appServer(input, output, session)

Arguments

Details

The server handles:

• Configuration loading from site-specific config files

• Navigation button handlers for the home page

• Dynamic tab management for QC errors and changed columns

• Module initialization and data flow between modules

Value

No return value, called for side effects. As a 'Shiny' server function, appServer() is invoked by the 'Shiny' runtime to wire up the application's reactive outputs, observers, and module servers for a running GeneKeepR session.

See Also

appUI for the corresponding UI function

modInputServer for data input module

modPedigreeServer for pedigree browser module

modGeneticValueServer for genetic value analysis

shouldShowChangedColsTab for changed columns tab logic

loadSiteConfig for site configuration loading

Description

Main Application UI for nprcgenekeepr

Usage

appUI(siteInfo = NULL)

Arguments

Value

A shiny.tag.list object (as produced by shiny::tagList()) describing the complete GeneKeepR user interface; pass it as the ui argument to shiny::shinyApp() or shiny::runApp().

Description

Assign parent alleles randomly

Usage

assignAlleles(alleles, parentType, parent, id, n)

Arguments

Value

The original list alleles passed into the function with newly randomly assigned alleles to each id based on dam and sire genotypes.

Examples

alleles <- list(alleles = list(), counter = 1)
alleles <- assignAlleles(alleles,
  parentType = "sire", parent = NA,
  id = "o1", n = 4
)
alleles
alleles <- assignAlleles(alleles,
  parentType = "dam", parent = NA,
  id = "o1", n = 4
)
alleles

Description

Converts a validated long-format marker genotype table (see checkMarkerGenotypeFile) into a wide id x locus character matrix, the input shape markerKinship consumes.

Usage

buildMarkerGenotypeMatrix(genotype)

Arguments

Details

Row and column order follow first appearance in genotype, not a string sort – a string sort would place "L10" before "L2" for any panel with more than nine loci, silently scrambling locus order. If genotype has more than one row for the same id x locus, the last row silently overwrites the others, so run checkMarkerGenotypeFile first.

Value

A character matrix with one row per unique id and one column per unique locus. Each cell holds that individual's two alleles at that locus, sorted (by string collation, so locale-dependent for mixed-case alleles) and joined by "/" (e.g. "A/B"), or NA when that individual has no genotype record at that locus. A record with a missing allele gives NA, the same as a record that is not in the file; checkMarkerGenotypeFile does not reject such rows.

See Also

checkMarkerGenotypeFile, markerKinship

Examples

library(nprcgenekeepr)
markerGenotype <- data.frame(
  id = c("A", "A", "B", "B"),
  locus = c("L1", "L2", "L1", "L2"),
  allele1 = c("A", "A", "A", "A"),
  allele2 = c("A", "B", "B", "B"),
  stringsAsFactors = FALSE
)
buildMarkerGenotypeMatrix(markerGenotype)

Description

Part of Genetic Value Analysis

Usage

calcA(alleles, threshold = 1L, byID = FALSE)

Arguments

Value

A matrix with named rows indicating the number of rare alleles (0, 1 or 2) an animal had during each round of simulation (indicated in columns).

References

Ballou JD, Lacy RC. 1995. Identifying genetically important individuals for management of genetic variation in pedigreed populations, p 77-111. In: Ballou JD, Gilpin M, Foose TJ, editors. Population management for survival and recovery. New York (NY): Columbia University Press.

MacCluer JW, et al. 1986. Pedigree analysis by computer simulation. Zoo Biology 5:147-160.

See Also

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

Examples

library(nprcgenekeepr)
rare <- calcA(nprcgenekeepr::ped1Alleles, threshold = 3, byID = FALSE)

Description

Part of Pedigree Curation

Usage

calcAge(birth, exit)

Arguments

Details

Given vectors of birth and exit dates, calculate an individuals age. If no exit date is provided, the calculation is based on the current date.

Value

A numeric vector (NA allowed) indicating age in years, rounded to one decimal place, from "birth" to "exit" or the current date if "exit" is NA. If birth has length zero, it is returned unchanged (a Date, not numeric, vector).

Examples

library(nprcgenekeepr)
qcPed <- nprcgenekeepr::qcPed
originalAge <- qcPed$age ## ages calculated at time of data collection
currentAge <- calcAge(qcPed$birth, qcPed$exit) ## assumes no changes in
## colony

Description

Part of the Genetic Value Analysis

Usage

calcFE(ped)

Arguments

Details

The pedigree must have no partial parentage (every animal has both parents known or both unknown); calcFE stops with an error otherwise.

Value

The founder equivalents FE = 1 / sum(p ^ 2), where p is the vector of founder mean contributions to the current descendants.

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(), calcFEFG(), calcFG(), calcFGSE(), calcGU(), calcGUSE(), calcGeneDiversity(), calcKurtosis(), calcNeSexRatio(), calcNeVariance(), calcRetention(), calcSkewness()

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)
fe <- calcFE(ped)
feFactors <- calcFE(pedFactors)

Description

Part of the Genetic Value Analysis

Usage

calcFEFG(ped, alleles)

Arguments

Value

The list containing the founder equivalents, FE = 1 / sum(p ^ 2), and 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.

FE is deterministic and always returned. FG is NA (with a warning) when a contributing founder (p > 0) is retained in zero of the gene-drop iterations (r == 0), 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(), calcFG(), calcFGSE(), calcGU(), calcGUSE(), calcGeneDiversity(), calcKurtosis(), calcNeSexRatio(), calcNeVariance(), calcRetention(), calcSkewness()

Examples

data(lacy1989Ped)
## 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 <- nprcgenekeepr::lacy1989Ped
alleles <- lacy1989PedAlleles
pedFactors <- data.frame(
  id = as.factor(ped$id),
  sire = as.factor(ped$sire),
  dam = as.factor(ped$dam),
  gen = ped$gen,
  population = ped$population,
  stringsAsFactors = TRUE
)
allelesFactors <- geneDrop(pedFactors$id, pedFactors$sire, pedFactors$dam,
  pedFactors$gen,
  genotype = NULL, n = 1000,
  updateProgress = NULL
)
feFg <- calcFEFG(ped, alleles)
feFgFactors <- calcFEFG(pedFactors, allelesFactors)

Description

Part of the Genetic Value Analysis

Usage

calcFG(ped, alleles)

Arguments

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(), calcKurtosis(), calcNeSexRatio(), calcNeVariance(), calcRetention(), calcSkewness()

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)

Description

Part of the Genetic Value Analysis

Usage

calcFGSE(ped, alleles)

Arguments

Details

Founder genome equivalents (calcFG) is a Monte Carlo estimate: FG = 1 / sum(p^2 / r), where the founder contributions p are deterministic but the mean allelic retention values r (calcRetention) are averages over the gene-drop iterations, so FG carries sampling error that shrinks as the number of iterations grows. Unlike genome uniqueness (a mean, whose standard error is a column variance), FG is a nonlinear function of r, so its standard error is obtained by the delta method (first-order linearization).

With S = sum(p_f^2 / r_f) and FG = 1 / S, the gradient is dFG/dr_f = FG^2 * p_f^2 / r_f^2. Writing R for the founder-by- iteration retention matrix (each column an independent gene drop), the influence series y_k = sum_f (dFG/dr_f) * R[f, k] has sd(y) / sqrt(K) equal to the full delta-method standard error, including the within-iteration covariance among founders. This influence form is used because it folds in that covariance automatically and never forms the founder-by-founder covariance matrix.

Founders are matched between p and r by name (not position), so the result is correct even when the founders are not in sorted pedigree order.

A contributing founder (p > 0) that is retained in zero of the iterations (r == 0) makes FG undefined (the same degeneracy that calcFG reports as NA); in that case this function returns NA with a warning advising more iterations. Founders that do not contribute to the current population (p == 0) are dropped, so the standard error refers to exactly the founder set FG is computed from.

Value

A single numeric value: the Monte Carlo sampling standard error of the colony founder-genome-equivalent estimate, on the same scale as calcFG. NA (with a warning) when a contributing founder has zero retention.

See Also

calcFG, calcFEFG, calcRetention, calcGUSE, reportGV

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

Examples

library(nprcgenekeepr)
data("lacy1989Ped")
data("lacy1989PedAlleles")
calcFGSE(lacy1989Ped, lacy1989PedAlleles)

Description

Part of the Genetic Value Analysis

Usage

calcGeneDiversity(fg)

Arguments

Details

Gene diversity is the expected heterozygosity retained relative to the founding gene pool, GD = 1 - 1 / (2 * FG), where FG is the founder genome equivalents (see calcFG). It summarizes how much of the founders' allelic diversity still survives: it is 0 when FG = 0.5 (the smallest value FG takes in practice) and approaches (never reaches) 1 as FG grows.

GD is a diversity proportion, not a count of effective individuals, and it is computed over the same analysis set as FG. NA propagates: when FG is NA (the zero-retention degeneracy calcFG() reports), GD is NA.

Value

The gene diversity GD = 1 - 1 / (2 * fg): a single number below 1 (realized values lie in [0, 1)), or NA when fg is NA.

References

Gene diversity is derived here from the founder genome equivalents (calcFG) of 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(), calcFG(), calcFGSE(), calcGU(), calcGUSE(), calcKurtosis(), calcNeSexRatio(), calcNeVariance(), calcRetention(), calcSkewness()

Examples

calcGeneDiversity(20) # 0.975
calcGeneDiversity(52.75) # gene diversity at the qcPed FG

Description

Part of Genetic Value Analysis

Usage

calcGU(alleles, threshold = 1L, byID = FALSE, pop = NULL)

Arguments

Details

The following functions calculate genome uniqueness according to the equation described in Ballou & Lacy.

It should be noted, however that this function differs slightly in that it does not distinguish between founders and non-founders in calculating the statistic.

Ballou & Lacy describe genome uniqueness as "the proportion of simulations in which an individual receives the only copy of a founder allele." We have interpreted this as meaning that genome uniqueness should only be calculated for living, non-founder animals. Alleles possessed by living founders are not considered when calculating genome uniqueness.

We have a differing view on this, since a living founder can still contribute to the population. The function below calculates genome uniqueness for all living animals and considers all alleles. It does not ignore living founders and their alleles.

Our results for genome uniqueness will, therefore differ slightly from those returned by Pedscope. Pedscope calculates genome uniqueness only for non-founders and ignores the contribution of any founders in the population. This will cause Pedscope's genome uniqueness estimates to possibly be slightly higher for non-founders than what this function will calculate.

The estimates of genome uniqueness for founders within the population calculated by this function should match the "founder genome uniqueness" measure calculated by Pedscope.

Note that calcGU computes this statistic for every animal and still includes living founders' alleles; it is unchanged. The genetic value report (reportGV) separately applies a colony decline-to-credit policy that reports genome uniqueness as 0 for unknown-origin both-unknown ("Undetermined") animals.

Value

Dataframe rows: id, col: gu A single-column table of genome uniqueness values as percentages. Rownames are set to 'id' values that are part of the population.

References

Ballou JD, Lacy RC. 1995. Identifying genetically important individuals for management of genetic variation in pedigreed populations, p 77-111. In: Ballou JD, Gilpin M, Foose TJ, editors. Population management for survival and recovery. New York (NY): Columbia University Press.

MacCluer JW, et al. 1986. Pedigree analysis by computer simulation. Zoo Biology 5:147-160.

See Also

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

Examples

library(nprcgenekeepr)
ped1Alleles <- nprcgenekeepr::ped1Alleles
gu_1 <- calcGU(ped1Alleles, threshold = 1, byID = FALSE, pop = NULL)
gu_2 <- calcGU(ped1Alleles, threshold = 3, byID = FALSE, pop = NULL)
gu_3 <- calcGU(ped1Alleles,
  threshold = 3, byID = FALSE,
  pop = ped1Alleles$id[20:60]
)

Description

Part of Genetic Value Analysis

Usage

calcGUSE(alleles, threshold = 1L, byID = FALSE, pop = NULL)

Arguments

Details

Genome uniqueness (calcGU) is a Monte Carlo estimate: it is the average, over the gene-drop iterations, of the proportion of an animal's two allele copies that are population-rare. Because it is an average over independent simulated iterations, it carries sampling error that shrinks as the number of iterations grows.

For animal i let m_ik = rare[i, k] / 2 be the per-iteration value in iteration k (so the mean of m_ik over the K iterations equals gu_i / 100). This function returns the exact Monte Carlo standard error of that mean, on the same percentage scale as calcGU:

$guSE_i = 100 \times \sqrt{\frac{var(m_{i\cdot})}{K}}$

The standard error is computed from the same per-iteration rare-allele matrix (calcA) that calcGU averages, so it is correct for any threshold / byID without a closed-form approximation. An animal whose rare-allele count does not vary across iterations has a standard error of 0.

Value

Dataframe rows: id, col: guSE A single-column table of genome-uniqueness standard errors as percentages. Rownames are set to 'id' values that are part of the population.

See Also

calcGU, calcA, reportGV

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

Examples

library(nprcgenekeepr)
ped1Alleles <- nprcgenekeepr::ped1Alleles
guSE <- calcGUSE(ped1Alleles, threshold = 3, byID = FALSE, pop = NULL)

Description

Part of the Genetic Value Analysis

Usage

calcKurtosis(x, na.rm = TRUE)

Arguments

Details

The bias-adjusted Fisher-Pearson standardized EXCESS kurtosis coefficient, G2 = ((n + 1) * g2 + 6) * (n - 1) / ((n - 2) * (n - 3)), where g2 = m4 / m2^2 - 3 and m2/m4 are the second/fourth central sample moments of x – the "Method 2" adjustment of Joanes and Gill (1998), the same convention SPSS, SAS, and Excel report by default and the type = 2 option in the e1071 CRAN package. Excess (not raw) kurtosis: a normal distribution reads 0. Positive values indicate heavier tails / a sharper peak than normal; negative, lighter tails / a flatter peak.

Returns NA when na.rm = FALSE and x contains an NA, when x has fewer than 4 non-NA values (n <= 3, the adjustment term divides by (n - 2) * (n - 3)) or has zero variance (all remaining values identical) – both would otherwise divide by zero.

Value

The bias-adjusted sample excess kurtosis, a single number; NA when degenerate (see Details).

References

Joanes, D.N. and Gill, C.A. (1998) "Comparing measures of sample skewness and kurtosis." Journal of the Royal Statistical Society: Series D (The Statistician), 47(1), 183-189.

See Also

calcSkewness

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

Examples

calcKurtosis(c(1, 2, 3, 4, 100)) # 4.986866, heavy-tailed
calcKurtosis(c(1, 2, 3, 4, 5)) # -1.2, flatter than normal

Description

Part of the Genetic Value Analysis

Usage

calcNeSexRatio(ped)

Arguments

Details

The sex-ratio effective size, Ne = 4 * nMale * nFemale / (nMale + nFemale), is the effective population size implied by an unequal breeding sex ratio, where nMale and nFemale are the numbers of current living breeders that are known male and known female. It quantifies the diversity lost when many of one sex are bred to few of the other (as in a harem colony): it equals the census count when the sexes are balanced and falls toward four times the rarer sex as the ratio skews.

The breeders are the current living breeders of ped (living animals that appear as a sire or dam, excluding auto-generated unknown parents), independent of which animals are selected as probands – a different population than the analysis-set founder statistics (calcFE, calcFG, calcGeneDiversity). Only animals with a known sex ("M" or "F") are counted; unknown and hermaphrodite breeders are excluded. When either sex is absent among the living breeders (nMale == 0 or nFemale == 0, including no living breeders at all), the result is 0: a single breeding sex contributes no diversity from sex balance.

Like all effective-size estimators this idealizes a Wright-Fisher population (constant size, discrete generations, random union of gametes within each sex); a managed colony departs from those assumptions, so read the result as a sex-ratio index rather than a literal head count.

Value

The sex-ratio effective size, a single non-negative number; 0 when either breeding sex is absent among the living breeders.

References

Crow, J. F. and Kimura, M. (1970) An Introduction to Population Genetics Theory. Harper and Row, New York.

See Also

calcGeneDiversity

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

Examples

ped <- data.frame(
  id = c("s1", "d1", "d2", "k1", "k2"),
  sire = c(NA, NA, NA, "s1", "s1"),
  dam = c(NA, NA, NA, "d1", "d2"),
  sex = c("M", "F", "F", "M", "F"),
  exit = c(NA, NA, NA, NA, NA),
  stringsAsFactors = FALSE
)
calcNeSexRatio(ped) # 4 * 1 * 2 / (1 + 2) = 2.666667

Description

Part of the Genetic Value Analysis

Usage

calcNeVariance(ped)

Arguments

Details

The variance effective size measures the diversity lost to unequal family sizes – typically the dominant reducer of effective size in a harem colony, where a few breeders produce most of the offspring. It is the mean-adjusted Crow & Kimura (1970) form

$N_e = \frac{N \bar{k} - 1}{\bar{k} - 1 + V_k / \bar{k}}$

where N is the number of current living breeders, $\bar{k}$ the mean number of lifetime offspring among them, and $V_k$ the variance of those offspring counts. This general form makes no constant-size assumption and reduces to the classic (4N - 2) / (Vk + 2) at exact replacement ($\bar{k} = 2$); it is preferred over that bare form, which assumes $\bar{k} \approx 2$ and misstates the effective size when the mean family size departs from replacement.

The breeders are the current living breeders of ped (living animals that appear as a sire or dam, excluding auto-generated unknown parents), independent of which animals are selected as probands – a different population than the analysis-set founder statistics (calcFE, calcFG, calcGeneDiversity). Unlike the sex-ratio effective size (calcNeSexRatio), breeders of every sex are counted. When fewer than two living breeders are present the variance is undefined and the result is NA.

Like all effective-size estimators this idealizes a Wright-Fisher population (constant size, discrete generations, random union of gametes); a managed colony departs from those assumptions, so read the result as a family-size-variance index rather than a literal head count.

Value

The variance effective size, a single number; NA when there are fewer than two living breeders.

References

Crow, J. F. and Kimura, M. (1970) An Introduction to Population Genetics Theory. Harper and Row, New York.

See Also

calcNeSexRatio, calcGeneDiversity

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

Examples

ped <- data.frame(
  id = c("s1", "d1", "k1", "k2", "k3"),
  sire = c(NA, NA, "s1", "s1", "s1"),
  dam = c(NA, NA, "d1", "d1", "d1"),
  sex = c("M", "F", "M", "F", "F"),
  exit = c(NA, NA, NA, NA, NA),
  stringsAsFactors = FALSE
)
calcNeVariance(ped) # 2 breeders, equal families: (2*3-1)/(3-1) = 2.5

Description

Part of Genetic Value Analysis

Usage

calcRetention(ped, alleles)

Arguments

Value

A named one-dimensional array with one value per founder: the proportion of the gene-drop simulations (a number from 0 to 1, not a count) in which the founder's allele is retained in the population's descendants.

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(), calcFG(), calcFGSE(), calcGU(), calcGUSE(), calcGeneDiversity(), calcKurtosis(), calcNeSexRatio(), calcNeVariance(), calcSkewness()

Examples

library(nprcgenekeepr)
data("lacy1989Ped")
data("lacy1989PedAlleles")
ped <- lacy1989Ped
alleles <- lacy1989PedAlleles
retention <- calcRetention(ped, alleles)

Description

Part of the Genetic Value Analysis

Usage

calcSkewness(x, na.rm = TRUE)

Arguments

Details

The bias-adjusted Fisher-Pearson standardized skewness coefficient, G1 = g1 * sqrt(n * (n - 1)) / (n - 2), where g1 = m3 / m2^1.5 and m2/m3 are the second/third central sample moments of x – the "Method 2" adjustment of Joanes and Gill (1998), the same convention SPSS, SAS, and Excel report by default and the type = 2 option in the e1071 CRAN package. A positive value indicates a longer right tail; negative, a longer left tail; 0, a symmetric distribution.

Returns NA when na.rm = FALSE and x contains an NA, when x has fewer than 3 non-NA values (n <= 2, the adjustment term divides by n - 2) or has zero variance (all remaining values identical) – both would otherwise divide by zero.

Value

The bias-adjusted sample skewness, a single number; NA when degenerate (see Details).

References

Joanes, D.N. and Gill, C.A. (1998) "Comparing measures of sample skewness and kurtosis." Journal of the Royal Statistical Society: Series D (The Statistician), 47(1), 183-189.

See Also

calcKurtosis

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

Examples

calcSkewness(c(1, 2, 3, 4, 100)) # 2.232396, a long right tail
calcSkewness(c(1, 2, 3, 4, 5)) # 0, symmetric

Description

Computes per-metric deltas between two snapshots of a longitudinal colony snapshot history (issue #167). The two snapshots must belong to the same membership-rule series: a snapshot is identified by its (snapshotDate, membershipRule) pair, and trend comparisons are only meaningful like with like. The result carries one row per numeric schema column — the 18 metric columns plus the 3 composition counts (nAnimals, nMales, nFemales), whose deltas make membership churn between the two snapshots visible.

Usage

calcSnapshotDeltas(history, from, to, membershipRule = NULL)

Arguments

Details

The comparabilityFlag column surfaces provenance differences between the two snapshots rather than refusing them: it is NA_character_ when the snapshots are comparable and otherwise names each differing provenance field with both values. A guIter difference flags the gene-drop-derived metrics (fg, fgSE, neGD, and the gu aggregates); a guThresh difference flags only the gu aggregates (the threshold reaches only the genome-uniqueness computation); a packageVersion difference flags every row.

Value

A data.frame with columns metric, from, to, delta (to - from), and comparabilityFlag, one row per numeric schema column in schema order.

Examples

history <- checkSnapshotHistory(readSnapshotHistory(system.file("extdata",
  "examples", "example_snapshot_history.csv",
  package = "nprcgenekeepr"
)))
deltas <- calcSnapshotDeltas(history,
  from = "2025-01-15", to = "2025-07-15",
  membershipRule = "wholePedigree"
)

Description

The Males are counted when the ped$sex value is "M". Females are counted when the ped$sex value is not "M". This means animals with ambiguous sex are counted with the females.

Usage

calculateSexRatio(ids, ped, additionalMales = 0L, additionalFemales = 0L)

Arguments

Value

Numeric value of the sex ratio of the animals provided, expressed as the number of non-males per male. It is Inf when there are no males (and no males are added) but at least one non-male; 0 when ids is empty, no females are added and males are added; and NA when ids is empty and nothing is added.

Examples

library(nprcgenekeepr)
data("qcBreeders")
data("pedWithGenotype")
available <- c(
  "JGPN6K", "8KM1MP", "I9TQ0T", "Q0RGP7", "VFS0XB", "CQC133",
  "2KULR3", "HOYW0S", "FHV13N", "OUM6QF", "6Z7MD9", "CFPEEU",
  "HLI95R", "RI0O7F", "7M51X5", "DR5GXB", "170ZTZ", "C1ICXL"
)
nonMales <- c(
  "JGPN6K", "8KM1MP", "I9TQ0T", "Q0RGP7", "CQC133",
  "2KULR3", "HOYW0S", "FHV13N", "OUM6QF", "6Z7MD9", "CFPEEU",
  "HLI95R", "RI0O7F", "7M51X5", "DR5GXB", "170ZTZ", "C1ICXL"
)
male <- "VFS0XB"
calculateSexRatio(ids = male, ped = pedWithGenotype)
calculateSexRatio(ids = nonMales, ped = pedWithGenotype)
calculateSexRatio(ids = available, ped = pedWithGenotype)
calculateSexRatio(
  ids = available, ped = pedWithGenotype,
  additionalMales = 1L
)
calculateSexRatio(
  ids = available, ped = pedWithGenotype,
  additionalFemales = 1L
)
calculateSexRatio(
  ids = available, ped = pedWithGenotype,
  additionalMales = 1, additionalFemales = 1L
)
calculateSexRatio(
  ids = nonMales, ped = pedWithGenotype,
  additionalMales = 1, additionalFemales = 0L
)
calculateSexRatio(
  ids = character(0), ped = pedWithGenotype,
  additionalMales = 1, additionalFemales = 0L
)

Description

Checks the structure and domain of a center-configurable ancestry compatibility rules table (issue #168). Each row is one unordered pair of standardized ancestry levels plus a severity: block rules exclude the pairing during breeding-group formation, flag rules annotate it afterward. It mirrors checkKinshipOverrides: it stop()s on structural or domain errors and returns the coerced table when the input is acceptable. An empty table (zero rules) is valid.

Usage

checkAncestryRules(rules)

Arguments

Details

Ancestry levels must come from convertAncestry's standardized vocabulary: CHINESE, INDIAN, HYBRID, JAPANESE, OTHER, UNKNOWN. Levels are coerced to uppercase and severity to lowercase before validation, so a hand-edited file's casing never matters. A rule may pair a level with itself (e.g. HYBRID with HYBRID); duplicated unordered pairs are a data error the user must resolve. Because convertAncestry maps a blank ancestry to UNKNOWN but any unrecognized text – including a literal re-standardized "UNKNOWN" string – to OTHER, a table that names one of UNKNOWN/OTHER without the other draws a warning here: a center wanting conservative treatment of animals without usable ancestry information almost always wants both.

Value

The validated rules data.frame with ancestry1 and ancestry2 coerced to uppercase character and severity to lowercase character. Leading and trailing white space is trimmed from these three columns.

Examples

rules <- data.frame(
  ancestry1 = c("INDIAN", "INDIAN"),
  ancestry2 = c("CHINESE", "HYBRID"),
  severity = c("block", "flag"), stringsAsFactors = FALSE
)
checkAncestryRules(rules)

Description

Check a changed-columns list for non-empty fields

Usage

checkChangedColsLst(changedCols)

Arguments

Value

Returns TRUE if any changed-columns field is non-empty, otherwise FALSE.

Examples

library(nprcgenekeepr)
library(lubridate)
pedOne <- data.frame(
  ego_id = c(
    "s1", "d1", "s2", "d2", "o1", "o2", "o3",
    "o4"
  ),
  `si re` = c(NA, NA, NA, NA, "s1", "s1", "s2", "s2"),
  dam_id = c(NA, NA, NA, NA, "d1", "d2", "d2", "d2"),
  sex = c("F", "M", "M", "F", "F", "F", "F", "M"),
  birth_date = mdy(
    paste0(
      sample(1:12, 8, replace = TRUE), "-",
      sample(1:28, 8, replace = TRUE), "-",
      sample(seq(0, 15, by = 3), 8, replace = TRUE) +
        2000
    )
  ),
  stringsAsFactors = FALSE, check.names = FALSE
)

errorLst <- qcStudbook(pedOne, reportErrors = TRUE, reportChanges = TRUE)
checkChangedColsLst(errorLst$changedCols)

Description

The "show every problem at once" companion to resolveCrossCenterIds (issue #149 Slice 1), sharing its four validation checks – id existence, mapping uniqueness, undeclared id collisions, and conflicting recorded parents – via the same internal helpers (D2, docs/planning/issue149-cross-center-identity-mapping-workflow-plan.md section 3). resolveCrossCenterIds() stop()s on the first problem it finds; checkCrossCenterMapping() never stop()s on a domain problem – every one found becomes a row in the returned data.frame instead, so a curator can see and fix every issue at once rather than one at a time. A structural problem (a required column missing from any of the three inputs) still stop()s immediately, matching every other checkXxx() function in this package (checkKinshipOverrides, checkTwinRelations).

Usage

checkCrossCenterMapping(pedA, pedB, mapping)

Arguments

Details

Existence and uniqueness problems (tier A) are checked first; if either is present, only those are returned and collision/conflict checks (tier B) are skipped entirely, since a mapped id that does not resolve to a real pedigree row makes those checks meaningless. Tier B – and both of its checks, across every mapped pair – runs only once tier A is clean.

Value

A data.frame of every domain problem found, with columns type ("existence", "uniqueness", "collision", or "conflict"), ids (the offending id(s), as a single comma-separated string), and message (a human-readable description). Zero rows means none of these four kinds of problem was found. resolveCrossCenterIds can still stop on conflicting values in other columns shared by both pedigrees (e.g. sex), which this function does not check.

See Also

resolveCrossCenterIds

Examples

library(nprcgenekeepr)
pedA <- data.frame(
  id = c("P1", "P2", "T1"), sire = c(NA, NA, "P1"), dam = c(NA, NA, "P2"),
  stringsAsFactors = FALSE
)
pedB <- data.frame(
  id = c("X9", "O1"), sire = c(NA, "X9"), dam = c(NA, NA),
  stringsAsFactors = FALSE
)
mapping <- data.frame(idA = "T1", idB = "X9", stringsAsFactors = FALSE)
checkCrossCenterMapping(pedA, pedB, mapping) # zero rows: clean

Description

Check an error list for non-empty fields

Usage

checkErrorLst(errorLst)

Arguments

Value

Returns FALSE if all fields are empty or the list is NULL otherwise TRUE. The changedCols field is not examined; use checkChangedColsLst for it.

Examples

errorLst <- qcStudbook(nprcgenekeepr::pedFemaleSireMaleDam,
  reportErrors = TRUE
)
checkErrorLst(errorLst)

Description

Checks to ensure the content and structure are appropriate for a genotype file. These checks are simply based on expected columns and legal domains. The function stops (an error, not a warning) when the dataframe has fewer than three columns, when the first column name does not contain "id" (any case), when any column is named first or second (any case), or when an allele in columns 2 or 3 that reads as an integer is above 10000 (it would collide with the integer codes addGenotype assigns).

Usage

checkGenotypeFile(genotype)

Arguments

Value

The genotype dataframe, checked for the column count, the first column's name and the allele range described above; no column types are checked. The returned genotype file has the first column name forced to "id".

Examples

library(nprcgenekeepr)
ped <- nprcgenekeepr::qcPed
ped <- ped[order(ped$id), ]
genotype <- data.frame(
  id = ped$id[50 + 1:20],
  first_name = paste0("first_name", 1:20),
  second_name = paste0("second_name", 1:20),
  stringsAsFactors = FALSE
)

## checkGenotypeFile disallows dataframe with < 3 columns
tryCatch(
  {
    checkGenotypeFile(genotype[, c("id", "first_name")])
  },
  warning = function(w) {
    cat("Warning produced")
  },
  error = function(e) {
    cat("Error produced")
  }
)

Description

Checks the structure and domain of an outside-information kinship override table. The table supplies pairwise kinship coefficients (id1, id2, kinship) that applyKinshipOverrides writes into a computed kinship matrix, replacing the pedigree-derived value for those pairs. It mirrors checkGenotypeFile: it stop()s on structural or domain errors and returns the (id-coerced) table when the input is acceptable.

Usage

checkKinshipOverrides(overrides)

Arguments

Details

kinship is the kinship coefficient f (the probability that an allele drawn at random from each of the two animals is identical by descent), not the coefficient of relatedness r (= 2f for non-inbred animals). Supplying r – e.g. 0.25 for half-sibs whose true f is 0.125 – silently corrupts the matrix, so an off-diagonal value above 0.5 (the maximum for a non-inbred pair) draws a warning here. It also stop()s on a missing (NA) or negative kinship, on a row whose id1 equals its id2, and on a duplicated unordered pair. The exact positive-semi-definiteness bound is enforced by applyKinshipOverrides once the matrix diagonal is known.

Value

The validated overrides data.frame with id1 and id2 coerced to character.

Examples

overrides <- data.frame(
  id1 = c("A1", "A3"), id2 = c("A2", "A4"),
  kinship = c(0.25, 0.125), stringsAsFactors = FALSE
)
checkKinshipOverrides(overrides)

Description

Validates the structure of a long-format marker genotype table (one row per id x locus), the same schema checkMarkerGenotypeFile checks – but, unlike that function, does not require every locus to be biallelic. This is a new, sibling validator for the linkage-aware and haplotype-block metrics family (issue #153): real colony marker panels (e.g. microsatellite/STR panels) are routinely multiallelic, a data shape the KING-robust kinship estimator checked by checkMarkerGenotypeFile cannot represent, but which buildMarkerGenotypeMatrix pivots without error. checkMarkerGenotypeFile itself, and everything downstream of it (markerKinship), is untouched by this function.

Usage

checkLinkageMarkerGenotypeFile(genotype)

Arguments

Details

All of checkMarkerGenotypeFile's structural checks are retained – exactly four columns, id as the first column, no duplicate id x locus rows – except the per-locus more-than-two-distinct-alleles rejection, which is deliberately omitted.

Value

The genotype dataframe, checked to ensure the column count, first-column identity, and row uniqueness are all valid. The returned dataframe has its column names forced to c("id", "locus", "allele1", "allele2").

See Also

checkMarkerGenotypeFile, buildMarkerGenotypeMatrix

Examples

library(nprcgenekeepr)
markerGenotype <- data.frame(
  id = c("W", "X", "Y", "Z"),
  locus = c("L1", "L1", "L1", "L1"),
  allele1 = c("A", "A", "A", "A"),
  allele2 = c("B", "C", "A", "D"),
  stringsAsFactors = FALSE
)
checkLinkageMarkerGenotypeFile(markerGenotype)

Description

Validates the structure of a locus-metadata sidecar table (locus, chrom, pos, and optionally cM) – the schema introduced by issue #152's own design decision and reused verbatim by issue #153 – and classifies each locus into one of three coverage tiers, following a PLINK-style three-state coverage model: "full" (both chrom and pos present; cM is optional even within "full"), "partial" (exactly one of chrom/pos present), or "none" (neither present). A value counts as present when it is not NA; an empty or all-space chrom is treated as missing and is returned as NA.

Usage

checkLocusMetadata(locusMetadata)

Arguments

Details

A locus-metadata table has one row per locus (not per individual x locus, unlike checkMarkerGenotypeFile's genotype table). Real curated marker panels typically supply little to no locus-order metadata – this function reports coverage explicitly per locus rather than requiring complete metadata before any downstream use.

Value

The locus-metadata dataframe, checked to ensure the column count, first-column identity, and row uniqueness are all valid, with a new coverage column appended ("full"/"partial"/ "none"). The returned dataframe has locus and chrom coerced to character, and its column names forced to locus, chrom, pos (and cM when there are four columns) whatever the input names were.

References

Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., & Sham, P. C. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics, 81(3), 559-575. doi:10.1086/519795

See Also

checkMarkerGenotypeFile

Examples

library(nprcgenekeepr)
locusMetadata <- data.frame(
  locus = c("L1", "L2", "L3"),
  chrom = c("1", "1", NA),
  pos = c(1000000, NA, NA),
  stringsAsFactors = FALSE
)
checkLocusMetadata(locusMetadata)

Description

Validates the structure and legal domain of a long-format marker genotype table (one row per id x locus), the input format for the marker-based (KING-robust) kinship estimator (markerKinship). This is a new, sibling schema to the single-locus first_name/second_name genotype format checked by checkGenotypeFile – that function, and everything downstream of it (addGenotype, geneDrop), is untouched by this one.

Usage

checkMarkerGenotypeFile(genotype)

Arguments

Details

The KING-robust kinship estimator (Manichaikul et al. 2010) is defined for biallelic markers only – every genotype is classified as homozygous-reference, heterozygous, or homozygous-alternate, with no representation for a third allele at a locus. A locus with more than two distinct alleles observed anywhere in the input is therefore rejected outright, rather than silently producing an uninterpretable kinship estimate.

Value

The genotype dataframe, checked to ensure the column count, first-column identity, per-locus allele count, and row uniqueness are all valid. The returned dataframe has its column names forced to c("id", "locus", "allele1", "allele2").

References

Manichaikul, A., Mychaleckyj, J. C., Rich, S. S., Daly, K., Sale, M., & Chen, W.-M. (2010). Robust relationship inference in genome-wide association studies. Bioinformatics, 26(22), 2867-2873. doi:10.1093/bioinformatics/btq559

See Also

buildMarkerGenotypeMatrix, markerKinship

Examples

library(nprcgenekeepr)
markerGenotype <- data.frame(
  id = c("A", "A", "B", "B"),
  locus = c("L1", "L2", "L1", "L2"),
  allele1 = c("A", "A", "A", "A"),
  allele2 = c("A", "B", "B", "B"),
  stringsAsFactors = FALSE
)
checkMarkerGenotypeFile(markerGenotype)

Description

Validates the structure of a wide per-animal MHC haplotype designation table: exactly three columns – id plus the animal's two named haplotype designations, one row per animal. This is the explicit input designation for MHC haplotype reporting (issue #148): a curator designates data as MHC by supplying it through this format, and no MHC semantics are ever inferred from locus or column names (the format has no locus names). A new, sibling input family: the long-format marker validators (checkMarkerGenotypeFile, checkLinkageMarkerGenotypeFile, checkSequenceGenotypeFile) and the single-locus checkGenotypeFile path are untouched by this one.

Usage

checkMhcHaplotypeFile(genotype)

Arguments

Details

Column names are not prescribed: the first column must be id-like (case-insensitive match on "id") and is forced to id; the second and third are forced to haplotype1 and haplotype2, so a colony file with, e.g., first_name/second_name headers (the bundled rhesusGenotypes shape) loads unchanged. NA/empty cells pass validation – a missing call is the statistics layer's concern, not a structural error. A trailing ? on a designation (an uncertain, provisional call) is likewise not validation's business; see the issue #148 design plan's D3 parse rule. Haplotype designations are otherwise opaque labels: never parsed, split, or matched against MHC region names.

Value

The genotype dataframe, checked to ensure the column count, first-column identity, and row uniqueness are all valid. The returned dataframe has its column names forced to c("id", "haplotype1", "haplotype2").

See Also

rhesusGenotypes, checkMarkerGenotypeFile

Examples

library(nprcgenekeepr)
checked <- checkMhcHaplotypeFile(rhesusGenotypes)
head(checked)

Description

Ensure parents are sufficiently older than offspring

Usage

checkParentAge(
  sb,
  minSireAge = NULL,
  minDamAge = NULL,
  minParentAge = lifecycle::deprecated(),
  reportErrors = FALSE
)

Arguments

Value

A dataframe containing rows for each animal where one or more parent was younger than the minimum breeding age (minSireAge or minDamAge, or the species floor when they are NULL) at the animal's birth. It contains all of the columns in the original sb dataframe, with exit converted to character and the rows reordered, plus the following added columns:

1. sireBirth – sire's birth date

2. sireAge – age of sire in years on the date indicated by birth.

3. damBirth – dam's birth date

4. damAge – age of dam in years on the date indicated by birth.

Examples

library(nprcgenekeepr)
qcPed <- nprcgenekeepr::qcPed
checkParentAge(qcPed, minSireAge = 2L, minDamAge = 2L)
checkParentAge(qcPed, minSireAge = 3L, minDamAge = 3L)
checkParentAge(qcPed, minSireAge = 5L, minDamAge = 5L)
checkParentAge(qcPed, minSireAge = 6L, minDamAge = 6L)
head(checkParentAge(qcPed, minSireAge = 10L, minDamAge = 10L))

Description

Check column names for required columns

Usage

checkRequiredCols(cols, reportErrors)

Arguments

Details

When reportErrors = TRUE, NA entries in cols are treated as ordinary non-matching column names when building the list of missing required columns, rather than causing an error. (Earlier versions could error with "missing value where TRUE/FALSE needed" on such out-of-contract input.)

Value

NULL is returned if all required columns are present. See description of reportErrors for what happens when required columns are missing.

Examples

library(nprcgenekeepr)
requiredCols <- getRequiredCols()
cols <- strsplit(
  paste0(
    "id,sire,siretype,dam,damtype,sex,numberofparentsknown,birth,",
    "arrivalatcenter,death,departure,status,ancestry,fromcenter?,",
    "origin"
  ),
  ","
)[[1L]]
checkRequiredCols(cols, reportErrors = TRUE) # NULL: all required present
# A missing required column is returned by name
checkRequiredCols(setdiff(cols, "birth"), reportErrors = TRUE)

Description

Validates the structure of a long-format marker genotype table (one row per id x locus), the same schema checkMarkerGenotypeFile checks – but sized and hardened for sequence-derived panels (issue #152): a soft, overridable warning above a sparse/GBS-scale panel-size ceiling, and an explicit rejection of a literal "." allele value (VCF's missing-genotype placeholder), rather than silently counting it as a real allele. Optionally validates the accompanying locus-metadata sidecar's own structure by reusing checkLocusMetadata, rather than reinventing that check; the sidecar is not cross-checked against the loci of the genotype table.

Usage

checkSequenceGenotypeFile(genotype, locusMetadata = NULL, maxLoci = 50000L)

Arguments

Details

All of checkMarkerGenotypeFile's structural checks are retained – exactly four columns, id as the first column, no duplicate id x locus rows, and the same biallelic-only rejection the KING-robust kinship estimator (Manichaikul et al. 2010) requires. Two rules are new: a literal "." allele value is rejected before the biallelic count is even checked, so a curator sees the correct, actionable error rather than a misleading "more than two alleles" report (a genotype-preprocessing pipeline emitting VCF-style missingness that was never converted to NA is a real, anticipated failure mode – see Danecek et al. 2011 for the VCF missing-genotype convention this guards against); and a locus count above maxLoci produces a warning(), not a stop() – the "right" ceiling for this package's own vectorized implementations is not yet empirically known, so a hard stop would risk blocking legitimate data.

Value

The genotype dataframe, checked to ensure the column count, first-column identity, per-locus allele count, absence of a literal "." placeholder, and row uniqueness are all valid. The returned dataframe has its column names forced to c("id", "locus", "allele1", "allele2").

References

Manichaikul, A., Mychaleckyj, J. C., Rich, S. S., Daly, K., Sale, M., & Chen, W.-M. (2010). Robust relationship inference in genome-wide association studies. Bioinformatics, 26(22), 2867-2873. doi:10.1093/bioinformatics/btq559

Danecek, P., et al. (2011). The variant call format and VCFtools. Bioinformatics, 27(15), 2156-2158. doi:10.1093/bioinformatics/btr330

See Also

checkMarkerGenotypeFile, checkLinkageMarkerGenotypeFile, checkLocusMetadata, buildMarkerGenotypeMatrix

Examples

library(nprcgenekeepr)
markerGenotype <- data.frame(
  id = c("A", "A", "B", "B"),
  locus = c("L1", "L2", "L1", "L2"),
  allele1 = c("A", "A", "A", "A"),
  allele2 = c("A", "B", "B", "B"),
  stringsAsFactors = FALSE
)
checkSequenceGenotypeFile(markerGenotype)

Description

Checks the structure and domain of a longitudinal colony snapshot history (issue #167): one row per recorded snapshot, in the 27-column version-1 schema. It mirrors checkKinshipOverrides: it stop()s with a specific message on structural or domain errors and returns the coerced history when the input is acceptable.

Usage

checkSnapshotHistory(history)

Arguments

Details

The schema has three groups of nine columns:

Provenance / comparability

schemaVersion, snapshotDate, packageVersion, membershipRule, guIter, guThresh, nAnimals, nMales, nFemales. These let successive snapshots be compared like with like: snapshots generated under different membership rules, guIter settings, or package versions are comparable only with care, and trend displays flag such mixed-provenance series rather than hiding them.

Colony scalars

fe, fg, fgSE, neGD, neSexRatio, neVariance, nMaleFounders, nFemaleFounders, nFounders — verbatim from reportGV.

Colony aggregates

meanIndivMeanKin, medianIndivMeanKin, skewnessIndivMeanKin, kurtosisIndivMeanKin, meanGu, medianGu, meanGuSE, skewnessGu, kurtosisGu — the Summary Statistics definitions applied to the per-animal indivMeanKin and gu report columns.

Violations rejected: missing columns, non-numeric metric or count fields, an unrecognized schemaVersion, a duplicated (snapshotDate, membershipRule) pair, and malformed (non ISO-8601) dates. The membershipRule field is an open string — no enumeration is enforced, so new rule names are additive. Extra columns are ignored, matching the sibling validators.

Value

The validated history with snapshotDate coerced to Date, packageVersion and membershipRule coerced to character, and schemaVersion and the count columns coerced to integer.

Examples

history <- readSnapshotHistory(system.file("extdata", "examples",
  "example_snapshot_history.csv",
  package = "nprcgenekeepr"
))
history <- checkSnapshotHistory(history)

Description

Checks the structure and domain of a twin/zygosity sidecar table. The table supplies pairwise twin declarations (id1, id2, code) that record which individuals in a pedigree are twins and with what twin zygosity certainty – a fact this package's per-individual pedigree data frame cannot represent directly. It mirrors checkKinshipOverrides: it stop()s on structural or domain errors and returns the (id-coerced) table when the input is acceptable.

Usage

checkTwinRelations(ped, twinRelations)

Arguments

Details

code adopts kinship2's own literal twin-code labels – "MZ twin", "DZ twin", "UZ twin" – rather than a bare "zygosity" identifier, which would collide with this package's unrelated marker-genetics heterozygosity vocabulary (see markerObservedHeterozygosity). kinship2's own fourth, non-twin "spouse" code is out of scope here.

Validation reproduces kinship2's own relation acceptance rules, confirmed empirically against the installed kinship2 namespace: both ids must exist in ped and differ (all codes); an "MZ twin"/"DZ twin" pair must already share both sire and dam in ped; an "MZ twin" pair must additionally have matching sex; "UZ twin" has no such precondition.

Value

The validated twinRelations data.frame with id1 and id2 coerced to character.

Examples

ped <- data.frame(
  id = c("F1", "F2", "S1", "S2"),
  sire = c(NA, NA, "F1", "F1"),
  dam = c(NA, NA, "F2", "F2"),
  sex = c("M", "F", "F", "F"),
  stringsAsFactors = FALSE
)
twinRelations <- data.frame(
  id1 = "S1", id2 = "S2", code = "MZ twin", stringsAsFactors = FALSE
)
checkTwinRelations(ped, twinRelations)

Description

Combine two allele vectors by Mendelian sampling

Usage

chooseAlleles(a1, a2)

Arguments

Value

An integer vector with the result of sampling from a1 and a2 according to Mendelian inheritance.

Examples

chooseAlleles(0L:4L, 5L:9L)

Description

Part of Pedigree Curation

Usage

chooseDate(d1, d2, earlier = TRUE)

Arguments

Details

Given two dates, one is selected to be returned based on whether it occurred earlier or later than the other. NAs are ignored if possible.

Value

A single Date value, the chosen date, or NA where neither is provided. Vectors longer than one are not supported; use pmin or pmax, or mapply, for vectors.

Examples

library(nprcgenekeepr)
someDates <- lubridate::mdy(paste0(
  sample(1:12, 2, replace = TRUE), "-",
  sample(1:28, 2, replace = TRUE), "-",
  sample(seq(0, 15, by = 3), 2,
    replace = TRUE
  ) + 2000
))
someDates
chooseDate(someDates[1], someDates[2], earlier = TRUE)
chooseDate(someDates[1], someDates[2], earlier = FALSE)

Description

Computes per-individual genomic Runs of Homozygosity (ROH) segments and the genomic inbreeding coefficient F_ROH from a sequence-scale marker genotype matrix (issue #152 Slice 3, design decision D6) – a sequence-verified inbreeding estimate independent of (often incomplete) pedigree records, complementing rather than duplicating this package's existing pedigree-based kinship/founder metrics.

Usage

computeGenomicROH(genotypeMatrix, locusMetadata, minSnp = 50L, minBp = 1e+06)

Arguments

Details

Within each chromosome, loci are ordered by pos. A "run" is a maximal, gapless stretch of homozygous, non-missing genotyped loci – both a heterozygous call and a missing (NA) call end the current run. A run qualifies as an ROH segment only if it has at least minSnp loci AND spans at least minBp base pairs (the field-standard dual threshold, matching PLINK's --homozyg-snp/ --homozyg-kb, Purcell et al. 2007) – either condition alone is not sufficient. totalRohLength sums the qualifying segments' spans across all chromosomes; fRoh divides that sum by genomeLength, a single value shared across every individual: the sum, per chromosome, of (max(pos) - min(pos)) among the full-coverage loci in locusMetadata that are also columns of genotypeMatrix (Ceballos et al. 2018's $L_{autosome}$ convention). The denominator is fixed across individuals within one call, which avoids conflating an individual's own missingness with inbreeding, but it changes with the matrix's locus set: full-coverage loci of locusMetadata that are not columns of genotypeMatrix are left out of that length, with one warning naming them (at most 10 are named, then "and N more"), so fRoh is comparable across a cohort only when the same loci are used. A locus without a position never enters the length, so its absence is not reported.

Only loci with full locusMetadata coverage (both chrom and pos present, per checkLocusMetadata's own three-tier classification) can be placed in the ordered walk; a locus lacking full coverage, or entirely absent from locusMetadata, is excluded from both the walk and the genomeLength denominator, with a warning naming it – it is never treated as a run-breaking gap. When every chromosome has at most one full-coverage locus, genomeLength is 0 and fRoh is an undefined 0/0 ratio: NA with a warning, not NaN silently, mirroring markerFst's own undefined-ratio precedent.

Value

A per-individual dataframe: id, nSegments (count of qualifying ROH segments), totalRohLength (their summed bp span), and fRoh (the genomic inbreeding coefficient, or NA if genomeLength is 0).

References

Ceballos, F. C., Joshi, P. K., Clark, D. W., Ramsay, M., & Wilson, J. F. (2018). Runs of homozygosity: windows into population history and trait architecture. Nature Reviews Genetics, 19(4), 220-234. doi:10.1038/nrg.2017.109

Purcell, S., et al. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics, 81(3), 559-575. doi:10.1086/519795

See Also

buildMarkerGenotypeMatrix, checkLocusMetadata, checkSequenceGenotypeFile

Examples

library(nprcgenekeepr)
locusMetadata <- data.frame(
  locus = c("L1", "L2", "L3", "L4"),
  chrom = c("1", "1", "1", "1"),
  pos = c(0, 400000, 900000, 1400000),
  stringsAsFactors = FALSE
)
genotypeMatrix <- matrix(
  c("A/A", "A/A", "A/B", "A/A"),
  nrow = 1L, dimnames = list("Animal1", locusMetadata$locus)
)
computeGenomicROH(genotypeMatrix, locusMetadata, minSnp = 2L, minBp = 300000)

Description

Part of Pedigree Curation

Usage

convertAncestry(ancestry)

Arguments

Value

A factor vector of standardized designators specifying if an animal is a Chinese rhesus, Indian rhesus, Chinese-Indian hybrid rhesus, or Japanese macaque. Levels: CHINESE, INDIAN, HYBRID, JAPANESE, OTHER, UNKNOWN.

Examples

original <- c("china", "india", "hybridized", NA, "human", "gorilla")
convertAncestry(original)

Description

Part of Pedigree Curation

Usage

convertDate(ped, timeOrigin = as.Date("1970-01-01"), reportErrors = FALSE)

Arguments

Value

A dataframe with an updated table with date columns converted from character data type to Date data type. A date must be in the form YYYY-MM-DD (month and day may have one digit), or YYYYMMDD when no date in the column has a separator. A year before 1000 is invalid. A value that does not conform is an error (see reportErrors), not set to NA. NA values and blank strings are left as NA. A date column must be character, factor, logical, integer or Date; any other class stops with an error. Records marked "added" are not checked and are returned after the other records. With reportErrors = TRUE see that argument instead.

Examples

library(lubridate)
set_seed(10)
someBirthDates <- paste0(
  sample(seq(0, 15, by = 3), 10,
    replace = TRUE
  ) + 2000, "-",
  sample(1:12, 10, replace = TRUE), "-",
  sample(1:28, 10, replace = TRUE)
)
someBadBirthDates <- paste0(
  sample(1:12, 10, replace = TRUE), "-",
  sample(1:28, 10, replace = TRUE), "-",
  sample(seq(0, 15, by = 3), 10,
    replace = TRUE
  ) + 2000
)
someDeathDates <- sample(someBirthDates, length(someBirthDates),
  replace = FALSE
)
someDepartureDates <- sample(someBirthDates, length(someBirthDates),
  replace = FALSE
)
ped1 <- data.frame(
  birth = someBadBirthDates, death = someDeathDates,
  departure = someDepartureDates
)
someDates <- ymd(someBirthDates)
ped2 <- data.frame(
  birth = someDates, death = someDeathDates,
  departure = someDepartureDates
)
ped3 <- data.frame(
  birth = someBirthDates, death = someDeathDates,
  departure = someDepartureDates
)
someNADeathDates <- someDeathDates
someNADeathDates[c(1, 3, 5)] <- ""
someNABirthDates <- someDates
someNABirthDates[c(2, 4, 6)] <- NA
ped4 <- data.frame(
  birth = someNABirthDates, death = someNADeathDates,
  departure = someDepartureDates
)

## convertDate identifies bad dates
result <- tryCatch(
  {
    convertDate(ped1)
  },
  warning = function(w) {
    print("Warning in date")
  },
  error = function(e) {
    print("Error in date")
  }
)

## convertDate with error flag returns error list and not an error
convertDate(ped1, reportErrors = TRUE)

## convertDate recognizes good dates
all(is.Date(convertDate(ped2)$birth))
all(is.Date(convertDate(ped3)$birth))

## convertDate handles NA and empty character string values correctly
convertDate(ped4)

Description

Part of Pedigree Curation

Usage

convertFromCenter(fromCenter)

Arguments

Value

A logical vector specifying TRUE if an animal is from the center otherwise FALSE. An NA input stays NA. Any other value that is not recognized as a yes or a no (including numbers such as 1L and empty strings), or that matches both, stops with an error naming the offending rows.

Examples

original <- c(
  "y", "yes", "Y", "Yes", "YES", "n", "N", "No", "NO", "no",
  "t", "T", "True", "true", "TRUE", "f", "F", "false", "False",
  "FALSE"
)
convertFromCenter(original)

Description

Part of Relations

Usage

convertRelationships(kmat, ped, ids = NULL, updateProgress = NULL)

Arguments

Value

A dataframe with columns id1, id2, kinship, relation. It is a long-form table of pairwise kinships, with relationship categories included for each pair. IDs that are not in kmat are ignored. One ID gives that animal's own Self row, and no ID in kmat (or an empty ids) gives a table with no rows.

Examples

library(nprcgenekeepr)
ped <- nprcgenekeepr::smallPed
kmat <- kinship(ped$id, ped$sire, ped$dam, ped$gen, sparse = FALSE)
ids <- c("A", "B", "D", "E", "F", "G", "I", "J", "L", "M", "O", "P")
relIds <- convertRelationships(kmat, ped, ids)
rel <- convertRelationships(kmat, ped, updateProgress = function() {})
head(rel)
ped <- nprcgenekeepr::qcPed
bkmat <- kinship(ped$id, ped$sire, ped$dam, ped$gen,
  sparse = FALSE
)
relBIds <- convertRelationships(bkmat, ped, c("4LFS70", "DD1U77"))
relBIds

Description

Part of Pedigree Curation

Usage

convertSexCodes(sex, ignoreHerm = TRUE)

Arguments

Details

Standard sex codes are

• F – replacing "FEMALE" or "2"

• M – replacing "MALE" or "1"

• H – replacing "HERMAPHRODITE" or "4", if ignoreHerm == FALSE

• U – replacing "HERMAPHRODITE" or "4", if ignoreHerm == TRUE

• U – replacing "UNKNOWN" or "3"

• U – replacing a missing, blank or unrecognized value

Case and any spaces around a code are ignored, so " male " and "M " both become M.

Value

A single factor with levels F, M, H and U holding the standardized sex codes after transformation from non-standard codes. Level H is used only when ignoreHerm = FALSE.

Examples

library(nprcgenekeepr)
original <- c(
  "m", "male", "1", "MALE", "M", "F", "f", "female",
  "FemAle", "U", "Unknown", "H", "hermaphrodite",
  "U", "Unknown", "3", "4"
)
sexCodes <- convertSexCodes(original)
sexCodes

Description

Part of Pedigree Curation

Usage

convertStatusCodes(status)

Arguments

Details

Case is ignored, but spaces are not trimmed. The recognized codes are ALIVE, A, 1 for alive; DECEASED, DEAD, DIED, D, 2 for deceased; SHIPPED, SHIPED, SOLD, SALE, S, 3 for shipped; and UNKNOWN, U, 4 or NA for unknown.

Value

A factor vector of the standardized status codes with levels: ALIVE, DECEASED, SHIPPED, and UNKNOWN. A value that is not recognized becomes NA, not UNKNOWN.

Examples

library(nprcgenekeepr)
original <- c(
  "A", "alive", "Alive", "1", "S", "Sale", "sold", "shipped",
  "D", "d", "dead", "died", "deceased", "2",
  "shiped", "3", "U", "4", "unknown", NA,
  "Unknown", "U", "Unknown", "4"
)
## A value that is not recognized becomes NA
convertStatusCodes("hermaphrodite")
convertStatusCodes(original)

Description

Part of Pedigree Curation

Usage

correctParentSex(id, sire, dam, sex, recordStatus, reportErrors = FALSE)

Arguments

Details

Only true female-sires ("F") and male-dams ("M") are corrected (to "M" and "F" respectively). Parents recorded as hermaphrodite ("H") or unknown ("U") sex are left unchanged, consistent with reportErrors = TRUE mode, which does not flag them.

When reportErrors = TRUE, only records whose recordStatus is exactly "added" are left out of the report. A missing (NA), blank or unrecognized status is treated as an original animal, and a NULL recordStatus means no added records are known, so every animal is checked.

Value

When reportErrors = FALSE, a factor (or character vector) of corrected sex codes with levels "M", "F", "H", and "U" for the ids provided. When reportErrors = TRUE, a named list of error vectors with elements sireAndDam, femaleSires, and maleDams (each NULL when no such errors are found).

Examples

library(nprcgenekeepr)
pedOne <- data.frame(
  id = c("s1", "d1", "s2", "d2", "o1", "o2", "o3", "o4"),
  sire = c(NA, "s0", "s4", NA, "s1", "s1", "s2", "s2"),
  dam = c(NA, "d0", "d4", NA, "d1", "d2", "d2", "d2"),
  sex = c("F", "F", "M", "F", "F", "F", "F", "M"),
  recordStatus = rep("original", 8),
  stringsAsFactors = FALSE
)
pedTwo <- data.frame(
  id = c("s1", "d1", "s2", "d2", "o1", "o2", "o3", "o4"),
  sire = c(NA, "s0", "s4", NA, "s1", "s1", "s2", "s2"),
  dam = c("d0", "d0", "d4", NA, "d1", "d2", "d2", "d2"),
  sex = c("M", "M", "M", "F", "F", "F", "F", "M"),
  recordStatus = rep("original", 8),
  stringsAsFactors = FALSE
)
pedOneCorrected <- pedOne
pedOneCorrected$sex <- correctParentSex(
  pedOne$id, pedOne$sire, pedOne$dam,
  pedOne$sex, pedOne$recordStatus
)
pedOne[pedOne$sex != pedOneCorrected$sex, ]
pedOneCorrected[pedOne$sex != pedOneCorrected$sex, ]

pedTwoCorrected <- pedTwo
pedTwoCorrected$sex <- correctParentSex(
  pedTwo$id, pedTwo$sire, pedTwo$dam,
  pedTwo$sex, pedTwo$recordStatus
)
pedTwo[pedTwo$sex != pedTwoCorrected$sex, ]
pedTwoCorrected[pedTwo$sex != pedTwoCorrected$sex, ]

Description

Part of Relations

Usage

countFirstOrder(ped, ids = NULL)

Arguments

Details

Tallies the number of first-order relatives for each member of the provided pedigree. If 'ids' is provided, the analysis is restricted to only the specified subset.

Value

A dataframe with column id, parents, offspring, siblings, and total. A table of first-order relationship counts, broken down to indicate the number of parents, offspring, and siblings that are part of the subset under consideration. Siblings are animals with the same sire and the same dam, so half-siblings are not counted and an animal with an unknown sire or dam has 0 siblings.

Examples

library(nprcgenekeepr)
ped <- nprcgenekeepr::lacy1989Ped
ids <- c("B", "D", "E", "F", "G")
countIds <- countFirstOrder(ped, ids)
countIds
count <- countFirstOrder(ped, NULL)
count

Description

Count kinship-value occurrences across simulated pedigrees

Usage

countKinshipValues(kinshipValues, accummulatedKValueCounts = NULL)

Arguments

Value

A list of three lists named kIds (kinship IDs), kValues (kinship values), and kCounts (kinship counts).

Examples

library(nprcgenekeepr)
ped <- nprcgenekeepr::smallPed
simParent_1 <- list(
  id = "A",
  sires = c("s1_1", "s1_2", "s1_3"),
  dams = c("d1_1", "d1_2", "d1_3", "d1_4")
)
simParent_2 <- list(
  id = "B",
  sires = c("s1_1", "s1_2", "s1_3"),
  dams = c("d1_1", "d1_2", "d1_3", "d1_4")
)
simParent_3 <- list(
  id = "E",
  sires = c("A", "C", "s1_1"),
  dams = c("d3_1", "B")
)
simParent_4 <- list(
  id = "J",
  sires = c("A", "C", "s1_1"),
  dams = c("d3_1", "B")
)
simParent_5 <- list(
  id = "K",
  sires = c("A", "C", "s1_1"),
  dams = c("d3_1", "B")
)
simParent_6 <- list(
  id = "N",
  sires = c("A", "C", "s1_1"),
  dams = c("d3_1", "B")
)
allSimParents <- list(
  simParent_1, simParent_2, simParent_3,
  simParent_4, simParent_5, simParent_6
)

extractKValue <- function(kValue, id1, id2, simulation) {
  kValue[
    kValue$id_1 == id1 & kValue$id_2 == id2,
    paste0("sim_", simulation)
  ]
}

n <- 10
simKinships <- createSimKinships(ped, allSimParents,
  pop = ped$id, n = n
)
kValues <- kinshipMatricesToKValues(simKinships)
extractKValue(kValues, id1 = "A", id2 = "F", simulation = 1:n)
counts <- countKinshipValues(kValues)

# A second, independent set of simulations is added to the first counts
n <- 10
simKinships <- createSimKinships(ped, allSimParents, pop = ped$id, n = n)
kValues <- kinshipMatricesToKValues(simKinships)
extractKValue(kValues, id1 = "A", id2 = "F", simulation = 1:n)
accummulatedCounts <- countKinshipValues(kValues, counts)

Description

Part of Pedigree Sampling From PedigreeSampling.R 2016-01-28

Usage

countLoops(loops, ptree)

Arguments

Details

Contains functions to build pedigrees from sub-samples of genotyped individuals.

The goal of sampling is to reduce the number of inbreeding loops in the resulting pedigree, and thus, reduce the amount of time required to perform calculations with SIMWALK2 or similar programs.

Uses the loops data structure and the list of all ancestors for each individual to calculate the number of loops for each individual.

Value

A list indexed with each ID in the pedigree tree (ptree) containing the number of loops for each individual.

Examples

library(nprcgenekeepr)
examplePedigree <- nprcgenekeepr::examplePedigree
exampleTree <- createPedTree(examplePedigree)
exampleLoops <- findLoops(exampleTree)
## You can count how many animals are in loops with the following code.
length(exampleLoops[exampleLoops == TRUE])
## You can count how many loops you have with the following code.
nLoops <- countLoops(exampleLoops, exampleTree)
sum(unlist(nLoops[nLoops > 0]))
## You can list the first 10 sets of ids, sires and dams in loops with
## the following line of code:
examplePedigree[exampleLoops == TRUE, c("id", "sire", "dam")][1:10, ]

Description

Create an Excel workbook with worksheets

Usage

create_wkbk(file, df_list, sheetnames, replace = FALSE)