Package {HAPTRACE}

Title:	Haplotype-Based Tracking of Admixed Population for Breed Composition Estimation
Version:	0.1.1
Maintainer:	Shweta Sahoo <queryhap01@gmail.com>
Description:	Simulate populations and track haplotypes over generations to evaluate population admixture. The 'HAPTRACE' supports customisable population parameters, including size, number of markers, mutation rates and recombination.
License:	GPL (≥ 3)
Encoding:	UTF-8
RoxygenNote:	8.0.0
Imports:	data.table, gdata, hsphase (≥ 3.0.1), Rcpp, RcppArmadillo
Suggests:	knitr, rmarkdown
VignetteBuilder:	knitr
LinkingTo:	Rcpp, RcppArmadillo
NeedsCompilation:	yes
Packaged:	2026-07-22 08:30:28 UTC; ssres
Author:	Shweta Sahoo [aut, cre], Sara de las Heras-Saldana [aut], Julius H. J. van der Werf [aut], Mohammad H. Ferdosi [aut]
Repository:	CRAN
Date/Publication:	2026-07-30 17:30:15 UTC

Plot for True Breed Composition

Description

Generates barplot of true breed composition of the animals with haplotype information coded according to the breed of origin.

Usage

Admixplot(trueBC, color, legendlabels)

Arguments

Value

Returns an admixture plot of animals where x axis shows number of animals and y axis shows the proportion of different breeds in different colors.

See Also

trueBreedComp()

Examples

popAdm <- matrix(c(-1, 2, 3, -4, 2,
1,-1,2,2,3,
1,2,-3,-4,-5,
2,-3,2,4,-5), byrow = TRUE, nrow = 4, ncol = 5)
TBC <- trueBreedComp(Haplotype = popAdm)
Admixplot(trueBC = TBC, color = c("#FFC107", "#2E7D32", "#FB8072", "#386CB0", "#4d4a49"),
legendlabels = c("A", "B", "C", "D", "E"))

Calculate Allele Frequency

Description

Function to calculate allele frequency at each locus of a population.

Usage

AlleleFreq(df)

Arguments

Value

Returns the value of allele frequency at each locus.

Examples

popC <- matrix(data = sample(c(0,1,2), 60, replace = TRUE), nrow = 6, ncol = 10)
allelefreq <- AlleleFreq(df = popC)

True Breeding Value (TBV) estimation according to breed of origin

Description

Function to estimate TBV for animals having different SNP effects according to breed of origin.

Usage

BreedEff_Haplo(Haplotype, Breed_Effect)

Arguments

Value

Returns a matrix of coded Haplotype with TBV estimated according to the breed of origin.

Examples

pop_BH <- matrix(sample(c(-1,1,-2,2,-3,3), 16, replace = TRUE), nrow = 4, byrow = TRUE)
Breed_Eff <- matrix(runif(4 * 4, 0, 1), nrow = 4, byrow = TRUE)
Breed_Hap <- BreedEff_Haplo(Haplotype = pop_BH, Breed_Effect = Breed_Eff)

Function for crossing two population with breed effects according to breed of origin

Description

Function to cross two population and estimation of TBV with breed effect according to the breed of origin.

Usage

BreedPopCross(
  map = map,
  populationSim_A,
  populationSim_B,
  nSireA = 54,
  nDamA = 1000,
  nSireB = 26,
  nDamB = 1000,
  Sire_From,
  mutationRate = 2.5 * 10^-5,
  SelType,
  h2 = 0.3,
  trait_mean,
  VarE_PopA,
  VarE_PopB,
  Breed_Effect_A,
  Breed_Effect_B,
  IndStartVal = 1,
  prefixID = "Cr",
  nProgeny = 1000,
  recL,
  nChr,
  recProb,
  minLength
)

Arguments

Value

Returns a list of Haplotype information, ID of the simulated animals, population with TBV and phenotype, recombination point list of sire and dam.

See Also

BreedEff_Haplo(), BreedPopSelect()

Examples

crossmap <- generateMAP(nChr = 5, n_markers = 2, len_Chr = 20)
QTL_cross <- matrix(data = c(-0.001, 0.0003, 0, 0, 0, 0.0032, 0.0023, -0.0012,0, 0)
, nrow = 1, ncol = 10)
P1cross <- matrix(data = sample(c(-1, 1), 40, replace = TRUE), nrow = 4, ncol = 10)
P2cross <- matrix(data = sample(c(-2, 2), 40, replace = TRUE), nrow = 4, ncol = 10)
P1cross <- PopulationID(Population = P1cross, PopCode = "P1")
P2cross <- PopulationID(Population = P2cross, PopCode = "P2")
F2cross <- BreedPopCross(map = crossmap, populationSim_A = P1cross,
populationSim_B = P2cross, nSireA = 1, nDamA = 1, nSireB = 1, nDamB = 1,
Sire_From = "A",mutationRate = 2.5 * 10^-5, SelType = "TBV", h2 = 0.3,
Breed_Effect_A = QTL_cross, Breed_Effect_B = QTL_cross, IndStartVal = 1,
prefixID = "Cr", nProgeny = 10, recL = 1, nChr = 2, minLength = 0,
trait_mean = 5, VarE_PopA = 0.6, VarE_PopB = 0.6)

Selecting Population with breed effect according to breed of origin

Description

Function to simulate a population with different breed effect according to breed of origin.

Usage

BreedPopSelect(
  map,
  Haplotype,
  nSire,
  nDam,
  rate = 2.5 * 10^-5,
  nProgeny,
  SelType = "random",
  Breed_Effect,
  h2,
  trait_mean,
  VarE,
  recL,
  nChr,
  recProb,
  minLength
)

Arguments

Value

Returns selected sire and dams along with with their ID, recombination points of sire and dam, and population coded haplotype with phenotype and TBV.

See Also

BreedEff_Haplo()

Examples

PopS <- matrix(data = sample(c(-1,1, -2,2), 200, replace = TRUE),
nrow = 20, ncol = 10)
PopS_map <- generateMAP(2, 5, 5)
PopS_QTL <-  matrix(c(-0.001, 0.0003, 0, 0, 0, 0.0032, 0.0023, -0.0012,0, 0,
 -0.002, 0.004, 0, 0, 0.002, 0, 0.002, 0,0, -0.0012), byrow = 2, nrow = 2, ncol = 10)
Selpop <- BreedPopSelect(map = PopS_map, Haplotype = PopS, nSire = 2, nDam = 2, rate = 2.5 * 10^-5,
nProgeny = 6, SelType = "random", Breed_Effect = PopS_QTL, trait_mean = 5,VarE = 0.6,
h2 = 0.2, recL = 1, nChr = 2, minLength = 0)

Calculate QTL effect from file generated from ReadQTL (For QMSim generated files)

Description

This function requires the file with information on markerID, chromosome and marker information with prefix "lm_mrk_qtl", total number of SNP markers ,and QTL effects generated from ReadQTL function. It generates SNP effects of length of total number of markers along with QTLs.

Usage

CalQTLeffect(filename, nSNP, QTLfile)

Arguments

Value

Returns a vector of the SNP effects with length equals to the total number of SNP markers.

See Also

ReadQTL()

Examples

## Not run: 
# This example is not executed since it needs large files.
popQM <- read_qmsim_geno("p1_mrk_qtl_001.txt")
QTL <- ReadQTL(filename = "effect_qtl_001.txt")
QTLeffect <- CalQTLeffect(filename = "lm_mrk_qtl_001.txt",
nSNP = ncol(popQM),QTLfile = QTL)

## End(Not run)

Recode Coded haplotypes according to breed of origin into 0 and 1 format

Description

Function to recode the coded haplotype back to its original form.

Usage

Coded_to_Haplo(df)

Arguments

Value

Returns a matrix consisting of a haplotype information in (0,1) format.

Examples

popB <- matrix(data = sample(c(-1,1), 60, replace = TRUE), nrow = 6, ncol = 10)
coded_haplo <- Coded_to_Haplo(df = popB)

Select Dams

Description

Function to select the dams.

Usage

DamSample(Haplotype, nDam, nProgeny)

Arguments

Value

Return a list of dam IDs and haplotype information of the dam.

Examples

pop_J <- matrix(data = sample(c(0,1), 60, replace = TRUE), nrow = 6, ncol = 10)
pop_J_dam <- DamSample(Haplotype = pop_J, nDam = 1, nProgeny = 4)

Extract TBV and Phenotype

Description

Extract TBV and Phenotype

Usage

Ext_Pheno(df)

Arguments

Value

A datafile comprising Animal ID, TBV and phenotype.

Examples

popEP <- matrix(data= sample(c(0,1), 16, replace= TRUE), nrow = 4, ncol = 4)
popEP <- PopulationID(Population = popEP, PopCode = "EP")
QTLEff <- c(0.2, 0.1, 0.3, 0.1)
TBV_haplotype2 <- TBV_Haplo(Haplotype = popEP, Effect = QTLEff)
Var_Ph_R <- Var_PhenoRecords(Haplotype = TBV_haplotype2, h2 = 0.3,
trait_mean = 20, VarE = 0.6)
Haplo_TBV_Ph <- Var_Ph_R$Haplo_pheno
Datafile <- Ext_Pheno(Haplo_TBV_Ph)

Generate Population with specific breed effect

Description

Generate Population of a crossbred with breed effect specific to the breed of origin.

Usage

GenBreedPop(
  ngenerations = 5,
  map = map,
  populationSim,
  nSire = 54,
  nDam = 1000,
  mutationRate = 2.5 * 10^-5,
  SelType = "random",
  h2 = 0.3,
  trait_mean,
  VarE,
  Breed_Effect,
  recL,
  nChr,
  recProb,
  minLength,
  IndStartVal = 1,
  prefixID = "A",
  nProgeny = 1000
)

Arguments

Value

Returns a list of Haplotype information, pedigree of the simulated animals, population with TBV and phenotype, recombination point list of sire and dam.

See Also

BreedEff_Haplo(), BreedPopSelect()

Examples

PopGen <- matrix(data = sample(c(-1, 1), 200, replace = TRUE), nrow = 20, ncol = 10)
PopGen <- PopulationID(Population = PopGen, PopCode = "PG1")
PopGen_map <- generateMAP(nChr = 2, n_markers = 5, len_Chr = 5)
QTL_PopGen <- matrix(data = c(-0.001, 0.0003, 0, 0, 0,
0.0032, 0.0023, -0.0012,0, 0), nrow = 1, ncol = 10)
PopF1 <- GenBreedPop(ngenerations = 2, map = PopGen_map, populationSim = PopGen,
nSire = 1, nDam = 1,mutationRate = 2.5 * 10^-5, SelType = "TBV", h2 = 0.3, trait_mean = 5,
VarE = 0.6, Breed_Effect = QTL_PopGen, recL = 1, nChr = 2, minLength = 0,
IndStartVal = 1,prefixID = "A", nProgeny = 10)

Simulate Population

Description

This function simulates a population for multiple number of generations as specified by the users. It generates coded Haplotype as per breed of origin, from which we can extract haplotype and genotype.It also generates pedigree file, true breeding value, phenotype, recombination points and recombination probability.

Usage

GenOnePopulation(
  ngenerations = 5,
  map = map,
  populationSim,
  nSire = 54,
  nDam = 1000,
  mutationRate = 2.5 * 10^-5,
  SelType = "random",
  h2 = 0.3,
  trait_mean,
  VarE,
  Effect,
  recL,
  nChr,
  recProb,
  minLength,
  IndStartVal = 1,
  prefixID = "A",
  nProgeny = 1000
)

Arguments

Value

List of Haplotype information , pedigree of the simulated animals, population with TBV and phenotype, recombination point list of sire and dam.

See Also

generateMAP(), PopSelect()

Examples

PopGen <-  matrix(data = sample(c(-1, 1), 200, replace = TRUE), nrow = 20, ncol = 10)
PopGen <- PopulationID(Population = PopGen, PopCode = "A1")
PopGen_map <- generateMAP(nChr = 2, n_markers = 5, len_Chr = 5)
QTL_PopGen <- c(-0.001, 0.0003, 0, 0, 0, 0.0032, 0.0023, -0.0012,0, 0 )
PopF1 <- GenOnePopulation(ngenerations = 2, map = PopGen_map, populationSim = PopGen,
nSire = 1, nDam = 1, mutationRate = 2.5 * 10^-5, SelType = "TBV", h2 = 0.3,
trait_mean = 5,VarE = 0.6, Effect = QTL_PopGen, recL = 1, nChr = 2,
minLength = 0, IndStartVal = 1, prefixID = "A", nProgeny = 10)

Make Genotype from Haplotype

Description

Function to generate genotype information from haplotype (0,1) information.

Usage

MakeGeno(df)

Arguments

Value

Returns the genotype information of the animal.

Examples

popD <- matrix(data = sample(c(0,1), 60, replace = TRUE), nrow = 6, ncol = 10)
genotype <- MakeGeno(df = popD)

Function for mating sire and dam

Description

Function to generate progeny from selected sires and dams.

Usage

MatingPop(Sire, Dam)

Arguments

Value

Returns haplotype information of the progeny.

Examples

Sire_K <- matrix(data = sample(c(0,1), 30, replace = TRUE), nrow = 3, ncol = 10)
Dam_K <- matrix(data = sample(c(0,1), 30, replace = TRUE), nrow = 3, ncol = 10)
popK <- MatingPop(Sire = Sire_K, Dam = Dam_K)

Adding Mutation

Description

Function to add mutation rate to the haplotype information of the animals.

Usage

MutationRate(Haplotype, rate)

Arguments

Value

Returns a haplotype matrix with mutations.

Examples

popH <- matrix(data = sample(c(0,1), 60, replace = TRUE), nrow = 6, ncol = 10)
popH_Mutation <- MutationRate(Haplotype = popH, rate = 2.5*10^-5)

Combine Pedigree and Phenotype data

Description

Combine Pedigree and Phenotype data

Usage

Ped_Pheno(Pedigree, Phenotype)

Arguments

Value

A datafile comprising AnimalID, Sire ID, Dam ID, Sex, TBV and phenotype of the animal.

See Also

Ext_Pheno()

Examples

PopGen1 <-  matrix(data = sample(c(-1, 1), 200, replace = TRUE), nrow = 20, ncol = 10)
PopGen1 <- PopulationID(Population = PopGen1, PopCode = "PG")
PopGen1_map <-  generateMAP(2, 5, 5)
QTL_PopGen1  <-  c(-0.001, 0.0003, 0, 0, 0, 0.0032, 0.0023, -0.0012,0, 0)
PopPP <- GenOnePopulation(ngenerations=2, map=PopGen1_map,
PopGen1,nSire=1,nDam=1,mutationRate=2.5 * 10^-5,
SelType = "TBV", h2 = 0.3, trait_mean = 5 ,VarE = 0.6, Effect = QTL_PopGen1,
recL = 1, nChr = 2, minLength = 0, IndStartVal=1,prefixID="A",nProgeny=10)
Phenofile <- Ext_Pheno(df = PopPP$population)
Ped_Phenofile <- Ped_Pheno(Pedigree = PopPP$parents, Phenotype = Phenofile)

Selecting Population with selection criteria random, phenotype and true breeding value

Description

This function select sires and dams of the population using random, phenotypic or true breeding value.

Usage

PopSelect(
  map,
  Haplotype,
  nSire,
  nDam,
  rate = 2.5 * 10^-5,
  nProgeny,
  SelType = "random",
  Effect,
  h2,
  trait_mean,
  VarE,
  recL,
  nChr,
  recProb,
  minLength
)

Arguments

Value

It generates selected sire and dams along with with their ID, recombination points of sire and dam, and population coded haplotype with phenotype and TBV.

See Also

generateMAP()

Examples

PopS <- matrix(data = sample(c(0,1), 200, replace = TRUE),
nrow = 20, ncol = 10)
PopS_map <- generateMAP(nChr = 2, n_markers = 5, len_Chr = 5)
PopS_QTL <-  c(-0.001, 0.0003, 0, 0, 0, 0.0032, 0.0023, -0.0012,0, 0 )
Selpop <-  PopSelect(map = PopS_map, Haplotype = PopS, nSire = 2, nDam = 2,
rate = 2.5 * 10^-5, nProgeny = 6, SelType = "random", Effect = PopS_QTL,
trait_mean = 5, VarE = 0.6, h2 = 0.2, recL = 1, nChr = 2, minLength = 0)

Adding Population Code before simulating the population

Description

Function to add Population code to the population which will help in generating parent IDs.

Usage

PopulationID(Population, PopCode)

Arguments

Value

Returns a population with provided PopCode, which will appear as PopulationID.

Examples

PopN_Check <- matrix(data = sample(c(0,1), 200, replace = TRUE), nrow = 10, ncol = 20)
PopN_CheckID <- PopulationID(Population = PopN_Check, PopCode = "P1")

Format PLINK MAP file from QMSim

Description

Function to format map file from QMSim for PLINK analysis.

Usage

QMSim_PlinkMAP(filepath, outputpath, filename)

Arguments

Value

Returns a map file that can be used for PLINK analysis.

Examples

## Not run: 
# This example is not executed since it needs large files.
output_folder <- file.path(tempdir(), "map")
dir.create(output_folder, showWarnings = FALSE, recursive = TRUE)
QMSim_PlinkMAP(filepath = "lm_mrk_qtl_001.txt",
outputpath = output_folder, filename = "pop")

## End(Not run)

Plot for the QTL effect

Description

Generates the density plot for the QTL effects.

Usage

QTL_densityplot(QTL_eff_file, color = "steelblue")

Arguments

Value

shows density plot depicting the distribution of the QTL effects.

See Also

QTLeffects()

Examples

example_QTL <- QTLeffects(n_QTL_Chr = 1, nChr = 5, len_Chr = c(10, 8, 6, 4, 2),
shape_gamma = 0.4)
plot <- QTL_densityplot(QTL_eff_file = example_QTL, color = "forestgreen")

Calculation QTL effects

Description

Function to estimate QTL effects with output of QTL ID, chromosome, QTL position and QTL effects

Usage

QTLeffects(
  n_QTL_Chr,
  nChr,
  len_Chr,
  shape_gamma = 0.4,
  effect_distribution = "gamma"
)

Arguments

Value

file comprising of QTL ID, chromosomes, position of the QTL and QTL effects.

See Also

generateMAP(), mapQTLfile()

Examples

QTL <- QTLeffects(n_QTL_Chr = 1, nChr = 2, len_Chr = 10,
shape_gamma = 0.4, effect_distribution = "gamma")

Random selection of sire and dam gametes

Description

Random selection of sire and dam gametes

Usage

Random_gametes(Gamete_Matrix)

Arguments

Value

Returns matrix comprising random selection of gametes

Examples

mat = matrix(data = sample(c(-1,1,-2,2), 100*10, replace = TRUE)
, nrow = 100, ncol = 10)
mat = PopulationID(Population = mat, PopCode = "M")
rev_mat = Random_gametes(Gamete_Matrix = mat)

Read QTL file obtained from QMSim

Description

This function reads the effect file generated from QMSim with the file prefix "effect_qtl". It generates the effects for the defined QTLs in QMSim.

Usage

ReadQTL(filename)

Arguments

Value

Returns a readable QTL effect

See Also

CalQTLeffect()

Examples

## Not run: 
# This example is not executed since it needs large files.
QTLfile <- ReadQTL(filename = "effect_qtl_001.txt")

## End(Not run)

Recode Haplotype with different breed codes

Description

Function to recode the haplotype according to different breed codes as specified by users.

Usage

Recode_Haplotype(df, popBase)

Arguments

Value

Returns a matrix consisting of coded haplotype information of the animals according to the defined popBase.

Examples

popA <- matrix(data = sample(c(0,1), 60, replace = TRUE), nrow = 6, ncol = 10)
PA_Haplo <- Recode_Haplotype(df = popA, popBase = 1)

Add Recombination

Description

Function to add recombination to the haplotype information of the animals.

Usage

RecombinationPoint(map, Haplotype, recL, nChr, recProb)

Arguments

Value

Returns a list of recombination points and number of recombinations per animal.

Examples

popG <- matrix(data = sample(c(0,1), 40, replace = TRUE), nrow = 4, ncol = 10)
popGmap <- generateMAP(nChr = 2, n_markers = 5, len_Chr = 10)
popGRec <- RecombinationPoint(map = popGmap, Haplotype = popG, recL = 2, nChr = 2)

Reformat genotype data for BLUPF90 analysis

Description

Function to format genotype file for BLUPF90 analysis.

Usage

Reformat_BLUPF90(Genodata, savefile)

Arguments

Value

Returns a text file comprising of genotype data for its use in BLUPF90.

Examples

Geno <- matrix(data = sample(c(0,1,2), 100, replace = TRUE), nrow = 10, ncol = 10)
rownames(Geno) <- paste("G", 1:nrow(Geno), sep = "_")
output_path <- file.path(tempdir(), "genotype")
Reformat_BLUPF90(Genodata = Geno, savefile = output_path)

Rescaling SNP Effect

Description

This function allows scaling of the SNP effects based on the scaling factor derived from genotype of the base population and standard deviation of the phenotype. This allows users to scale the SNP effects to get desirable variance components.

Usage

Rescale(Haplotype, Effect, Phenosd, h2)

Arguments

Value

This function returns a scaled SNP effect, scaled BV along with phenotypic, additive and residual variance.

Examples

popRescale <- matrix(c(0,1,1,0,
1,0,0,0,
1,1,1,1,
1,1,1,0), byrow = TRUE, nrow = 4, ncol = 4)
QTLEff = c(0.1, 0.2, 0, 0)
RescaleEff <- Rescale(Haplotype = popRescale, Effect = QTLEff,
Phenosd = 4, h2 = 0.2)

Select Sires

Description

Function to select the sire.

Usage

SireSample(Haplotype, nSire, nProgeny)

Arguments

Value

Returns a list of sire IDs and haplotype information of the sire.

Examples

popH <- matrix(data = sample(c(0,1), 60, replace = TRUE), nrow = 6, ncol = 10)
popH_Sire <- SireSample(Haplotype = popH, nSire = 1, nProgeny = 4)

Calculation of True breeding Value (TBV)

Description

Function to estimate true breeding value.

Usage

TBV_Haplo(Haplotype, Effect)

Arguments

Value

Returns a haplotype information with TBV estimated from animal's haplotype information. The TBV mentioned in "TBV" column provides final TBV (sum of TBV from haplotype of the animal) for an animal.

See Also

Var_PhenoRecords()

Examples

popE <- matrix(data= sample(c(0,1), 16, replace= TRUE), nrow = 4, ncol = 4)
QTLEff <- c(0.2, 0.1, 0.3, 0.1)
TBV_haplotype <- TBV_Haplo(Haplotype = popE, Effect = QTLEff)

Function for crossing two populations

Description

Function for making a cross between two populations.

Usage

TwoPopCross(
  map = map,
  populationSim_A,
  populationSim_B,
  nSireA = 54,
  nDamA = 1000,
  nSireB = 26,
  nDamB = 1000,
  Sire_From,
  mutationRate = 2.5 * 10^-5,
  SelType,
  h2 = 0.3,
  trait_mean,
  VarE_PopA,
  VarE_PopB,
  Effect_PopA,
  Effect_PopB,
  IndStartVal = 1,
  prefixID = "Cr",
  nProgeny = 1000,
  recL,
  nChr,
  recProb,
  minLength
)

Arguments

Value

List of Haplotype information , ID of the simulated animals, population with TBV and phenotype, recombination point list of sire and dam.

See Also

generateMAP(), PopSelect(), RecombinationPoint()

Examples

crossmap <- generateMAP(nChr = 5, n_markers = 2, len_Chr = 20)
QTL_cross <- c(-0.001, 0.0003, 0, 0, 0, 0.0032, 0.0023, -0.0012,0, 0 )
P1cross <- matrix(data = sample(c(-1, 1), 40, replace = TRUE), nrow = 4, ncol = 10)
P2cross <- matrix(data = sample(c(-2, 2), 40, replace = TRUE), nrow = 4, ncol = 10)
P1cross <- PopulationID(Population = P1cross, PopCode = "P1")
P2cross <- PopulationID(Population = P2cross, PopCode = "P2")
F2cross <- TwoPopCross(map = crossmap, populationSim_A = P1cross, populationSim_B = P2cross,
nSireA = 1, nDamA = 1, nSireB = 1, nDamB = 1, Sire_From = "A", mutationRate = 2.5 * 10^-5,
SelType = "TBV", h2 = 0.3, Effect_PopA = QTL_cross, Effect_PopB = QTL_cross, IndStartVal = 1,
prefixID ="Cr", nProgeny = 10, recL = 1, nChr = 2, minLength = 0, trait_mean = 5,
VarE_PopA = 0.6, VarE_PopB = 0.6)

Generate variance components and phenotypic records

Description

Function to generate variance components and phenotype records for the simulated population.

Usage

Var_PhenoRecords(Haplotype, h2, trait_mean, VarE)

Arguments

Value

Additive genetic variance, phenotypic variance, error variance and haplotype marker information with phenotypic records.

See Also

TBV_Haplo()

Examples

popF <- matrix(data= sample(c(0,1), 16, replace= TRUE), nrow = 4, ncol = 4)
QTLEff <- c(0.2, 0.1, 0.3, 0.1)
TBV_haplotype <- TBV_Haplo(Haplotype = popF, Effect = QTLEff)
Var_Ph_R <- Var_PhenoRecords(Haplotype = TBV_haplotype, h2 = 0.3,
trait_mean = 20, VarE = 0.6)

Generate Historical Population

Description

Function to simulate historical population.

Usage

generateHP(
  n_generations,
  n_animals,
  map,
  nSire,
  nDam,
  nProgeny,
  mutationRate = 2.5 * 10^-5,
  SelType = "random",
  Effect,
  h2 = 0.3,
  trait_mean = 10,
  VarE,
  recL = 5,
  nChr,
  recProb,
  minLength = 0,
  IndStartVal = 1,
  prefixID = "H"
)

Arguments

Value

Returns haplotype information of the historical population.

See Also

MutationRate(), mapQTLfile(), generateMAP(), QTLeffects()

Examples

HP_map <- generateMAP(nChr = 2, n_markers = 5, len_Chr = 5)
QTL_HP <- QTLeffects(n_QTL_Chr = 2, nChr = 2, len_Chr = 5, shape_gamma = 0.4,
effect_distribution = "gamma")
HP_mapqtl <- mapQTLfile(map = HP_map, QTL = QTL_HP)
HP_effect <- as.numeric(HP_mapqtl$Effect)
H1 <- generateHP(n_generations = 5, n_animals = 20, map = HP_mapqtl,
nSire = 1, nDam = 1, nProgeny = 6, mutationRate = 2.5 * 10^-5, SelType = "random",
Effect = HP_effect, h2 = 0.3, trait_mean = 10, VarE = 0.6,recL = 2,nChr = 2, minLength = 0)

Generate MAP file

Description

Function to generate MAP file with marker ID, chromosome and marker position columns.

Usage

generateMAP(nChr, n_markers, len_Chr)

Arguments

Value

Returns a map file of the population with marker ID, chromosome number and position of the markers on the chromosome.

See Also

QTLeffects(), mapQTLfile()

Examples

map <- generateMAP(nChr = 2, n_markers = 10, len_Chr = 5)

Create combined map and qtl file

Description

This function combines marker and qtl file together. SNP markers are often observed to be near or away from the QTL in real life scenario. However, QTL position is included along with map file to get combined marker file for simulation purpose in this package. This step is crucial to calculate the true breeding value and simulate the phenotype with desired population parameters.

Usage

mapQTLfile(map, QTL)

Arguments

Value

combined file with marker and QTL ID, chromosome, position and QTL effects.

See Also

generateMAP(), QTLeffects()

Examples

popmap <- generateMAP(nChr = 5, n_markers = 10,
len_Chr = c(10, 20, 40, 50, 60))
popQTL <- QTLeffects(n_QTL_Chr = 2, nChr = 5, len_Chr = c(10, 20, 40, 50, 60),
shape_gamma = 0.4, effect_distribution = "gamma")
pop_mapqtl <- mapQTLfile(map = popmap, QTL = popQTL)

Generate PLINK pedigree file

Description

Function to generate ped file for PLINK analysis.

Usage

plinkped(Genotype, AnimalID, filename)

Arguments

Value

A ped file for PLINK analysis. As of now, it only saves animal ID and genotypes. Saving pedigree in ped file is in our plan for the update.

Examples

pop_plink <- matrix(data = sample(c(0,1,2), 60, replace = TRUE), nrow = 6, ncol = 10)
rownames(pop_plink) <- paste("PP", 1:nrow(pop_plink), sep = "_")
output_path <- file.path(tempdir(), "pop")
plinkped(Genotype = pop_plink, AnimalID = rownames(pop_plink), filename = output_path)

Plot for Haplotype segments

Description

Function to generate plot of haplotype segments inherited from different breeds.It utilises the coded haplotype information according to the breed of origin and generates plot at all chromosomes.

Usage

plotHaplo(Haplotype, colors, map, nChr, legendlabels)

Arguments

Value

Haplotype plot showing blocks from different population for animals.

See Also

generateMAP()

Examples

Haplotype <- matrix(data = sample(c(-1, 1, -2, 2, -3, 3), 100, replace = TRUE),
nrow = 10, ncol = 10)
Haplotype_ID <- PopulationID(Population = Haplotype, PopCode = "H1")
map <- generateMAP(nChr = 2, n_markers = 5, len_Chr = 5)
colors = c("gold", "forestgreen", "steelblue")
plot <- plotHaplo(Haplotype = Haplotype_ID, colors = colors, map = map,
nChr = 2, legendlabels = c("A", "B", "C"))

Read QMSim Marker/QTL Genotype File

Description

Efficiently reads a QMSim p1_mrk_qtl_*.txt genotype file using compiled C++ code (Rcpp) and returns a single integer matrix.

Usage

read_qmsim_geno(file_path, geno_mode = "haplotype", phase_seed = 42L)

Arguments

Details

Each character in the QMSim genotype string represents one locus:

Lines ending with > are continuation lines and are concatenated automatically.

Value

An integer matrix with rownames set to animal IDs.

Examples

## Not run: 
# This example is not executed since it needs large files.
# Two 0/1 rows per individual (paternal / maternal haplotypes)
hap <- read_qmsim_geno("p1_mrk_qtl_001.txt")
dim(hap)        # 2*n_ind  x  n_loci

# Raw genotype codes (0-4) as in the file
geno <- read_qmsim_geno("p1_mrk_qtl_001.txt", geno_mode = "allele")
dim(geno)       # n_ind  x  n_loci

## End(Not run)

Plot for recombination points and recombination probability

Description

Function to generate plot for recombination points and recombination probability.

Usage

recProbplot(recList, n_markers, col = "steelblue", map)

Arguments

Value

This function returns the plots for recombination points and recombination probability.

See Also

generateMAP(), RecombinationPoint()

Examples

map_rec <- generateMAP(nChr = 2, n_markers = 5, len_Chr = 10)
poprec <- matrix(data = sample(c(0,1), 1000, replace = TRUE), nrow = 100, ncol = 10)
RecPoint <- RecombinationPoint(map = map_rec, Haplotype = poprec, recL = 2, nChr = 2)
reclist <- RecPoint$recList
recom_plot <- recProbplot(recList = reclist, n_markers = 10, map = map_rec)

Calculate True Breed Proportion with coded haplotype

Description

Function to calculate true breed proportion in an animal with coded haplotype information.

Usage

trueBreedComp(Haplotype)

Arguments

Value

Returns true breed proportion of population with coded haplotype.

See Also

Admixplot()

Examples

P1 <- matrix(data = sample(c(-1, 1, 2, -2), 20, replace = TRUE), nrow = 4, ncol = 5)
TBP <- trueBreedComp(Haplotype = P1)