Package 'pedSimulate'

Title: Pedigree, Genetic Merit, Phenotype, and Genotype Simulation
Description: Simulate pedigree, genetic merits and phenotypes with random/non-random matings followed by random/non-random selection with different intensities and patterns in males and females. Genotypes can be simulated for a given pedigree, or an appended pedigree to an existing pedigree with genotypes. Mrode, R. A. (2005) <ISBN:9780851989969, 0851989969>; Nilforooshan, M.A. (2022) <doi:10.37496/rbz5120210131>.
Authors: Mohammad Ali Nilforooshan [aut, cre]
Maintainer: Mohammad Ali Nilforooshan <[email protected]>
License: GPL-3
Version: 1.4.3
Built: 2025-02-14 03:09:22 UTC
Source: https://github.com/nilforooshan/pedsimulate

Help Index

Pedigree, genetic merit, phenotype, and genotype simulation

Description

An R package for simulating a pedigree with genetic merits and phenotypes, starting from a base population (generation 0) or an existing pedigree. The pedigree depth and design can be chosen by the values provided to the arguments of the simulation function. Genotypes can be simulated for a given pedigree, or an appended pedigree to an existing pedigree with genotypes. Marker effects to be chosen by the researcher.

Details

Starting from a base population with a user-defined size and equal number of males and females, next generations are simulated for the user-defined litter size and number of generations. No selection (natural or artificial) and non-random mating is applied to this population. Alternatively, the simulation can be started from an existing pedigree. Natural (mortality) and artificial selection are applied to the next generations. Different generation overlap, selection intensities and selection patterns can be applied to males and females. Selected males and females are ordered similarly/differently to simulate various random, assortative or disassortative mating scenarios. Performance and genetic merit of individuals are simulated using the basic rules of quantitative genetics. The performance (P) of an individual is influenced by genetic (A) and environmental (E) effects. Thus, P = A + E, and Var(P) = Var(A) + Var(E). The additive genetic merit (A) of an individual is the average of its parents' additive genetic merits (PA = (Asire + Adam)/2) plus the Mendelian Sampling term due to the sampling of alleles passed from the parent to the offspring. The Mendelian Sampling variance is half of Var(A) in the base population. Because there is no provided information for environmental effects, the environment effect is assigned to individuals from a normal distribution of random numbers (E ~ N(0, IVar(E))). The package also provides functions to identify halfsib, fullsib and parent-progeny matings in the pedigree. For a given pedigree, genotypes can be simulated. Marker effects can be chosen by the researcher and added to the simulated phenotypes. In that case, genetic effects and variance used to simulate phenotypes change to residual polygenic effects and variance (genetic effects and variance not explained by the markers).

Author(s)

Mohammad Ali Nilforooshan [email protected]

References

Mrode, R. A. 2005. Linear Models for the Prediction of Animal Breeding Values, 2nd ed. Cambridge, MA: CABI Publishing. <ISBN:9780851989969, 0851989969>

Nilforooshan, M. A. 2022. pedSimulate – An R package for simulating pedigree, genetic merit, phenotype, and genotype data. R. Bras. Zootec., 51:e20210131. <doi:10.37496/rbz5120210131>

Simulate genotypes for an appended pedigree

Description

Simulate genotypes for an appended pedigree to an existing pedigree with genotypes.

Usage

appendGen(ped, M, AF = c(), mut.rate = 0, seed = NA)

Arguments

ped

: Pedigree data.frame with columns for animal, sire, and dam identification.
M

: Genotype data.frame with rows corresponding to the initial rows of the pedigree and columns corresponding to markers.
AF

: Vector of allele frequencies at different loci for the genotypes to be simulated. If no value is provided, it will be estimated from M.
mut.rate

: Vector of mutation rates at different loci for the genotypes to be simulated, default = 0 for no mutation.
seed

: A numeric variable input to the random number generator for reproducible simulations, default = 'NA' for non-reproducible simulations.

Details

Only diploid and bi-allelic situations are covered. No linkage disequilibrium is simulated.

Value

M2 : New simulated genotypes appended to M.

Examples

nSNP = 100
AF = runif(nSNP, 0.01, 0.99)
mut.rate = runif(nSNP, 0, 10^-5)
ped = data.frame(ID=1:5, SIRE=c(0,0,1,0,3), DAM=c(0,0,2,2,4))
gen = simulateGen(ped, AF, mut.rate)
ped = rbind(ped, data.frame(ID=6:8, SIRE=c(3,6,6), DAM=c(0,4,5)))
gen = appendGen(ped, M=gen, AF, seed=34)

Simulate new generations from an existing pedigree

Description

Simulate pedigree, genetic merits and phenotypes with random/assortative/disassortative matings followed by random/non-random selection of males and females with similar/different selection patterns in males and females, starting from an existing pedigree.

Usage

appendPed(
  ped,
  Va0,
  Ve,
  littersize = 1,
  ngen,
  mort.rate = 0,
  overlap.s = 0,
  overlap.d = 0,
  f.rate = 1,
  m.rate = 1,
  fsel = "R",
  msel = "R",
  f.order = "fsel",
  m.order = "msel",
  seed = NA
)

Arguments

ped

: The input pedigree data.frame with 9 columns: ID, SIRE, DAM, SEX, GEN (generation), PA (parent average), MS (Mendelian Sampling), E (environment and residuals), and P (phenotype). Note that PA + MS + E = P - μ\mu, where μ\mu is the population mean, and PA + MS = BV (genetic merit or breeding value). If MS and E are unknown, those can be set to 0. PA should be equal to the average of sire BV (SBV) and dam BV (DBV). If this condition is not met, PA - (SBV + DBV)/2 is added to MS and (SBV + DBV)/2 replaces PA.
Va0

: Additive genetic variance in the base generation (i.e., F0).
Ve

: Residual variance, constant across generations.
littersize

: Litter size, default = 1.
ngen

: Number of generations to simulate after the founder generation.
mort.rate

: Mortality rate per generation, after the availability of phenotype (e.g., birth weight, weaning weight) and before the age of maturity (i.e., before mating), default = 0. Maximum mort.rate = 0.5.
overlap.s

: Number of overlapping generations for sires, default = 0 for no generation overlap.
overlap.d

: Number of overlapping generations for dams, default = 0 for no generation overlap.
f.rate

: Proportion of females selected as dams, default = 1.
m.rate

: Proportion of males (<= f.rate) selected as sires, default = 1.
fsel

: If "R" (default), random selection on females; if "P", selection on phenotypes or true breeding values if Ve = 0; if "PA", selection on true parent averages. "-P" and "-PA" work in opposite direction of "P" and "PA", respectively.
msel

: If "R" (default), random selection on males; if "P", selection on phenotypes or true breeding values if Ve = 0; if "PA", selection on true parent averages. "-P" and "-PA" work in opposite direction of "P" and "PA", respectively.
f.order

: Ordering selected females for mating; if "fsel" (default), same as the selection order; if "R" random ordering; if "P", ordering based on phenotypes or true breeding values if Ve = 0; if "PA", ordering based on true parent averages. "-P" and "-PA" work in opposite direction of "P" and "PA", respectively.
m.order

: Ordering selected males for mating; if "msel" (default), same as the selection order; if "R" random ordering; if "P", ordering based on phenotypes or true breeding values if Ve = 0; if "PA", ordering based on true parent averages. "-P" and "-PA" work in opposite direction of "P" and "PA", respectively.
seed

: A numeric variable input to the random number generator for reproducible simulations, default = 'NA' for non-reproducible simulations.

Value

ped2 : New generations appended to the input pedigree data.frame.

Examples

ped = simulatePed(
    F0size = 100,
    Va0 = 9,
    Ve = 36,
    littersize = 2,
    ngen = 4,
    mort.rate = 0.05,
    overlap.s = 1,
    overlap.d = 0,
    f.rate = 0.8,
    m.rate = 0.5,
    fsel = "P",
    msel = "PA"
)
ped2 = appendPed(
    ped = ped,
    Va0 = 9,
    Ve = 36,
    littersize = 2,
    ngen = 2,
    mort.rate = 0.05,
    overlap.s = 1,
    overlap.d = 0,
    f.rate = 0.8,
    m.rate = 0.5,
    fsel = "R",
    msel = "R",
    f.order = "P",
    m.order = "PA",
    seed = 76
)

Find fullsib mates

Description

Find fullsib matings in the pedigree

Usage

fs_mate_finder(ped)

Arguments

ped

: A pedigree data.frame. The first three columns (ID, SIRE, DAM) are used.

Value

fs_mates : A data.frame with two columns (SIRE, DAM) representing fullsib mates.

Examples

ped = data.frame(ID=1:7, SIRE=c(0,0,1,0,3,3,5), DAM=c(0,0,0,2,4,4,6))
fs_mate_finder(ped)

Find halfsib mates

Description

Find halfsib matings in the pedigree

Usage

hs_mate_finder(ped)

Arguments

ped

: A pedigree data.frame. The first three columns (ID, SIRE, DAM) are used.

Value

hs_mates : A data.frame with two columns (SIRE, DAM) representing halfsib mates.

Examples

ped = data.frame(ID=1:7, SIRE=c(0,0,1,1,0,3,5), DAM=c(0,0,2,2,2,4,4))
hs_mate_finder(ped)

Find parent-progeny mates

Description

Find parent-progeny matings in the pedigree

Usage

pp_mate_finder(ped)

Arguments

ped

: A pedigree data.frame. The first three columns (ID, SIRE, DAM) are used.

Value

pp_mates : A data.frame with two columns (SIRE, DAM) representing parent-progeny mates.

Examples

ped = data.frame(ID=1:4, SIRE=c(0,0,1,1), DAM=c(0,0,2,3))
pp_mate_finder(ped)

Simulate genotypes

Description

Simulate genotypes for a given pedigree, allele frequency and mutation rate at each marker locus.

Usage

simulateGen(ped, AF, mut.rate = 0, seed = NA)

Arguments

ped

: Pedigree data.frame with columns for animal, sire, and dam identification.
AF

: Vector of allele frequencies at different loci for the genotypes to be simulated.
mut.rate

: Vector of mutation rates at different loci for the genotypes to be simulated, default = 0 for no mutation.
seed

: A numeric variable input to the random number generator for reproducible simulations, default = 'NA' for non-reproducible simulations.

Details

Only diploid and bi-allelic situations are covered. No linkage disequilibrium is simulated.

Value

M : The simulated genotype data.frame with rows corresponding to animals (in the same order as in the pedigree) and columns corresponding to markers.

Examples

nSNP = 100
AF = runif(nSNP, 0.01, 0.99)
mut.rate = runif(nSNP, 0, 10^-5)
ped = data.frame(ID=1:5, SIRE=c(0,0,1,0,3), DAM=c(0,0,2,2,4))
gen = simulateGen(ped, AF, mut.rate, seed=684)

Simulate pedigree, genetic merits and phenotypes

Description

Simulate pedigree, genetic merits and phenotypes with random/assortative/disassortative matings followed by random/non-random selection of males and females with similar/different selection patterns in males and females.

Usage

simulatePed(
  F0size,
  Va0,
  Ve,
  littersize = 1,
  ngen,
  mort.rate = 0,
  overlap.s = 0,
  overlap.d = 0,
  f.rate = 1,
  m.rate = 1,
  fsel = "R",
  msel = "R",
  f.order = "fsel",
  m.order = "msel",
  seed = NA
)

Arguments

F0size

: Even number of founder animals. No mortality, selection and non-random mating in this generation.
Va0

: Additive genetic variance in the base generation (i.e., F0).
Ve

: Residual variance, constant across generations.
littersize

: Litter size, default = 1.
ngen

: Number of generations to simulate after the founder generation.
mort.rate

: Mortality rate per generation, after the availability of phenotype (e.g., birth weight, weaning weight) and before the age of maturity (i.e., before mating), default = 0. Maximum mort.rate = 0.5.
overlap.s

: Number of overlapping generations for sires, default = 0 for no generation overlap.
overlap.d

: Number of overlapping generations for dams, default = 0 for no generation overlap.
f.rate

: Proportion of females selected as dams, default = 1.
m.rate

: Proportion of males (<= f.rate) selected as sires, default = 1.
fsel

: If "R" (default), random selection on females; if "P", selection on phenotypes or true breeding values if Ve = 0; if "PA", selection on true parent averages. "-P" and "-PA" work in opposite direction of "P" and "PA", respectively.
msel

: If "R" (default), random selection on males; if "P", selection on phenotypes or true breeding values if Ve = 0; if "PA", selection on true parent averages. "-P" and "-PA" work in opposite direction of "P" and "PA", respectively.
f.order

: Ordering selected females for mating; if "fsel" (default), same as the selection order; if "R" random ordering; if "P", ordering based on phenotypes or true breeding values if Ve = 0; if "PA", ordering based on true parent averages. "-P" and "-PA" work in opposite direction of "P" and "PA", respectively.
m.order

: Ordering selected males for mating; if "msel" (default), same as the selection order; if "R" random ordering; if "P", ordering based on phenotypes or true breeding values if Ve = 0; if "PA", ordering based on true parent averages. "-P" and "-PA" work in opposite direction of "P" and "PA", respectively.
seed

: A numeric variable input to the random number generator for reproducible simulations, default = 'NA' for non-reproducible simulations.

Details

The output pedigree data.frame (ped) has 9 columns: ID, SIRE, DAM, SEX, GEN (generation number starting with 0 for the base generation), PA (parent average), MS (Mendelian Sampling), E (environment and residuals), and P (phenotype).

Random, assortative, and disassortative matings can be simulated with different combinations of fsel, msel, f.order, and m.order.

Value

ped : The output pedigree data.frame. Further information provided in Details.

Examples

ped = simulatePed(
    F0size = 100,
    Va0 = 9,
    Ve = 36,
    littersize = 2,
    ngen = 4,
    mort.rate = 0.05,
    overlap.s = 1,
    overlap.d = 0,
    f.rate = 0.8,
    m.rate = 0.5,
    fsel = "P",
    msel = "PA",
    f.order = "fsel",
    m.order = "msel",
    seed = 68
)