pedtools (version 2.8.0)

maskPed: Mask and unmask pedigree datasets

Description

The maskPed() function replaces the individual IDs, marker names and allele names with generic labels, and randomly changes their internal order. For markers with stepwise mutation models, the allelic ladder is simply translated to start at 1, thereby preserving the intra-allelic differences.

Usage

maskPed(
  x,
  ids = NULL,
  markerNames = NULL,
  markerShuffle = TRUE,
  alleleLabels = NULL,
  alleleShuffle = TRUE,
  seed = NULL
)
unmaskPed(x, keys)

Value

An object similar to x but with replaced ID labels, marker names and allele labels.

Arguments

x

A ped object or a list of such.

ids

(Optional) A named character with the new IDs, written as c(old = new, ...). By default: 1, 2, ... .

markerNames

(Optional) A named character with the new marker names (and order), written as c(old = new, ...). By default: M1, M2, ... .

markerShuffle

A logical: Randomly reorder the markers? (Default: TRUE)

alleleLabels

(Optional) A list of character vectors. The list names should be the original marker names. Each vector gives the new allele labels, as c(old = new, ...). By default, each marker gets alleles 1, 2, ... .

alleleShuffle

A logical: Randomly reorder the alleles? (Default: TRUE)

seed

An optional seed for the random number generator.

keys

A list with entries ids, markerNames, alleleLabels.

Details

Note that in order to preserve likelihoods, the allele frequencies are not modified. Thus, if the data uses a publicly available frequency databases, the result cannot be considered to be fully anonymised, since one could (at least in theory) deduce the original marker names and alleles from the frequencies.)

Examples

x = nuclearPed(father = "fa", mother = "mo", children = "ch") |>
  addMarker(name = "myMarker", ch = "b/c", afreq = c(a=0.2, b=0.3, c=0.5)) |>
  setMutmod(model = "proportional", rate = 0.01)

# Mask
y = maskPed(x, seed = 1729)

# Unmask
z = unmaskPed(y$maskedPed, keys = y$keys)
stopifnot(identical(x, z))

# With stepwise model
x2 = x |>
  addMarker(name = "mySTR", ch = "7.2/8.2",
            alleles = c("7", "7.2", "8", "8.2")) |>
  setMutmod(marker = 2, model = "stepwise", rate = 0.1, rate2 = 1e-6,
            range = 0.1)

y2 = maskPed(x2, seed = 1729)

z2 = unmaskPed(y2$maskedPed, keys = y2$keys)

stopifnot(identical(x2, z2))

# Check likelihoods with pedprobr:
# stopifnot(setequal(likelihood(x2), likelihood(y2$maskedPed)))