Title: Visualize, Summarize and Simulate Data from Pedigrees
Version: 0.4.2
Description: Sensitivity and power analysis, for calculating statistics describing pedigrees from wild populations, and for visualizing pedigrees.
URL: https://juliengamartin.github.io/pedtricks/
BugReports: https://github.com/JulienGAMartin/pedtricks/issues
Language: en-US
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.2
LazyData: true
LazyLoad: yes
Depends: R (≥ 4.0)
Imports: ggplot2, dplyr, kinship2, tidyr, Matrix, mvtnorm, MCMCglmm, nadiv, genetics, methods, igraph
NeedsCompilation: no
Packaged: 2024-09-04 00:28:02 UTC; julien
Author: Julien Martin ORCID iD [aut, cre], Matthew Wolak ORCID iD [aut], Susan Johnston ORCID iD [aut], Michael Morrissey ORCID iD [aut]
Maintainer: Julien Martin <julien.martin@uottawa.ca>
Repository: CRAN
Date/Publication: 2024-09-09 08:50:06 UTC

pedtricks: Visualize, Summarize and Simulate Data from Pedigrees

Description

Sensitivity and power analysis, for calculating statistics describing pedigrees from wild populations, and for visualizing pedigrees.

Details

For backward compatibility with code using pedantics older names are still usable but not recommended.

Author(s)

Maintainer: Julien Martin julien.martin@uottawa.ca (ORCID)

Authors:

See Also

Useful links:

Seychelles Warbler Genotypes

Description

Genotype data collected by David Richardson from Cousin Island in 1999.

Usage

WarblerG

Format

An object of class data.frame with 307 rows and 29 columns.

References

Richardson et.al. (2001) Molecular Ecology 10 2263-2273 Hadfield J.D. et al (2006) Molecular Ecology 15 3715-31

Converts a pedigree with individuals specified as factors to a numeric pedigree

Description

Some internal pedtricks modules require that pedigrees be specified only by numerical values, or including numerical values for missing data. This function provides the conversion to numeric but also back to factors if needed

Usage

convert_ped(type = "numeric", id, sire, dam, missingVal = NA, key = NULL)

Arguments

type

define how to convert the pedigree so "numeric" or "factor"

id

Individual identifiers - pass using as.character()

sire

Sire codes - pass using as.character()

dam

Dam codes - pass using as.character()

missingVal

the indicator that should be substituted for missing values

key

A dataframe, as produced by convert_ped, specifying factor codes for numeric values in id, sire, and dam

Value

numericPedigree

The factor pedigree in numeric form

idKey

A key to facilitate conversion back to the original identifiers

Examples

pedigree <- as.data.frame(matrix(c(
  "m1",   NA,     NA,
  "m2",   NA,     NA,
  "m3",   NA,     NA,
  "d4",   NA,     NA,
  "d5",   NA,     NA,
  "o6",   "m1",   "d4",
  "o7",   "m1",   "d4",
  "o8",   "m1",   "d4",
  "o9",   "m1",   "d4",
  "o10",  "m2",   "d5",
  "o11",  "m2",   "d5",
  "o12",  "m2",   "d5",
  "o13",  "m2",   "d5",
  "o14",  "m3",   "d5",
  "o15",  "m3",   "d5",
  "o16",  "m3",   "d5",
  "o17",  "m3",   "d5"
), 17, 3, byrow = TRUE))
names(pedigree) <- c("id", "dam", "sire")
for (x in 1:3) pedigree[, x] <- as.factor(pedigree[, x])

## make the test pedigree numeric with NAs denoted by -1
convert_ped(
  type = "numeric",
  id = as.character(pedigree[, 1]),
  dam = as.character(pedigree[, 2]),
  sire = as.character(pedigree[, 3]),
  missingVal = -1
)

Produce a graphical representation of a pedigree

Description

Plots a pedigree, with options specific to considerations for pedigrees used to for quantitative genetic inference in natural populations. Pedigrees containing only those individuals that are informative with respect to (genetic) variation in an arbitrary trait can be plotted, potentially overlain on a complete pedigree. Functions also exist to plot various types of pedigree links associated with focal individuals.

Usage

draw_ped(
  Ped,
  cohorts = NULL,
  sex = NULL,
  dat = NULL,
  dots = "n",
  plotfull = "y",
  writeCohortLabels = "n",
  links = "all",
  sexInd = c(0, 1),
  dotSize = 0.001,
  dataDots = "n",
  dataDots.cex = 2,
  cohortLabs.cex = 1,
  retain = "informative",
  focal = NULL,
  sexColours = c("red", "blue"),
  ...
)

Arguments

Ped

An ordered pedigree with 3 columns: id, dam, sire

cohorts

An optional numeric vector of the same length as the pedigree designating, for example cohort affinities or birth years

sex

An optional numeric vector of the same length as the pedigree containing the sexes (may be unknown) of all individuals with entries in the pedigree. Defaults (modifiable with sexInd) are 0=male and 1=female

dat

An optional vector or data frame containing indicators of data availability. If dat contains only ones and zeros, then any individual with any entry of one will be considered as having data records. If data contains values other than ones and zeros, individuals in the pedigree with rows in data that contain at least one available record, i.e., one data record is not NA, will be treated as having data.

dots

If 'y', then a dot will be printed representing each individual in the pedigree. If sexes are available, dots will be colour coded by sex.

plotfull

To be used when dat is supplied. If 'y' (the default), individuals in the pedigree that are uninformative with respect to the available data have their pedigree links plotted in gray.

writeCohortLabels

To be used when cohorts is used. Will plot the cohort values on the left hand side of the pedigree image.

links

Default is 'all', other values are 'mums' to print only maternal pedigree links and 'dads' to print only paternal pedigree links.

sexInd

To be used with if sex is supplied and if the vector of sex specifiers differs from the default.

dotSize

Set the dot size bigger or smaller

dataDots

Will print dots over the dots denoting individuals, but denoting individuals with available data as indicated by dat.

dataDots.cex

controls the size of dataDots relative to dots.

cohortLabs.cex

controls the size of cohort labels.

retain

When those pedigree links only informative relative to phenotypic data availability are to be plotted, this controls whether or not a pruned pedigree based on phenotypic data is plotted (if set to "pruned"), or whether strictly only those informative pedigree links are plotted (the default)

focal

An optional list containing the id of an individual and the kinds of relatives of the focal individual to which to plot pedigree links. Available types are 'offspring','descendants','parents',,ancestors', and 'kin'.

sexColours

The colours that will be used to draw points and or lines associated with males and females.

...

Additional graphical parameters.

Value

output a plot of the pedigree, and does not return a value

Author(s)

Michael Morrissey michael.morrissey@st-andrews.ac.uk

See Also

fix_ped to prepare pedigrees that may not explicitly contain records for all individuals (i.e., where founding individuals may only appear in the dam or sire column).)

Examples

data(gryphons)
pedigree <- fix_ped(gryphons[, 1:3])

## draw the gryphon pedigree by pedigree depth
draw_ped(pedigree)

## draw the gryphon pedigree by cohort
draw_ped(pedigree,
  cohorts = gryphons$cohort, writeCohortLabels = "y",
  cohortLabs.cex = 1
)

## draw the gryphon pedigree by cohort with only maternal links
draw_ped(pedigree, cohorts = gryphons$cohort, links = "mums")

## draw the gryphon pedigree by cohort with colour only for those
## indiduals that are informative relative to the quantitative
## genetics of a hypothetical trait only measured for individuals
## in the last two cohorts, emphasize the phenotyped individuals
## with large black dots, and all other individuals with dots
## colour coded by sex:
dataAvailability <- (gryphons$cohort >= (max(gryphons$cohort) - 1)) + 0

draw_ped(pedigree,
  cohorts = gryphons$cohort, sex = gryphons$sex,
  dots = "y", dat = dataAvailability, writeCohortLabels = "y", dataDots = "y"
)

Produce a graphical representation of the relatedness matrix of a pedigree

Description

Creates the object needed to plot a pedigree's numerator relatedness matrix given a few different choices for ordering. The resulting image for a pedigree of size n can be visualized as a n x n grid of colored squares based on values of the numerator relatedness matrix.

Usage

draw_pedA(
  pedigree,
  order = c("original", "generation", "user"),
  grp = NULL,
  ...
)

Arguments

pedigree

A data.frame of a pedigree with 3 columns: id, dam, sire

order

A character expression for how the pedigree should be ordered for visualization. See Details.

grp

A character expression for the column name in pedigree indicating how to order the pedigree for visualization.

...

Additional plotting arguments passed to image.

Value

A list of class “trellis”.

Author(s)

mew0099@auburn.edu

Examples


data(gryphons)
pedigree <- fix_ped(gryphons[, 1:3])

## draw the gryphon pedigree
draw_pedA(pedigree, order = "original")

## draw the gryphon pedigree by function assigned generation
(Agen <- draw_pedA(pedigree, order = "generation"))



## draw the gryphon pedigree by cohort in the dataset
## add cohort back from original data
pedigree$cohort <- NA
pedigree$cohort[match(gryphons$id, pedigree[, 1])] <- gryphons$cohort
(Achrt <- draw_pedA(pedigree, order = "user", grp = "cohort"))

## show two images of the same pedigree in different orders
### (i.e., plotting multiple trellis objects in the same figure)
plot(Agen,
  position = c(xmin = 0, ymin = 0, xmax = 0.45, ymax = 1),
  more = TRUE
)
plot(Achrt, position = c(xmin = 0.55, ymin = 0, xmax = 1, ymax = 1))

Allele Frequencies

Description

Extracts allele frequencies from genotype data

Usage

extractA(G, marker.type = "MSW")

Arguments

G

data frame or list of genotype objects

marker.type

"MSW" or "MSC" for co-dominant markers with Wang's (2004) model of genotyping error or CERVUS's model of genotyping error (Marshall, 1998) or "AFLP" for dominant markers.

Value

list of allele frequencies at each loci

Author(s)

Jarrod Hadfield j.hadfield@ed.ac.uk

Examples


data(WarblerG)

A <- extractA(WarblerG)
A[[1]]

Manipulating pedigrees to prepare them for requirements of subsequent analyses Prepares a pedigree to conform with requirements of many softwares used in quantitative genetic analysis, as well as for many of the functions in pedtricks.

Description

Manipulating pedigrees to prepare them for requirements of subsequent analyses Prepares a pedigree to conform with requirements of many softwares used in quantitative genetic analysis, as well as for many of the functions in pedtricks.

Usage

fix_ped(ped, dat = NULL)

Arguments

ped

An ordered pedigree with 3 columns: id, dam, sire

dat

An optional data frame, the same length as the pedigree

Value

Returns a pedigree in which all individuals that exist in the dam and sire columns are represented by their own record lines, occurring before the records of their first offspring. If data are supplied, then fix_ped will return a dataframe, the first three columns are the 'fixed' pedigree, and the following columns of which contain appropriately reordered data.

Examples

##  a valid pedigree, i.e., no loops, no bisexuality, etc.,
## but where not all parents have a record line, and where
## parents do not necessarily occur before their offspring:
pedigree <- as.data.frame(matrix(c(
  10, 1, 2,
  11, 1, 2,
  12, 1, 3,
  13, 1, 3,
  14, 4, 5,
  15, 6, 7,
  4, NA, NA,
  5, NA, NA,
  6, NA, NA,
  7, NA, NA
), 10, 3, byrow = TRUE))
names(pedigree) <- c("id", "dam", "sire")
pedigree
fixed_pedigree <- fix_ped(ped = pedigree)
fixed_pedigree

A function to simulate QTL and/or SNP data.

Description

Simulates a chromosome of arbitrary length with arbitrary numbers, types, and spacings of genetic loci over arbitrary pedigrees.

Usage

genome_sim(
  pedigree,
  founders = NULL,
  positions = NULL,
  initHe = NULL,
  mutationType = NULL,
  mutationRate = NULL,
  phenotyped = NULL,
  founderHaplotypes = NULL,
  genotyped = NULL,
  returnG = "n",
  initFreqs = NULL
)

Arguments

pedigree

A pedigree

founders

A vector of indicator variables denoting founder status (1=founder, 0=non-founder)

positions

Genome locations in cM for markers

initHe

Initial levels of expected heterozygosity

mutationType

A vector of locus types - see details

mutationRate

A vector of mutation rates

phenotyped

A vector of IDs of those individuals for which to return phenotypic data

founderHaplotypes

A matrix or dataframe containing founder haplotypes

genotyped

A vector of IDs of those individuals for which to return genotypic data

returnG

If 'y' then genotypic data for all loci (including cIAM loci) will be returned.

initFreqs

A list of allele frequencies for all loci. If initFreqs is specified, it will override information from initHe. extractA from package MasterBayes can be used to obtain obtain initFreqs form a sample of genotypes. For cIAM loci, allele names in initFreqs should be allelic substitution effects.

Details

Valid mutation types are ⁠Micro', ⁠Dom', ⁠dIAM' and ⁠cIAM', for microsatellite, dominant (AFLP), discrete infinite alleles mutation model loci (SNPs), and continuous infinite alleles mutation model loci (polymorphisms effecting phenotypic variation). cIAM loci have mutational allelic substitution effects taken drawn from a normal distribution with mean 0 and variance 1.

Value

Phenotypes

A vector of phenotypes. Calculated as the sum of all allelic effects. Scaling is currently left to be done post-hoc.

MarkerData

A vector of marker genotypes, i.e. alleles at all loci except those designated ‘cIAM’

See Also

phen_sim, micro_sim

Examples

testData <- as.data.frame(matrix(
  c(
    1,      NA,     NA,     1,      1,      1,      2,      2,
    2,      NA,     NA,     1,      1,      1,      2,      2,
    3,      NA,     NA,     1,      1,      1,      2,      2,
    4,      NA,     NA,     1,      0,      1,      2,      2,
    5,      NA,     NA,     1,      0,      1,      2,      2,
    6,      1,      4,      0,      -1,     2,      3,      3,
    7,      1,      4,      0,      -1,     2,      3,      3,
    8,      1,      4,      0,      -1,     2,      3,      3,
    9,      1,      4,      0,      -1,     2,      3,      3,
    10,     2,      5,      0,      -1,     2,      3,      3,
    11,     2,      5,      0,      -1,     2,      3,      3,
    12,     2,      5,      0,      -1,     2,      3,      3,
    13,     2,      5,      0,      -1,     2,      3,      3,
    14,     3,      5,      0,      -1,     2,      3,      3,
    15,     3,      5,      0,      -1,     2,      3,      3,
    16,     3,      5,      0,      -1,     2,      3,      3,
    17,     3,      5,      0,      -1,     2,      3,      3
  ),
  17, 8,
  byrow = TRUE
))

names(testData) <- c(
  "id", "dam", "sire", "founder", "sex",
  "cohort", "first", "last"
)
pedigree <- as.data.frame(cbind(
  testData$id, testData$dam,
  testData$sire
))
for (x in 1:3) pedigree[, x] <- as.factor(pedigree[, x])
names(pedigree) <- c("id", "dam", "sire")
pedigree

## make up some microsatellite and gene allele frquencies:
sampleGenotypes <- as.data.frame(matrix(c(
  1, 2, -1.32, 0.21, 2, 1, 0.21, 0.21
), 2, 4, byrow = TRUE))
testFreqs <- extractA(sampleGenotypes)

## note that alleles at the gene locus are given as their
## allelic substitution effects:
testFreqs

## simulate data for these indivdiuals based on a single QTL
## with two equally alleles with balanced frequencies in the
## founders, linked (2 cM) to a highly  polymorphic microsatellite:
genome_sim(
  pedigree = pedigree, founders = testData$founder, positions = c(0, 2),
  mutationType = c("Micro", "cIAM"), mutationRate = c(0, 0),
  initFreqs = testFreqs, returnG = "y"
)
## since we specified returnG='y', we can check that
## the phenotypes add up to the
## allelic substitution effects for the second locus.

ggpedigree: Plotting tool for simple and complex pedigrees.

Description

This function plots simple and complex pedigrees, with options specific to the types of pedigrees used for quantitative genetic inference in natural populations. This function is flexible to missing parents and can be customized to visualise specific cohorts, sexes, and/or phenotype availability. Pedigree layout is optimized using a Sugiyama algorithm. For simpler pedigrees, visualisation may be improved by specifying spread_x_coordinates = FALSE.

Usage

ggpedigree(
  .data,
  ids,
  mothers,
  fathers,
  cohort,
  sex,
  pheno,
  sex_code = NULL,
  id_labels = FALSE,
  remove_singletons = TRUE,
  plot_unknown_cohort = FALSE,
  spread_x_coordinates = TRUE,
  print_cohort_labels = TRUE,
  return_plot_tables = FALSE,
  line_col_mother = "#E41A1C",
  line_col_father = "#377EB8",
  line_col_no_pheno = "#aaaaaa",
  line_alpha = 0.3,
  point_size = 1,
  point_colour = "black",
  point_alpha = 1
)

Arguments

.data

a data frame object with all the pedigree information

ids

a column of .data of individual identifiers

mothers

A column of .data of mothers corresponding to ids. Missing values are 0 or NA.

fathers

A column of .data of fathers corresponding to ids. Missing values are 0 or NA.

cohort

integer. Default NULL. A optional column of .data assigning a cohort to each id. If NULL, then kinship2::kindepth is used to assign cohorts to ids.

sex

integer or character. Default NULL. An optional column of .data assigning a sex to each id. When using this option, sex_code must be specified. Any values not matching values in sex_code will be treated as unknown sex.

pheno

integer or character. Default NULL. An optional column of .data assigning a phenotype to each id. Links originating from parents that have NA values for this argument will be plotted with a grey line, unless otherwise specified in line_col_no_pheno.

sex_code

Default NULL. A vector of length 2, indicating the value used in sex for females and males respectively. Females are plotted as circles, males as squares, and unknown values as triangles.

id_labels

logical. Default FALSE. Print the ids on the pedigree plot.

remove_singletons

logical. Default TRUE. Remove ids with no relatives i.e., no offspring or parents assigned.

plot_unknown_cohort

logical. Default FALSE. Plots ids of unknown cohorts. These are plotted in an "Unknown" cohort at the top of the pedigree. Be aware that any mothers and fathers of these individuals will be plotted below them.

spread_x_coordinates

logical. Default TRUE. Evenly spreads the x-axis (horizontal) distribution of points within each cohort. If FALSE, this will plot the direct outcome of igraph::layout_with_sugiyama; the FALSE option is only recommended for small pedigrees and/or less connected pedigrees.

print_cohort_labels

logical. Default TRUE. Prints cohort levels on the left hand side of plot.

return_plot_tables

logical. Default FALSE. Returns an object with the line and point data used for the plot, but the plot will not be generated

line_col_mother

Default = "#E41A1C". Line colour for maternal links.

line_col_father

Default = "#377EB8". Line colour for paternal links.

line_col_no_pheno

Default = "#aaaaaa". Line colour for parents with NA values in pheno.

line_alpha

Default = 0.3. Line alpha (transparency) value for maternal and paternal links.

point_size

Default = 1. Point size for ids.

point_colour

Default = "black". Point colour for ids.

point_alpha

Default = 1. Point alpha for ids.

Value

output a ggplot object or a list of tables if return_plot_tables = TRUE

Examples

data(gryphons)
pedigree <- fix_ped(gryphons[, 1:3])

## draw the gryphon pedigree by pedigree depth
ggpedigree(pedigree)


# specifying the column names for id, mother and father
ggpedigree(pedigree, id, dam, sire)

# with cohort and sex
ggpedigree(gryphons, cohort = cohort, sex = sex, sex_code = c(1, 0))

#' with cohort, sex, and pheno
gryphons$pheno <- 1
gryphons$pheno[sample(length(gryphons$pheno), 1000)] <- NA
ggpedigree(gryphons, cohort = cohort, sex = sex, sex_code = c(1, 0), pheno = pheno)

Example dataset for pedtricks examples and tutorial

Description

This contains pedigree and life history data of a fictional population. The data are relevant to power and sensitivity analyses for quantitative genetic studies of natural populations.

Usage

gryphons

Format

An object of class data.frame with 4918 rows and 9 columns.

Details

id

what

dam

what

sire

what

sex

what

cohort

what

fatherErrorProb

what

fatherSampledProb

what

firstReproCohort

what

lastReproCohort

what

Deprecated functions from package pedantics.

Description

The functions listed below are deprecated and are available for backward compatibility only. They might be defunct in a future version When possible, alternative functions with similar functionality are also mentioned.

Usage

makemakePedigreeFactor(id, sire, dam, key = NULL)

makePedigreeNumeric(id, sire, dam, missingVal = NA)

drawPedigree(
  Ped,
  cohorts = NULL,
  sex = NULL,
  dat = NULL,
  dots = "n",
  plotfull = "y",
  writeCohortLabels = "n",
  links = "all",
  sexInd = c(0, 1),
  dotSize = 0.001,
  dataDots = "n",
  dataDots.cex = 2,
  cohortLabs.cex = 1,
  retain = "informative",
  focal = NULL,
  sexColours = c("red", "blue"),
  ...
)

fixPedigree(ped, dat = NULL)

genomesim(
  pedigree,
  founders = NULL,
  positions = NULL,
  initHe = NULL,
  mutationType = NULL,
  mutationRate = NULL,
  phenotyped = NULL,
  founderHaplotypes = NULL,
  genotyped = NULL,
  returnG = "n",
  initFreqs = NULL
)

microsim(
  pedigree,
  genFreqs = NULL,
  genotypesSample = NULL,
  knownGenotypes = NULL,
  records = NULL,
  eRate1 = 0,
  eRate2 = 0,
  eRate3 = 0
)

pedigreeStat()

pedStatSummary()

phensim(
  pedigree,
  traits = 1,
  randomA = NULL,
  randomE = NULL,
  parentalA = NULL,
  parentalE = NULL,
  sampled = NULL,
  records = NULL,
  returnAllEffects = FALSE
)

Value

dependant on the deprecated function see pedantics help for details

makePedigreeFactor

the function is now a special case of the convert_ped function when type is factor

makePedigreeNumeric

the function is now a special case of the convert_ped function when type is numeric

drawPedigree

the function has just been renamed with no other changes for the moment, but will soon be replace by "ggpedigree" function

fixPedigree

the function has just been renamed with no other changes for the moment

genomesim

the function has just been renamed with no other changes for the moment

microsim

the function has just been renamed with no other changes for the moment

pedigreeStats

the function has been simplified but only the functionality are still available via ped_stats and its summary and plot methods

pedStatSummary

the function has been simplified but only the functionality are still available via ped_stats and its summary and plot methods

phensim

the function has just been renamed with no other changes for the moment

Simulates microsatellite data across a pedigree.

Description

Uses a pedigree with parents identified for all non-founding individuals and simulates microsatellite genotypes

Usage

micro_sim(
  pedigree,
  genFreqs = NULL,
  genotypesSample = NULL,
  knownGenotypes = NULL,
  records = NULL,
  eRate1 = 0,
  eRate2 = 0,
  eRate3 = 0
)

Arguments

pedigree

A pedigree

genFreqs

(optional) A list of allele frequencies, can be produced with extractA

genotypesSample

(required if genFreqs is not supplied) a sample of genotypes from which to estimate population allele frequencies

knownGenotypes

(not yet implemented) a data frame of genotypes for (potentially a subset) of founder individuals

records

Record availability, see details.

eRate1

The rate of genotypic substitution errors, i.e., when a true genotype at a given locus is replaced by a pair of alleles selected at random based on the population allele frequencies

eRate2

The rate of allelic substitution errors, i.e. when an allele is erroneously replaced at a given locus by an allele chosen at random based on the population allele frequencies

eRate3

The rate of large allele dropouts, simulated by setting the value of the larger allele at a locus to the value of the smaller allele

Details

Error rates and data availability rates can be specified as either (1) single values to be applied to all individuals and all loci, (2) as a vector the same length as the number of loci, representing locus-specific rates to be applied uniformly to all individuals, or (3) as data frames with rows for each individual and columns for each locus. In the third option, observed patterns of data availability can be simulated by supplying 0s and 1s for missing and available individual genotypes, respectively.

Value

trueGenotypes

A data frame of true genotypes

observedGenotypes

A data frame of plausible observed genotypes, given specified patterns of missingness and errors.

See Also

phen_sim, genome_sim

Examples


pedigree <- as.data.frame(matrix(c(
  "m1",   NA,     NA,
  "m2",   NA,     NA,
  "m3",   NA,     NA,
  "d4",   NA,     NA,
  "d5",   NA,     NA,
  "o6",   "m1",   "d4",
  "o7",   "m1",   "d4",
  "o8",   "m1",   "d4",
  "o9",   "m1",   "d4",
  "o10",  "m2",   "d5",
  "o11",  "m2",   "d5",
  "o12",  "m2",   "d5",
  "o13",  "m2",   "d5",
  "o14",  "m3",   "d5",
  "o15",  "m3",   "d5",
  "o16",  "m3",   "d5",
  "o17",  "m3",   "d5"
), 17, 3, byrow = TRUE))
names(pedigree) <- c("id", "dam", "sire")
for (x in 1:3) pedigree[, x] <- as.factor(pedigree[, x])

## some sample genotypes, very simple, two markers with He = 0.5
sampleGenotypes <- as.data.frame(matrix(c(
  1, 2, 1, 2, 2, 1, 2, 1
), 2, 4, byrow = TRUE))
## locus names
names(sampleGenotypes) <- c("loc1a", "loc1b", "loc2a", "loc2b")

## simulate some genotypes
micro_sim(pedigree = pedigree, genotypesSample = sampleGenotypes)

Calculates a range of statistics of pedigrees

Description

Statistics are those that will hopefully be useful for describing pedigrees to be used in quantitative genetic analyses of natural populations. This module will be most useful when cohort affinities for all individuals can be provided. All outputs are produced in a numerical form as well as in graphical summaries.

Usage

ped_stats(
  Ped,
  cohorts = NULL,
  dat = NULL,
  retain = "informative",
  includeA = TRUE,
  lowMem = FALSE
)

Arguments

Ped

A pedigree

cohorts

(Optional) Cohort affinities for members of the pedigree

dat

(Optional) Available data based upon which the pedigree can be pruned for just informative individuals

retain

The default value ('informative') results in pedigree being pruned to only those individuals who's records contribute to estimation of quantitative genetic parameters with respect to the available data specified in dat. Otherwise, specifying a value of 'ancestors' will result in the inclusion of all ancestors of phenotyped individuals.

includeA

If TRUE, additive genetic relatedness matrix is returned.

lowMem

If TRUE, then stats based on calculation of A are not performed.

Value

totalMaternities

Total number of maternities defined by the pedigree.

totalPaternities

Total number of paternities defined by the pedigree.

totalFullSibs

Total number of pair-wise full sib relationships defined by the pedigree.

totalMaternalSibs

Total number of pair-wise maternal sib relationships defined by the pedigree. To get the number of maternal half sibs, subtract totalFullSibs.

totalPaternalSibs

Total number of pair-wise paternal sib relationships defined by the pedigree. To get the number of paternal half sibs, subtract totalFullSibs.

totalMaternalGrandmothers

Total number of maternal grandmothers defined by the pedigree.

totalMaternalGrandfathers

Total number of maternal grandfathers defined by the pedigree.

totalPaternalGrandmothers

Total number of paternal grandmothers defined by the pedigree.

totalPaternalGrandfathers

Total number of paternal grandfathers defined by the pedigree.

pedigreeDepth

The pedigree depth, i.e. maximum number of ancestral generations, for each individual.

inbreedingCoefficients

Individual inbreeding coefficients

maternalSibships

Sibship size of each individual appearing the the dam column of the pedigree.

paternalSibships

Sibship size of each individual appearing the the sire column of the pedigree.

cumulativeRelatedness

Proportion of pair-wise relatedness values less than values ranging from 0 to 1.

relatednessCategories

Discretized distribution of relatedness.

analyzedPedigree

Returns the pedigree.

sampleSizesByCohort

(Optional) Number of individuals belonging to each cohort.

maternitiesByCohort

(Optional) Number of assigned maternities by offspring cohort.

paternitiesByCohort

(Optional) Number of assigned paternities by offspring cohort.

fullSibsByCohort

(Optional) Number of pair-wise full sib relationships by cohort - note the sum of these need not be equal to totalFullSibs in pedigrees of long-lived organisms.

maternalSibsByCohort

(Optional) Number of pair-wise maternal sib relationships by cohort - note the sum of these need not be equal to totalMaternalSibs in pedigrees of long-lived organisms.

paternalSibsByCohort

(Optional) Number of pair-wise paternal sib relationships by cohort - note the sum of these need not be equal to totalPaternalSibs in pedigrees of long-lived organisms.

maternalGrandmothersByCohort

(Optional) Numbers of maternal grandmother assignments by offspring cohort.

maternalGrandfathersByCohort

(Optional) Numbers of maternal grandmother assignments by offspring cohort.

paternalGrandmothersByCohort

(Optional) Numbers of paternal grandfather assignments by offspring cohort.

paternalGrandfathersByCohort

(Optional) Numbers of paternal grandfather assignments by offspring cohort.

cumulativePedigreeDepth

(Optional) Distributions of pedigree depth by cohort.

meanRelatednessAmongCohorts

(Optional) Mean relatedness among cohorts.

cohorts

(Optional) Returns cohort designations.

Graphical summaries of a number of these summary statistics are printed to the console when graphicalReports=='y'.

Examples


data(gryphons)
pedigree <- gryphons[, 1:3]

gryphons_ped_stats <- ped_stats(pedigree,
  cohorts = gryphons$cohort
)

gryphons_ped_stats$totalMaternities
gryphons_ped_stats$paternitiesByCohort

summary(gryphons_ped_stats)

plot(gryphons_ped_stats)

Defunct functions from package pedantics.

Description

The functions listed below have not been transferred from pedantics to pedtricks with no short-term plan to implement them since they were relying on C code that need to be heavily updated.

Details

In addition, The questions about rates of pedigree error typically encountered, and their effects on inference of elementary QG parameters are basically settled (Charmantier & Réale 2005, Morrissey et al. 2007, Firth et al. 2015, Bourret & Garant 2017), and outstanding questions will not plausibly be supported by these functions or any straightforward modifications thereof.

Bourret, A., and D. Garant. 2017. An assessment of the reliability of quantitative genetics estimates in study systems with high rate of extra pair paternity and low recruitment. Heredity 118: 229-38.

Charmantier, A., and D. Réale. 2005. How do misassigned paternities affect estimation of heritability in the wild? Molecular Ecology 14: 2839-50

Firth, J.A. J.D. Hadfield, A.W. Santure, J. Slate and B.C. Sheldon. 2015. The influence of non-random extra pair paternity on heritability estimates derived from wild pedigrees. Evolution 69: 1336-44.

Morrissey, M.B., A.J. Wilson, J.M. Pemberton, and M.M. Ferguson. 2007. A framework for power and sensitivity analysis for quantitative genetic studies of natural populations, and a case study in Soay sheep (Ovis aries). Journal of Evolutionary Biology 20: 2309-21.

rpederr

Permutes a pedigree to create a plausible complete pedigree

fpederr

Simulates a pedigree with errors and missing data from a complete pedigree.

A function to simulated phenotypic data

Description

Simulates phenotypic data across arbitrary pedigrees. phen_sim simulate direct, maternal and paternal genetic and environmental effects for an arbitrary number of traits with arbitrary patterns of missing data.

Usage

phen_sim(
  pedigree,
  traits = 1,
  randomA = NULL,
  randomE = NULL,
  parentalA = NULL,
  parentalE = NULL,
  sampled = NULL,
  records = NULL,
  returnAllEffects = FALSE,
  verbose = TRUE
)

Arguments

pedigree

A pedigree

traits

The number of traits for which data should be simulated.

randomA

An additive genetic covariance matrix, with dimensions a multiple of traits - see details

randomE

An additive environmental covariance matrix, with dimensions a multiple of traits - see details

parentalA

A vector indicating which effects in randomA (if any) to treat as parental effects

parentalE

A vector indicating which effects in randomE (if any) to treat as parental effects

sampled

A vector indicating which individuals are sampled

records

A single value, array of matrix specifying data record availability - see details

returnAllEffects

If TRUE then all individual breeding values and environmental effects are returned

verbose

If TRUE provide a progress bar and messages, Default: TRUE

Details

randomA and randomE are square matrices with dimension equal to the sum of the number direct and indirect effects. This must be a multiple of the number of traits, i.e. if an indirect effect is to be simulated for only one of multiple traits, those traits with no indirect effect should be included with (co)variances of zero.

parentalA and parentalE are optional vectors of characters indicating which trait positions in randomA and randomE are to be treated as indirect effects, and which effects to treat as maternal or paternal. Valid values are 'd', 'm', and 'p', for direct, maternal indirect and paternal indirect effects, respectively.

records can be specified either (1) as a single value to be applied to all individuals and traits, (2) as a vector the same length as the number of traits, representing trait-specific rates to be applied uniformly to all individuals, or (3) as data frames with rows for each individual and columns for each trait. In the third option, observed patterns of data availability can be simulated by supplying 0s and 1s for missing and available individual genotypes, respectively.

Value

phenotypes

A dataframe containing phenotypes for all individuals specified to have records.

allEffects

(optional) A dataframe with all direct and indirect genetic and environmental effects.

See Also

micro_sim, genome_sim

Examples

## make up a pedigree
id <- c("a1", "a2", "a3", "a4", "a5", "a6", "a7", "a8", "a9")
dam <- c(NA, NA, NA, "a1", "a1", "a1", "a4", "a4", "a4")
sire <- c(NA, NA, NA, "a2", "a2", "a2", "a5", "a6", "a6")
pedigree <- as.data.frame(cbind(id, sire, dam))

traits <- 2
## no correlations
randomA <- diag(4)
randomE <- diag(4)
parentalA <- c("d", "d", "m", "m")
parentalE <- c("d", "d", "m", "m")

## generate phenoypic data based on this architecture
phen_sim(
  pedigree = pedigree, traits = 2, randomA = randomA, randomE = randomE,
  parentalA = parentalA, parentalE = parentalE
)

## let's do it again but see how the phenotypes were composed
phen_sim(
  pedigree = pedigree, traits = 2, randomA = randomA, randomE = randomE,
  parentalA = parentalA, parentalE = parentalE, returnAllEffects = TRUE
)

Plot output from ped_stats

Description

Generates a manageable summary of pedigree-wide statistics reported by ped_stats, either for a single pedigree or for a comparison between pedigrees

Usage

## S3 method for class 'ped_stats'
plot(x, lowMem = FALSE, grContrast = FALSE, ...)

Arguments

x

An object of class ped_stats generated by ped_stats

lowMem

If TRUE, then stats based on calculation of A are not performed.

grContrast

If TRUE, then uglier shades of red and blue are used to denote male and female statistics in graphical reports, but these colours provide better contrast in greyscale.

...

extra arguments

Value

Returns a table of numbers of records, maternities, paternities, pairwise sibship relationships, numbers of different classes of grand-parental relationships, pedigree depth, number of founders, mean sibship sizes, simple statistics of numbers of inbred and non-inbred individuals, and proportions of pairwise relationship coefficients equal to or greater than several thresholds.

Post-processes output from ped_stats

Description

Generates a manageable summary of pedigree-wide statistics reported by ped_stats, either for a single pedigree or for a comparison between pedigrees

Usage

## S3 method for class 'ped_stats'
summary(object, ...)

Arguments

object

An object of class ped_stats generated by ped_stats

...

extra arguments

Value

Returns a table of numbers of records, maternities, paternities, pairwise sibship relationships, numbers of different classes of grand-parental relationships, pedigree depth, number of founders, mean sibship sizes, simple statistics of numbers of inbred and non-inbred individuals, and proportions of pairwise relationship coefficients equal to or greater than several thresholds.

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