breeding.diploid: Breeding function
In MoBPS: Modular Breeding Program Simulator
View source: R/breeding.diploid.R
breeding.diploid R Documentation
Breeding function
Description
Function to simulate a step in a breeding scheme
Usage
breeding.diploid(
population,
selection.size = 0,
selection.criteria = NULL,
selection.m.gen = NULL,
selection.f.gen = NULL,
selection.m.database = NULL,
selection.f.database = NULL,
selection.m.cohorts = NULL,
selection.f.cohorts = NULL,
max.selection.fullsib = Inf,
max.selection.halfsib = Inf,
class.m = 0,
class.f = 0,
add.class.cohorts = TRUE,
multiple.bve = "add",
selection.index.weights.m = NULL,
selection.index.weights.f = NULL,
selection.index.scale.m = NULL,
selection.index.scale.f = NULL,
selection.index.kinship = 0,
selection.index.gen = NULL,
selection.index.database = NULL,
selection.index.cohorts = NULL,
selection.highest = TRUE,
ignore.best = 0,
best.selection.ratio.m = 1,
best.selection.ratio.f = NULL,
best.selection.criteria.m = "bv",
best.selection.criteria.f = NULL,
best.selection.manual.ratio.m = NULL,
best.selection.manual.ratio.f = NULL,
best.selection.manual.reorder = TRUE,
selection.m.random.prob = NULL,
selection.f.random.prob = NULL,
reduced.selection.panel.m = NULL,
reduced.selection.panel.f = NULL,
threshold.selection.index = NULL,
threshold.selection.value = NULL,
threshold.selection.sign = ">",
threshold.selection.criteria = NULL,
threshold.selection = NULL,
threshold.sign = ">",
remove.duplicates = TRUE,
selection.m.miesenberger = FALSE,
selection.f.miesenberger = NULL,
selection.miesenberger.reliability.est = "derived",
miesenberger.trafo = 0,
sort.selected.pos = FALSE,
ogc = FALSE,
relationship.matrix.ogc = "pedigree",
depth.pedigree.ogc = 7,
ogc.target = "min.sKin",
ogc.uniform = NULL,
ogc.ub = NULL,
ogc.lb = NULL,
ogc.ub.sKin = NULL,
ogc.lb.BV = NULL,
ogc.ub.BV = NULL,
ogc.eq.BV = NULL,
ogc.ub.sKin.increase = NULL,
ogc.lb.BV.increase = NULL,
ogc.c1 = NULL,
ogc.isCandidate = NULL,
ogc.plots = TRUE,
ogc.weight = NULL,
ogc.freq = NULL,
selection.skip = FALSE,
breeding.size = 0,
breeding.size.litter = NULL,
name.cohort = NULL,
breeding.sex = NULL,
breeding.sex.random = FALSE,
sex.s = NULL,
add.gen = 0,
share.genotyped = 0,
phenotyping.child = NULL,
fixed.effects.p = NULL,
fixed.effects.freq = NULL,
new.class = 0L,
max.offspring = Inf,
max.offspring.individual.m = NULL,
max.offspring.individual.f = NULL,
max.offspring.individual.gen = NULL,
max.offspring.individual.database = NULL,
max.offspring.individual.cohorts = NULL,
max.litter = Inf,
max.mating.pair = Inf,
avoid.mating.fullsib = FALSE,
avoid.mating.halfsib = FALSE,
avoid.mating.parent = FALSE,
avoid.mating.inb = NULL,
avoid.mating.inb.quantile = NULL,
avoid.mating.inb.min = 0,
avoid.mating.kinship = NULL,
avoid.mating.kinship.quantile = NULL,
avoid.mating.kinship.gen = NULL,
avoid.mating.kinship.database = NULL,
avoid.mating.kinship.cohorts = NULL,
avoid.mating.kinship.min = 0,
avoid.mating.kinship.median = FALSE,
avoid.mating.depth.pedigree = 7,
avoid.mating.remove = FALSE,
avoid.mating.ignore = 0,
avoid.mating.resampling = 1000,
fixed.breeding = NULL,
fixed.breeding.best = NULL,
fixed.breeding.id = NULL,
fixed.assignment = FALSE,
breeding.all.combination = FALSE,
repeat.mating = NULL,
repeat.mating.copy = NULL,
repeat.mating.fixed = NULL,
repeat.mating.overwrite = TRUE,
repeat.mating.trait = 1,
repeat.mating.max = NULL,
repeat.mating.s = NULL,
same.sex.activ = FALSE,
same.sex.sex = 0.5,
same.sex.selfing = FALSE,
selfing.mating = FALSE,
selfing.sex = 0.5,
dh.mating = FALSE,
dh.sex = 0.5,
combine = FALSE,
copy.individual = FALSE,
copy.individual.m = FALSE,
copy.individual.f = FALSE,
copy.individual.keep.bve = TRUE,
copy.individual.keep.pheno = TRUE,
added.genotyped = NULL,
bv.ignore.traits = NULL,
generation.cores = NULL,
generation.core.make.small = FALSE,
pedigree.error = 0,
pedigree.unknown = 0,
genotyped.database = NULL,
genotyped.gen = NULL,
genotyped.cohorts = NULL,
genotyped.share = 1,
genotyped.array = 1,
genotyped.remove.gen = NULL,
genotyped.remove.database = NULL,
genotyped.remove.cohorts = NULL,
genotyped.remove.all.copy = TRUE,
genotyped.selected = FALSE,
phenotyping = NULL,
phenotyping.gen = NULL,
phenotyping.cohorts = NULL,
phenotyping.database = NULL,
n.observation = NULL,
phenotyping.class = NULL,
heritability = NULL,
repeatability = NULL,
multiple.observation = FALSE,
phenotyping.selected = FALSE,
share.phenotyped = 1,
offpheno.parents.gen = NULL,
offpheno.parents.database = NULL,
offpheno.parents.cohorts = NULL,
offpheno.offspring.gen = NULL,
offpheno.offspring.database = NULL,
offpheno.offspring.cohorts = NULL,
sigma.e = NULL,
sigma.e.gen = NULL,
sigma.e.cohorts = NULL,
sigma.e.database = NULL,
new.residual.correlation = NULL,
new.breeding.correlation = NULL,
phenotyping.trafo.parameter = NULL,
bve = FALSE,
bve.gen = NULL,
bve.cohorts = NULL,
bve.database = NULL,
relationship.matrix = "GBLUP",
depth.pedigree = 7,
singlestep.active = TRUE,
bve.ignore.traits = NULL,
bve.array = NULL,
bve.imputation = TRUE,
bve.imputation.errorrate = 0,
bve.all.genotyped = FALSE,
bve.insert.gen = NULL,
bve.insert.cohorts = NULL,
bve.insert.database = NULL,
variance.correction = "none",
bve.class = NULL,
sigma.g = NULL,
sigma.g.gen = NULL,
sigma.g.cohorts = NULL,
sigma.g.database = NULL,
forecast.sigma.g = NULL,
remove.effect.position = FALSE,
estimate.add.gen.var = FALSE,
estimate.pheno.var = FALSE,
bve.avoid.duplicates = TRUE,
calculate.reliability = FALSE,
estimate.reliability = FALSE,
bve.input.phenotype = "own",
mas.bve = FALSE,
mas.markers = NULL,
mas.number = 5,
mas.effects = NULL,
mas.geno = NULL,
bve.parent.mean = FALSE,
bve.grandparent.mean = FALSE,
bve.mean.between = "bvepheno",
bve.exclude.fixed.effects = NULL,
bve.beta.hat.approx = TRUE,
bve.per.sample.sigma.e = TRUE,
bve.p_i.list = NULL,
bve.p_i.gen = NULL,
bve.p_i.database = NULL,
bve.p_i.cohorts = NULL,
bve.p_i.exclude.nongenotyped = FALSE,
bve.use.all.copy = FALSE,
bve.pedigree.error = TRUE,
mobps.bve = TRUE,
mixblup.bve = FALSE,
blupf90.bve = FALSE,
mixblup.reliability = FALSE,
emmreml.bve = FALSE,
rrblup.bve = FALSE,
sommer.bve = FALSE,
sommer.multi.bve = FALSE,
BGLR.bve = FALSE,
pseudo.bve = FALSE,
pseudo.bve.accuracy = 1,
bve.solve = "exact",
mixblup.jeremie = FALSE,
mixblup.hpblup = FALSE,
mixblup.pedfile = TRUE,
mixblup.parfile = TRUE,
mixblup.datafile = TRUE,
mixblup.inputfile = TRUE,
mixblup.genofile = TRUE,
mixblup.path = NULL,
mixblup.path.pedfile = NULL,
mixblup.path.parfile = NULL,
mixblup.path.datafile = NULL,
mixblup.path.inputfile = NULL,
mixblup.path.genofile = NULL,
mixblup.full.path.genofile = NULL,
mixblup.files = "MiXBLUP_files",
mixblup.verbose = TRUE,
blupf90.verbose = TRUE,
mixblup.genetic.cov = NULL,
mixblup.residual.cov = NULL,
mixblup.lambda = 1,
mixblup.alpha = NULL,
mixblup.beta = NULL,
mixblup.omega = NULL,
mixblup.maxit = 5000,
mixblup.stopcrit = NULL,
mixblup.maf = 0.005,
mixblup.numproc = NULL,
mixblup.apy = FALSE,
mixblup.apy.core = NULL,
mixblup.ta = FALSE,
mixblup.tac = FALSE,
mixblup.skip = FALSE,
blupf90.skip = FALSE,
mixblup.restart = FALSE,
mixblup.nopeek = FALSE,
mixblup.calcinbr.s = FALSE,
mixblup.multiple.records = FALSE,
mixblup.attach = FALSE,
mixblup.debug = FALSE,
mixblup.plink = FALSE,
mixblup.cleanup = Inf,
blupf90.pedfile = TRUE,
blupf90.parfile = TRUE,
blupf90.datafile = TRUE,
blupf90.inputfile = TRUE,
blupf90.genofile = TRUE,
mixblup.dgv = FALSE,
mixblup.dgv.freq = NULL,
mixblup.dgv.effect = NULL,
blupf90.path = NULL,
renumf90.path = NULL,
blupf90.path.pedfile = NULL,
blupf90.path.parfile = NULL,
blupf90.path.datafile = NULL,
blupf90.path.inputfile = NULL,
blupf90.path.genofile = NULL,
blupf90.files = "blupf90_files",
blupf90.blksize = NULL,
blupf90.no.quality = FALSE,
blupf90.conv_crit = NULL,
BGLR.model = "RKHS",
BGLR.burnin = 500,
BGLR.iteration = 5000,
BGLR.print = TRUE,
BGLR.save = "RKHS",
BGLR.save.random = FALSE,
miraculix = NULL,
miraculix.cores = 1,
miraculix.mult = NULL,
miraculix.chol = TRUE,
miraculix.destroyA = TRUE,
estimate.u = FALSE,
fast.uhat = TRUE,
gwas.u = FALSE,
approx.residuals = TRUE,
gwas.gen = NULL,
gwas.cohorts = NULL,
gwas.database = NULL,
gwas.group.standard = FALSE,
y.gwas.used = "pheno",
gene.editing.offspring = FALSE,
gene.editing.best = FALSE,
gene.editing.offspring.sex = TRUE,
gene.editing.best.sex = TRUE,
nr.edits = 0,
culling.non.selected = FALSE,
culling.gen = NULL,
culling.database = NULL,
culling.cohorts = NULL,
culling.type = 0,
culling.time = Inf,
culling.name = "Not_named",
culling.bv1 = 0,
culling.share1 = NULL,
culling.bv2 = NULL,
culling.share2 = NULL,
culling.index = 0,
culling.single = TRUE,
culling.all.copy = TRUE,
mutation.rate = 10^-8,
remutation.rate = 10^-8,
recombination.rate = 1,
recombination.rate.trait = 0,
recombination.function = NULL,
recombination.minimum.distance = NULL,
recombination.distance.penalty = NULL,
recombination.distance.penalty.2 = NULL,
recom.f.indicator = NULL,
import.position.calculation = NULL,
duplication.rate = 0,
duplication.length = 0.01,
duplication.recombination = 1,
gen.architecture.m = 0,
gen.architecture.f = NULL,
add.architecture = NULL,
intern.func = 0,
delete.haplotypes = NULL,
delete.recombi = NULL,
delete.recombi.only.non.genotyped = FALSE,
delete.recombi.class = NULL,
delete.individuals = NULL,
delete.gen = NULL,
delete.sex = 1:2,
delete.same.origin = FALSE,
save.recombination.history = FALSE,
store.sparse = FALSE,
storage.save = 1.05,
verbose = TRUE,
report.accuracy = TRUE,
store.breeding.totals = FALSE,
store.bve.data = FALSE,
store.comp.times = TRUE,
store.comp.times.bve = TRUE,
store.comp.times.generation = TRUE,
store.effect.freq = FALSE,
Rprof = FALSE,
randomSeed = NULL,
display.progress = NULL,
time.point = 0,
age.point = NULL,
creating.type = 0,
import.relationship.matrix = NULL,
export.selected = FALSE,
export.selected.database = FALSE,
export.relationship.matrix = FALSE,
pen.assignments = NULL,
pen.size = NULL,
pen.by.sex = TRUE,
pen.by.litter = FALSE,
pen.size.overwrite = TRUE,
selection.m = NULL,
selection.f = NULL,
new.bv.observation.gen = NULL,
new.bv.observation.cohorts = NULL,
new.bv.observation.database = NULL,
best1.from.group = NULL,
best2.from.group = NULL,
best1.from.cohort = NULL,
best2.from.cohort = NULL,
new.bv.observation = NULL,
reduce.group = NULL,
reduce.group.selection = "random",
new.bv.child = NULL,
computation.A = NULL,
computation.A.ogc = NULL,
new.phenotype.correlation = NULL,
offspring.bve.parents.gen = NULL,
offspring.bve.parents.database = NULL,
offspring.bve.parents.cohorts = NULL,
offspring.bve.offspring.gen = NULL,
offspring.bve.offspring.database = NULL,
offspring.bve.offspring.cohorts = NULL,
input.phenotype = NULL,
multiple.bve.weights.m = 1,
multiple.bve.weights.f = NULL,
multiple.bve.scale.m = "bv_sd",
multiple.bve.scale.f = NULL,
use.recalculate.manual = NULL,
recalculate.manual.subset = 5000,
compute.grandparent.contribution = FALSE,
size.scaling = NULL,
parallel.internal = FALSE,
varg = FALSE,
gain.stats = FALSE,
next.id = NULL,
copy.individual.use = NULL,
copy.individual.use2 = NULL
)
Arguments
population
Population list
selection.size
Number of selected individuals as parents (default: all individuals in selection.m/f.gen/database/gen - alt: positive numbers)
selection.criteria
What to use in the selection process (default: "bve", alt: "bv", "pheno", "random", "offpheno")
selection.m.gen, selection.m.cohorts, selection.m.database
Generations/cohorts/groups available for selection of first/paternal parent
selection.f.gen, selection.f.cohorts, selection.f.database
Generations available for selection of maternal parent
max.selection.fullsib
Maximum number of individual to select from the same family (same sire & dam)
max.selection.halfsib
Maximum number of individual to select from the same family (same sire or same dam)
class.m, class.f
For selection only individuals from this class (included in selection.m/f.gen/database/cohorts) will be considered for selection (default: 0 - which is all individuals if never used class elsewhere)
add.class.cohorts
Initial classes of cohorts used in selection.m/f.cohorts are automatically added to class.m/f (default: TRUE)
multiple.bve
Way to handle multiple traits in selection (default: "add" - use values directly in an index, alt: "ranking" - ignore values but only use ranking per trait)
selection.index.weights.m, selection.index.weights.f
Weighting between traits (default: 1)
selection.index.scale.m, selection.index.scale.f
Default: "bv_sd"; Set to "pheno_sd" when using gains per phenotypic SD, "unit" when using gains per unit, "bve" when using estimated breeding values
selection.index.kinship
Include avg. kinship to a reference population (selection.index.gen/database/cohorts) as part of the selection index (Default: 0)
selection.index.gen, selection.index.database, selection.index.cohorts
Generation/cohorts/groups to use as a reference of the kinship value in the selection index
selection.highest
If FALSE to select individuals with lowest value for the selection criterium (default c(TRUE,TRUE) - (m,w))
ignore.best
Not consider the top individuals of the selected individuals (e.g. to use 2-10 best individuals)
best.selection.ratio.m, best.selection.ratio.f
Ratio of the frequency of the selection of the best best individual and the worst best individual (default=1)
best.selection.criteria.m, best.selection.criteria.f
Criteria to calculate this ratio (default: "bv", alt: "bve", "pheno")
best.selection.manual.ratio.m, best.selection.manual.ratio.f
vector containing probability to draw from for every individual (e.g. c(0.1,0.2,0.7))
best.selection.manual.reorder
Set to FALSE to not use the order from best to worst selected individual but plain order based on database-order
selection.m.random.prob, selection.f.random.prob
Use this parameter to control the probability of each individual to be selected when doing random selection
reduced.selection.panel.m, reduced.selection.panel.f
Use only a subset of individuals of the potential selected ones ("Split in user-interface")
threshold.selection.index
Selection index on which to access (matrix which one index per row)
threshold.selection.value
Minimum value in the selection index selected individuals have to have
threshold.selection.sign
Pick all individuals above (">") the threshold. Alt: ("<", "=", "<=", ">=")
threshold.selection.criteria
Criterium on which to evaluate the index (default: "bve", alt: "bv", "pheno")
threshold.selection
Minimum value in the selection index selected individuals have to have
threshold.sign
Pick all individuals above (">") the threshold. Alt: ("<", "=", "<=", ">=")
remove.duplicates
Set to FALSE to select the same individual multiple times when the gen/database/cohorts for selection contains it multiple times
selection.m.miesenberger, selection.f.miesenberger
Use Weighted selection index according to Miesenberger 1997 for paternal/maternal selection
selection.miesenberger.reliability.est
If available reliability estimated are used. If not use default: "derived" (cor(BVE,BV)^2) , alt: "heritability", "estimated" (SD BVE / SD Pheno) as replacement
miesenberger.trafo
Ignore all eigenvalues below this threshold and apply dimension reduction (default: 0 - use all)
sort.selected.pos
Set to TRUE to arrange selected individuals according to position in the database (not by breeding value)
ogc
If TRUE use optimal genetic contribution theory to perform selection ( This requires the use of the R-package optiSel)
relationship.matrix.ogc
Method to calculate relationship matrix for OGC (Default: "pedigree", alt: "vanRaden", "CE", "non_stand", "CE2", "CM")
depth.pedigree.ogc
Depth of the pedigree in generations (default: 7)
ogc.target
Target of OGC (default: "min.sKin" - minimize inbreeding; alt: "max.BV" / "min.BV" - maximize genetic gain; both under constrains selected below)
ogc.uniform
This corresponds to the uniform constrain in optiSel
ogc.ub
This corresponds to the ub constrain in optiSel
ogc.lb
This corresponds to the lb constrain in optiSel
ogc.ub.sKin
This corresponds to the ub.sKin constrain in optiSel
ogc.lb.BV
This corresponds to the lb.BV constrain in optiSel
ogc.ub.BV
This corresponds to the ub.BV constrain in optiSel
ogc.eq.BV
This corresponds to the eq.BV constrain in optiSel
ogc.ub.sKin.increase
This corresponds to the upper bound (current sKin + ogc.ub.sKin.increase) as ub.sKin in optiSel
ogc.lb.BV.increase
This corresponds to the lower bound (current BV + ogc.lb.BV.increase) as lb.BV in optiSel
ogc.c1
Only applicable when TN-version of OGC is available
ogc.isCandidate
Only applicable when TN-version of OGC is available
ogc.plots
Only applicable when TN-version of OGC is available
ogc.weight
Only applicable when B4F-version of OGC is available
ogc.freq
Only applicable when B4F-version of OGC is available
selection.skip
Set to FALSE in case no selection of individuals should be performed (just skips some unneccessary computations)
breeding.size
Number of individuals to generate (default: 0, use vector with two entries to control offspring per sex)
breeding.size.litter
Number of litters to generate (default: NULL - use breeding.size; only single positive number input allowed)
name.cohort
Name of the newly added cohort
breeding.sex
Share of female individuals (if single value is used for breeding size; default: 0.5)
breeding.sex.random
If TRUE randomly chose sex of new individuals (default: FALSE - use expected values)
sex.s
Specify which newly added individuals are male (1) or female (2)
add.gen
Generation you want to add the new individuals to (default: New generation)
share.genotyped
Share of individuals newly generated individuals that are genotyped (Default: 0). Also applies if individuals are copied with copy.individual
phenotyping.child
Starting phenotypes of newly generated individuals (default: "zero", alt: "mean" of both parents, "obs" - regular observation)
fixed.effects.p
Parametrization for the fixed effects (default: c(0,0..,0), if multiple different parametrizations are possible use a matrix with one parametrization per row)
fixed.effects.freq
Frequency of each different parametrization of the fixed effects
new.class
Migration level of newly generated individuals (default: 0 / use vector for different classes for different sexes)
max.offspring
Maximum number of offspring per individual (default: c(Inf,Inf) - (m,w))
max.offspring.individual.m, max.offspring.individual.f
Vector with maximum number of offspring for first/second parent (default: NULL). Order in the vector by order of selection
max.offspring.individual.gen
matrix with first column generation with limited number offspring, second column number of allowed offspring
max.offspring.individual.database
matrix with first four columns database with limited number offspring, fifth column number of allowed offspring
max.offspring.individual.cohorts
matrix with first column cohort with limited number offspring, second column number of allowed offspring
max.litter
Maximum number of litters per individual (default: c(Inf,Inf) - (m,w))
max.mating.pair
Maximum number of matings between two specific individuals (default: Inf)
avoid.mating.fullsib
Set to TRUE to not generate offspring of full siblings
avoid.mating.halfsib
Set to TRUE to not generate offspring from half or full siblings
avoid.mating.parent
Set to TRUE to not generate offspring from parent / sibling matings
avoid.mating.inb
Maximum allowed expected inbreeding to allow a mating combination (based on kinships)
avoid.mating.inb.quantile
Use this to not perform mating between more related potential parents (quantile of all expected inbreeding levels)
avoid.mating.inb.min, avoid.mating.kinship.min
Share of mating to at minimum perform for each individual (default: 0)
avoid.mating.kinship
Maximum allowed expected kinship of an offspring to a reference group of individuals
avoid.mating.kinship.quantile
Maximum allowed expected kinship of an offspring to a reference group of individuals
avoid.mating.kinship.gen, avoid.mating.kinship.database, avoid.mating.kinship.cohorts
Gen/database/cohorts of individuals to consider as a reference pool in avoid.mating.kinship
avoid.mating.kinship.median
Set to TRUE to use median kinship instead of mean kinship in avoid.mating.kinship (default: FALSE)
avoid.mating.depth.pedigree
Depth of the pedigree to calculate expected inbreeding levels / kinships
avoid.mating.remove
Set to TRUE to automatically exclude any selected individuals from the sample of parents
avoid.mating.ignore
Set to value higher 0 for avoid.mating.inb/kinship restrictions to not always be applied
avoid.mating.resampling
Number of sampling attempts to avoid unwanted matings (( last couple of individuals otherwise can have unwanted relatedness, default = 1000))
fixed.breeding
Set of targeted matings to perform (matrix with 7 columns: database position first parent (gen, sex, nr), database position second parent (gen,sex,nr), likelihood to be female (optional))
fixed.breeding.best
Perform targeted matings in the group of selected individuals (matrix with 5 columns: position first parent (male/female pool of selected individuals, ranking in selected animals), position second parent (male/female pool of selected individuals, ranking in selected animals), likelihood to be female (optional))
fixed.breeding.id
Set of target matings to perform (matrix with 3 columns: id first parent, id second parent, likelihood to be female (optional))
fixed.assignment
Set to "bestbest" / TRUE for targeted mating of best-best individual till worst-worst (of selected). set to "bestworst" for best-worst mating
breeding.all.combination
Set to TRUE to automatically perform each mating combination possible exactly ones.
repeat.mating
Generate multiple mating from the same dam/sire combination (first column: number of offspring; second column: probability)
repeat.mating.copy
Generate multiple copies from a copy action (combine / copy.individual.m/f) (first column: number of offspring; second column: probability)
repeat.mating.fixed
Vector containing number of times each mating is repeated. This will overwrite sampling from repeat.mating / repeat.mating.copy (default: NULL)
repeat.mating.overwrite
Set to FALSE to not use the current repeat.mating / repeat.mating.copy input as the new standard values (default: TRUE)
repeat.mating.trait
Trait that should be linked to the litter size
repeat.mating.max
Maximum number of individuals in a litter
repeat.mating.s
Use this parameter to manually provide the size of each litter generated
same.sex.activ
If TRUE allow matings of individuals of same sex (Sex here is a general term with the first sex referring to the first parent, second sex second parent)
same.sex.sex
Probability to use female individuals as parents (default: 0.5)
same.sex.selfing
Set to TRUE to allow for selfing when using same.sex matings (default: FALSE)
selfing.mating
If TRUE generate new individuals via selfing
selfing.sex
Share of female individuals used for selfing (default: 0.5)
dh.mating
If TRUE generate a DH-line in mating process
dh.sex
Share of DH-lines generated from selected female individuals
combine
Copy existing individuals (e.g. to merge individuals from different groups in a joined cohort). Individuals to use are used as the first parent
copy.individual
Set TRUE to generate a copy of an already existing individual. If only one of the sexes has individuals to select from it will automatically detect with sex to chose. Otherwise the first/male parent will be copied
copy.individual.m, copy.individual.f
If TRUE generate exactly one copy of all selected male/female in a new cohort (or more by setting breeding.size)
copy.individual.keep.bve
Set to FALSE to not keep estimated breeding value in case of use of copying individuals instead of regular meiosis
copy.individual.keep.pheno
Set to FALSE to not keep phenotypes in case of use of copying individuals instead of regular meiosis
added.genotyped
(OLD! use share.genotyped) Share of individuals that is additionally genotyped (only for copy.individual, default: 0)
bv.ignore.traits
Vector of traits to ignore in the calculation of the genomic value (default: NULL; Only recommended for high number of traits and experienced users!)
generation.cores
Number of cores used for the generation of new individuals (This will only be active when generating more than 500 individuals)
generation.core.make.small
Set to TRUE to delete not necessary individuals during parallelization
pedigree.error
Share of errors in the pedigree (default: 0; vector with two entries for errors on male/female side)
pedigree.unknown
Share of individuals with unknown parents (default: 0; vector with two entries for differences in unknown-share between male/female side)
genotyped.gen, genotyped.cohorts, genotyped.database
Generations/cohorts/groups to generate genotype data (that can be used in a BVE)
genotyped.share
Share of individuals in genotyped.gen/database/cohort to generate genotype data from (default: 1)
genotyped.array
Genotyping array used
genotyped.remove.gen, genotyped.remove.database, genotyped.remove.cohorts
Generations/cohorts/groups from which to remove genotyping information (this will affect all copies of an individual unless genotyped.remove.all.copy is set to FALSE)
genotyped.remove.all.copy
Set to FALSE to only change the genotyping state of this particular copy of an individual (default: TRUE)
genotyped.selected
Set to TRUE to genotype all selected individuals
phenotyping
Quick access to phenotyping for (all: "all", non-phenotyped: "non_obs", non-phenotyped male: "non_obs_m", non-phenotyped female: "non_obs_f")
phenotyping.gen, phenotyping.cohorts, phenotyping.database
Generations/cohorts/groups from which to generate additional phenotypes
n.observation
Number of phenotypic observations generated per trait and per individuals (use repeatability to control correlation between observations)
phenotyping.class
Classes of individuals for which to generate phenotypes (default: NULL –> all classes)
heritability
Use sigma.e to obtain a certain heritability (default: NULL)
repeatability
Set this to control the share of the residual variance (sigma.e) that is permanent (there for each observation)
multiple.observation
If an already phenotyped trait is phenotyped again this will on NOT lead to an additional phenotyped observation unless this is set to TRUE
phenotyping.selected
Set to TRUE to phenotype all selected individuals
share.phenotyped
Share of the individuals to phenotype (use vector for different probabilities for different traits)
offpheno.parents.gen, offpheno.parents.database, offpheno.parents.cohorts
Generations/groups/cohorts to consider to derive phenotype from offspring phenotypes
offpheno.offspring.gen, offpheno.offspring.cohorts, offpheno.offspring.database
Active generations/cohorts/groups for import of offspring phenotypes
sigma.e
Enviromental standard deviation (default: use sigma.e from last run / usually fit by use of heritability; if never provided: 10; used in BVE for variance components if manually set)
sigma.e.gen, sigma.e.cohorts, sigma.e.database
Generations/cohorts/groups to consider when estimating sigma.e when using heritability
new.residual.correlation
Correlation of the simulated residual variance
new.breeding.correlation
Correlation of the simulated genetic variance (only impacts non-QTL based traits. Needs to be fit in creating.diploid/trait for QTL-based traits)
phenotyping.trafo.parameter
Additional input parameter for phenotypic transformation function
bve
If TRUE perform a breeding value estimation (default: FALSE)
bve.gen, bve.cohorts, bve.database
Generations/Groups/Cohorts of individuals to consider in breeding value estimation (default: NULL)
relationship.matrix
Method to calculate relationship matrix for the breeding value estimation. This will automatically chosen between GBLUP, ssGBLUP, pBLUP based on if genotyped individuals are available (Default: "GBLUP", alt: "pedigree", "CE", "non_stand", "CE2", "CM")
depth.pedigree
Depth of the pedigree in generations (default: 7)
singlestep.active
Set FALSE remove all individuals without genomic data from the breeding value estimation
bve.ignore.traits
Vector of traits to ignore in the breeding value estimation (default: NULL, use: "zero" to not consider traits with 0 index weight in selection.index.weights.m/.w)
bve.array
Array to use in the breeding value estimation (default: NULL; chose largest possible based on used individuals in BVE)
bve.imputation
Set to FALSE to not perform imputation up to the highest marker density of genotyping data that is available
bve.imputation.errorrate
Share of errors in the imputation procedure (default: 0)
bve.all.genotyped
Set to TRUE to act as if every individual in the breeding value estimation has been genotyped
bve.insert.gen, bve.insert.cohorts, bve.insert.database
Generations/Groups/Cohorts of individuals to compute breeding values for (default: all groups in bve.database)
variance.correction
Correct for "parental.mean" or "generation.mean" in the estimation of sigma.g for BVE / sigma.e estimation (default: "none")
bve.class
Consider only individuals of those class classes in breeding value estimation (default: NULL - use all)
sigma.g
Genetic standard deviation (default: calculated based on individuals in BVE ; used in BVE for variance components if manually set; mostly recommended to be used for non-QTL based traits)
sigma.g.gen, sigma.g.cohorts, sigma.g.database
Generations/cohorts/groups to consider when estimating sigma.g
forecast.sigma.g
Set FALSE to not estimate sigma.g (Default: TRUE // in case sigma.g is set this is automatically set to FALSE)
remove.effect.position
If TRUE remove real QTLs in breeding value estimation
estimate.add.gen.var
If TRUE estimate additive genetic variance and heritability based on parent model
estimate.pheno.var
If TRUE estimate total variance in breeding value estimation
bve.avoid.duplicates
If set to FALSE multiple generations of the same individual can be used in the bve (only possible by using copy.individual to generate individuals)
calculate.reliability
Set TRUE to calculate a reliability when performing Direct-Mixed-Model BVE
estimate.reliability
Set TRUE to estimate the reliability in the BVE by calculating the correlation between estimated and real breeding values
bve.input.phenotype
Select what to use in BVE (default: own phenotype ("own"), offspring phenotype ("off"), their average ("mean") or a weighted average ("weighted"))
mas.bve
If TRUE use marker assisted selection in the breeding value estimation
mas.markers
Vector containing markers to be used in marker assisted selection
mas.number
If no markers are provided this nr of markers is selected (if single marker QTL are present highest effect markers are prioritized)
mas.effects
Effects assigned to the MAS markers (Default: estimated via lm())
mas.geno
Genotype dataset used in MAS (default: NULL, automatic internal calculation)
bve.parent.mean
Set to TRUE to use the average parental performance as the breeding value estimate
bve.grandparent.mean
Set to TRUE to use the average grandparental performance as the breeding value estimate
bve.mean.between
Select if you want to use the "bve", "bv", "pheno" or "bvepheno" to form the mean (default: "bvepheno" - if available bve, else pheno)
bve.exclude.fixed.effects
Vector of fixed effects to ignore in the BVE (default: NULL)
bve.beta.hat.approx
Set to FALSE to use the true underlying value for beta_hat for the fixed effect in the direct BVE model. rrBLUP, BGLR, sommer will always estimate beta_hat.
bve.per.sample.sigma.e
Set to FALSE to deactivate the use of a heritability based on the number of observations generated per sample
bve.p_i.list
Vector of allele frequencies to be used when calculating the genomic relationship matrix (default: calculate them based on Z)
bve.p_i.gen, bve.p_i.database, bve.p_i.cohorts
Generations/cohorts/groups to use when manually calculating allele frequencies for genomic relationship matrix
bve.p_i.exclude.nongenotyped
Set to TRUE to exclude non-genotyped individuals when calculating allele frequencies for genomic relationship matrix standardization
bve.use.all.copy
Set to TRUE to use phenotypes and genotyped status from all copies of an individual instead of just the provided ones in the bve.gen/database/cohorts (default: FALSE)
bve.pedigree.error
Set to FALSE to ignore/correct for any pedigree errors
mobps.bve
If TRUE predict BVEs in direct estimation with assumed known heritability (default: TRUE; activating use of any other BVE method to TRUE will overwrite this)
mixblup.bve
Set to TRUE to activate breeding value estimation via MiXBLUP (requires MiXBLUP license!)
blupf90.bve
Set to TRUE to activate breeding value estimation via BLUPF90 (requires blupf90 software!)
mixblup.reliability
Set to TRUE to activate breeding value estimation via MiXBLUP (requires MiXBLUP license!)
emmreml.bve
If TRUE use REML estimator from R-package EMMREML in breeding value estimation
rrblup.bve
If TRUE use REML estimator from R-package rrBLUP in breeding value estimation
sommer.bve
If TRUE use REML estimator from R-package sommer in breeding value estimation
sommer.multi.bve
Set TRUE to use a multi-trait model in the R-package sommer for BVE
BGLR.bve
If TRUE use BGLR to perform breeding value estimation
pseudo.bve
If set to TRUE the breeding value estimation will be simulated with resulting accuracy pseudo.bve.accuracy (default: 1)
pseudo.bve.accuracy
The accuracy to be obtained in the "pseudo" - breeding value estimation
bve.solve
Provide solver to be used in BVE (default: "exact" solution via inversion, alt: "pcg", function with inputs A, b and output y_hat)
mixblup.jeremie
Set to TRUE to use Jeremies suggested MiXBLUP settings
mixblup.hpblup
Set to TRUE to use hpblup in MiXBLUP (default: FALSE)
mixblup.pedfile
Set to FALSE to manually generate your MiXBLUP pedfile
mixblup.parfile
Set to FALSE to manually generate your MiXBLUP parfile
mixblup.datafile
Set to FALSE to manually write your MiXBLUP datafile
mixblup.inputfile
Set to FALSE to manually write your MiXBLUP inputfile
mixblup.genofile
Set to FALSE to manually write the MiXBLUP genotypefile
mixblup.path
Provide path to MiXBLUP.exe (default is your working directory: Windows: MixBLUP; Linux ./MixBLUP.exe)
mixblup.path.pedfile
Path from where to import the MiXBLUP pedfile
mixblup.path.parfile
Path from where to import the MiXBLUP parfile
mixblup.path.datafile
Path from where to import the MiXBLUP datafile
mixblup.path.inputfile
Path from where to import the MiXBLUP inputfile
mixblup.path.genofile
Path from where to import the MiXBLUP genofile
mixblup.full.path.genofile
Path from where to import the MiXBLUP genofile
mixblup.files
Directory to generate all files generated when using MiXBLUP (default: MiXBLUP_files/ )
mixblup.verbose
Set to TRUE to display MiXBLUP prints
blupf90.verbose
Set to TRUE to display blupf90 prints
mixblup.genetic.cov
Provide genetic covariance matrix to be used in MiXBLUP (lower-triangle is sufficent) (default: underlying true values)
mixblup.residual.cov
Provide residual covariance matrix to be used in MiXBLUP (lower-triangle is sufficent) (default: underlying true values)
mixblup.lambda
Lambda parameter in MiXBLUP (default: 1)
mixblup.alpha
Alpha parameter in MiXBLUP (default: 0.95, with alpha + beta = 1 , warning: MiXBLUP software this is 1)
mixblup.beta
Beta parameter in MiXBLUP (default: 0.05, with alpha + beta = 1 , warning: MiXBLUP software this is 0)
mixblup.omega
Omega parameter in MiXBLUP (default: mixblup.lambda)
mixblup.maxit
!Maxit qualifier in MiXBLUP (default: 5.000)
mixblup.stopcrit
!STOPCRIT qualifier in MiXBLUP (default: not used, suggested value 1.E-4 for ssGBLUP) // will overwrite maxit
mixblup.maf
!MAF qualifier in MiXBLUP (default: 0.005)
mixblup.numproc
Numproc parameter in MiXBLUP (default: not set // 1)
mixblup.apy
Set to TRUE to use APY inverse in MiXBLUP (default: FALSE)
mixblup.apy.core
Number of core individuals in the APY algorithm (default: 5000)
mixblup.ta
Set to TRUE to use the !Ta flag in MixBLUP
mixblup.tac
Set to TRUE to use the !TAC flag in MixBLUP
mixblup.skip
Set to TRUE to skip the actually system call to MiXBLUP and only write the MiXBLUP files
blupf90.skip
Set to TRUE to skip the actually system calls of blupf90 and only write the blupf90 input files
mixblup.restart
Set to TRUE to set the !RESTART flag in MiXBLUP (requires a "Solunf" file in the working directory)
mixblup.nopeek
Set to TRUE to set the !NOPEEK flag in MiXBLUP
mixblup.calcinbr.s
Set to TRUE to set the !CalcInbr flag to S
mixblup.multiple.records
Set to TRUE to write multiple phenotypic records for an individual
mixblup.attach
Set TRUE to just extent the existing genotype file instead of writting it completely new
mixblup.debug
Set TRUE to set debugging flags for mixblup call (-Dmst > mixblup_debug.log) (default: FALSE)
mixblup.plink
Set TRUE to write genotype files in PLINK format (requires R-package genio, default: FALSE)
mixblup.cleanup
Delete all mixblup output files above the indicated size after MiXBLUP run completes (default: Inf)
blupf90.pedfile
Set to FALSE to manually generate your MiXBLUP pedfile
blupf90.parfile
Set to FALSE to manually generate your MiXBLUP parfile
blupf90.datafile
Set to FALSE to manually generate your blupf90 datafile
blupf90.inputfile
Set to FALSE to manually write your MiXBLUP inputfile
blupf90.genofile
Set to FALSE to manually write the blupf90 genotypefile
mixblup.dgv
Set TRUE to use DGV-PBLUP (Only applicable with TAC-BLUP)
mixblup.dgv.freq
Path of allele frequency file for DGV-PBLUP
mixblup.dgv.effect
Path of SNP effect file for DGV-PBLUP
blupf90.path
Provide path to blupf90 (default is your working directory: Windows: ./blupf90+.exe ; Linux ./blupf90+.exe)
renumf90.path
Provide path to blupf90 (default is your working directory: Windows: ./renumf90.exe ; Linux ./renumf90.exe)
blupf90.path.pedfile
Path from where to import the blupf90 pedfile
blupf90.path.parfile
Path from where to import the blupf90 parfile
blupf90.path.datafile
Path from where to import the blupf90 data file
blupf90.path.inputfile
Path from where to import the blupf90 inputfile
blupf90.path.genofile
Path from where to import the blupf90 genotype file
blupf90.files
Directory to generate all files generated when using blupf90 (default: blupf90_files/ )
blupf90.blksize
blupf90 parameter blksize (Default: number of traits)
blupf90.no.quality
blupf90 setting OPTION no_quality_control (Default: FALSE)
blupf90.conv_crit
blupf90 parameter conv_crit (Default: blupf90 default)
BGLR.model
Select which BGLR model to use (default: "RKHS", alt: "BRR", "BL", "BayesA", "BayesB", "BayesC")
BGLR.burnin
Number of burn-in steps in BGLR (default: 1000)
BGLR.iteration
Number of iterations in BGLR (default: 5000)
BGLR.print
If TRUE set verbose to TRUE in BGLR
BGLR.save
Method to use in BGLR (default: "RKHS" - alt: NON currently)
BGLR.save.random
Add random number to store location of internal BGLR computations (only needed when simulating a lot in parallel!)
miraculix
If TRUE use miraculix to perform computations (ideally already generate population in creating.diploid with this; default: automatic detection from population list)
miraculix.cores
Number of cores used in miraculix applications (default: 1)
miraculix.mult
If TRUE use miraculix for matrix multiplications even if miraculix is not used for storage
miraculix.chol
Set to FALSE to deactive miraculix based Cholesky-decomposition (default: TRUE)
miraculix.destroyA
If FALSE A will not be destroyed in the process of inversion (less computing / more memory)
estimate.u
If TRUE estimate u in breeding value estimation (Y = Xb + Zu + e)
fast.uhat
Set to FALSE to derive inverse of A in rrBLUP (only required when this becomes numerical unstable otherwise)
gwas.u
If TRUE estimate u via GWAS (relevant for gene editing)
approx.residuals
If FALSE calculate the variance for each marker separatly instead of using a set variance (does not change order - only p-values)
gwas.gen, gwas.cohorts, gwas.database
Generations/cohorts/groups to consider in GWAS analysis
gwas.group.standard
If TRUE standardize phenotypes by group mean
y.gwas.used
What y value to use in GWAS study (Default: "pheno", alt: "bv", "bve")
gene.editing.offspring
If TRUE perform gene editing on newly generated individuals
gene.editing.best
If TRUE perform gene editing on selected individuals
gene.editing.offspring.sex
Which sex to perform editing on (Default c(TRUE,TRUE), mw)
gene.editing.best.sex
Which sex to perform editing on (Default c(TRUE,TRUE), mw)
nr.edits
Number of edits to perform per individual
culling.non.selected
Set TRUE to cull all non-selected individuals (default: FALSE)
culling.gen, culling.cohorts, culling.database
Generations/cohorst/groups to consider to culling
culling.type
Default: 0, can be set to code different type of culling reasons (e.g. 0 - aging, 1 - selection, 2 - health)
culling.time
Age of the individuals at culling // use time.point if the age of individuals is variable and culling is executed on individuals of different ages culled at the same time
culling.name
Name of the culling action (user-interface stuff)
culling.bv1
Reference Breeding value
culling.share1
Probability of death for individuals with bv1
culling.bv2
Alternative breeding value (linear extended for other bvs)
culling.share2
Probability of death for individuals with bv2
culling.index
Genomic index (default:0 - no genomic impact, use: "lastindex" to use the last selection index applied in selection)
culling.single
Set to FALSE to not apply the culling module on all individuals of the cohort
culling.all.copy
Set to FALSE to not kill copies of the same individual in the culling module
mutation.rate
Mutation rate in each marker (default: 10^-8)
remutation.rate
Remutation rate in each marker (default: 10^-8)
recombination.rate
Average number of recombination per 1 length unit (default: 1M)
recombination.rate.trait
Select a trait which BV will be used as a scalar for the expected number of recombination (default: 0)
recombination.function
Function used to calculate position of recombination events (default: MoBPS::recombination.function.haldane())
recombination.minimum.distance
Minimum distance between two points of recombination (default: 0)
recombination.distance.penalty
Reduced probability for recombination events closer than this value - linear penalty (default: 0)
recombination.distance.penalty.2
Reduced probability for recombination events closer than this value - quadratic penalty (default: 0)
recom.f.indicator
Use step function for recombination map (transform snp.positions if possible instead)
import.position.calculation
Function to calculate recombination point into adjacent/following SNP
duplication.rate
Share of recombination points with a duplication (default: 0 - DEACTIVATED)
duplication.length
Average length of a duplication (Exponentially distributed)
duplication.recombination
Average number of recombinations per 1 length uit of duplication (default: 1)
gen.architecture.m, gen.architecture.f
Genetic architecture for male/female individuals (default: 0 - no transformation)
add.architecture
List with two vectors containing (A: length of chromosomes, B: position in cM of SNPs)
intern.func
Chose which function will be used for simulation of meiosis (default: 0, alt: 1,2) - can be faster for specific cases
delete.haplotypes
Generations for with haplotypes of founders can be deleted from population list for memory reduction (default: NULL)
delete.recombi
Generations for which recombination points can be deleted from the population list for memory reduction (default: NULL)
delete.recombi.only.non.genotyped
Set TRUE to only remove points of recombination for non-genotyped individuals
delete.recombi.class
Set TRUE to only remove points of recombination for individuals from a specific class
delete.individuals
Generations for with individuals are completely deleted from population list for memory reduction (default: NULL)
delete.gen
Generations to entirely deleted fro population list for memory reduction (default: NULL)
delete.sex
Remove all individuals from these sex from generation delete.individuals (default: 1:2 ; note:delete individuals=NULL)
delete.same.origin
If TRUE delete recombination points when genetic origin of adjacent segments is the same
save.recombination.history
If TRUE store the time point of each recombination event
store.sparse
Set to TRUE to store the pedigree relationship matrix as a sparse matrix
storage.save
Lower numbers will lead to less memory but slightly higher computing time for calculation of the pedigree relationship matrix (default: 1.5, min: 1)
verbose
Set to FALSE to not display any prints
report.accuracy
Report the accuracy of the breeding value estimation
store.breeding.totals
If TRUE store information on selected individuals in $info$breeding.totals (default: FALSE)
store.bve.data
If TRUE store information of bve in $info$bve.data
store.comp.times
If TRUE store computation times in $info$comp.times.general (default: TRUE)
store.comp.times.bve
If TRUE store computation times of breeding value estimation in $info$comp.times.bve (default: TRUE)
store.comp.times.generation
If TRUE store computation times of mating simulations in $info$comp.times.generation (default: TRUE)
store.effect.freq
If TRUE store the allele frequency of effect markers per generation
Rprof
Store computation times of each function
randomSeed
Set random seed of the process
display.progress
Set FALSE to not display progress bars. Setting verbose to FALSE will automatically deactive progress bars
time.point
Time point at which the new individuals are generated
age.point
Time point at which the new individuals are born (default: time.point - mostly useful in the founder generation)
creating.type
Technique to generate new individuals (use mostly intended for web-based application)
import.relationship.matrix
Input the wanted relationship matrix with this parameter (default: NULL - relationship matrix will be calculated from other sources)
export.selected
Set to TRUE to export the list of selected individuals
export.selected.database
Set to TRUE to export a database of the selected individuals
export.relationship.matrix
Export the relationship matrix used in the breeding value estimation
pen.assignments
This is a placeholder to deactivate this module for now
pen.size
Pen size. When different types of pen are used: use a matrix with two columns coding Number of individuals per pen, Probability for each pen size
pen.by.sex
Only individuals of the same sex are put in the same pen (default: TRUE)
pen.by.litter
Only individuals of the same litter are put in the same pen (default: FALSE)
pen.size.overwrite
Set to FALSE to not use the input for pen.size for down-stream use of breeding.diploid (default: TRUE)
selection.m, selection.f
(OLD! use selection criteria) Selection criteria for male/female individuals (Set to "random" to randomly select individuals - default: "function" based on selection.criteria ((usually breeding values)))
new.bv.observation.gen, new.bv.observation.cohorts, new.bv.observation.database
(OLD! use phenotyping.gen/cohorts/database) Vector of generation from which to generate additional phenotypes
best1.from.group, best1.from.cohort
(OLD!- use selection.m.database/cohorts) Groups of individuals to consider as First Parent / Father (also female individuals are possible)
best2.from.group, best2.from.cohort
(OLD!- use selection.f.database/cohorts) Groups of individuals to consider as Second Parent / Mother (also male individuals are possible)
new.bv.observation
(OLD! - use phenotyping) Quick access to phenotyping for (all: "all", non-phenotyped: "non_obs", non-phenotyped male: "non_obs_m", non-phenotyped female: "non_obs_f")
reduce.group
(OLD! - use culling modules) Groups of individuals for reduce to a new size (by changing class to -1)
reduce.group.selection
(OLD! - use culling modules) Selection criteria for reduction of groups (cf. selection.m / selection.f - default: "random")
new.bv.child
(OLD! - use phenotyping.child) Starting phenotypes of newly generated individuals (default: "zero", alt: "mean" of both parents, "obs" - regular observation)
computation.A
(OLD! - use relationship.matrix) Method to calculate relationship matrix for the breeding value estimation (Default: "vanRaden", alt: "pedigree", "CE", "non_stand", "CE2", "CM")
computation.A.ogc
(OLD! use relationship.matrix.ogc) Method to calculate pedigree matrix in OGC (Default: "pedigree", alt: "vanRaden", "CE", "non_stand", "CE2", "CM")
new.phenotype.correlation
(OLD! - use new.residual.correlation!) Correlation of the simulated enviromental variance
offspring.bve.parents.gen, offspring.bve.parents.cohorts, offspring.bve.parents.database
(OLD! use offpheno.parents.gen/database/cohorts) Generations/cohorts/groups to consider to derive phenotype from offspring phenotypes
offspring.bve.offspring.gen, offspring.bve.offspring.cohorts, offspring.bve.offspring.database
(OLD! use offpheno.offspring.gen/database/cohorts) Active generations/cohorts/groups for import of offspring phenotypes
input.phenotype
(OLD! use bve.input.phenotype) Select what to use in BVE (default: own phenotype ("own"), offspring phenotype ("off"), their average ("mean") or a weighted average ("weighted"))
multiple.bve.weights.m, multiple.bve.weights.f
(OLD! use selection.index.weights.m/f) Weighting between traits (default: 1)
multiple.bve.scale.m, multiple.bve.scale.f
(OLD! use selection.index.scale.m/f) Default: "bv_sd"; Set to "pheno_sd" when using gains per phenotypic SD, "unit" when using gains per unit, "bve" when using estimated breeding values
use.recalculate.manual
Set to TRUE to use recalculate.manual to calculate genomic values (all individuals and traits jointly, default: FALSE)
recalculate.manual.subset
Maximum number of individuals to process at the same time (( genotypes are in memory ))
compute.grandparent.contribution
compute share of genome inherited from each grandparent based on recombination points (default: FALSE)
size.scaling
Set to value to scale all input for breeding.size / selection.size (This will not work for all breeding programs / less general than json.simulation)
parallel.internal
Internal parameter for the parallelization
varg
Experimental parameter for Tobias Niehoff (do not touch!)
gain.stats
Set to FALSE to not compute genetic gains compared to previous generation (selection)
next.id
Id to assign to first next individual generated
copy.individual.use, copy.individual.use2
Use this to skip copying some entries from the internal storage ((minor speed up))
Value
Population-list
Examples
population <- creating.diploid(nsnp=1000, nindi=100)
population <- breeding.diploid(population, breeding.size=100, selection.size=c(25,25))
MoBPS documentation built on Nov. 5, 2025, 6:26 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
View source: R/breeding.diploid.R
| breeding.diploid | R Documentation |
Breeding function
Description
Function to simulate a step in a breeding scheme
Usage
breeding.diploid(
population,
selection.size = 0,
selection.criteria = NULL,
selection.m.gen = NULL,
selection.f.gen = NULL,
selection.m.database = NULL,
selection.f.database = NULL,
selection.m.cohorts = NULL,
selection.f.cohorts = NULL,
max.selection.fullsib = Inf,
max.selection.halfsib = Inf,
class.m = 0,
class.f = 0,
add.class.cohorts = TRUE,
multiple.bve = "add",
selection.index.weights.m = NULL,
selection.index.weights.f = NULL,
selection.index.scale.m = NULL,
selection.index.scale.f = NULL,
selection.index.kinship = 0,
selection.index.gen = NULL,
selection.index.database = NULL,
selection.index.cohorts = NULL,
selection.highest = TRUE,
ignore.best = 0,
best.selection.ratio.m = 1,
best.selection.ratio.f = NULL,
best.selection.criteria.m = "bv",
best.selection.criteria.f = NULL,
best.selection.manual.ratio.m = NULL,
best.selection.manual.ratio.f = NULL,
best.selection.manual.reorder = TRUE,
selection.m.random.prob = NULL,
selection.f.random.prob = NULL,
reduced.selection.panel.m = NULL,
reduced.selection.panel.f = NULL,
threshold.selection.index = NULL,
threshold.selection.value = NULL,
threshold.selection.sign = ">",
threshold.selection.criteria = NULL,
threshold.selection = NULL,
threshold.sign = ">",
remove.duplicates = TRUE,
selection.m.miesenberger = FALSE,
selection.f.miesenberger = NULL,
selection.miesenberger.reliability.est = "derived",
miesenberger.trafo = 0,
sort.selected.pos = FALSE,
ogc = FALSE,
relationship.matrix.ogc = "pedigree",
depth.pedigree.ogc = 7,
ogc.target = "min.sKin",
ogc.uniform = NULL,
ogc.ub = NULL,
ogc.lb = NULL,
ogc.ub.sKin = NULL,
ogc.lb.BV = NULL,
ogc.ub.BV = NULL,
ogc.eq.BV = NULL,
ogc.ub.sKin.increase = NULL,
ogc.lb.BV.increase = NULL,
ogc.c1 = NULL,
ogc.isCandidate = NULL,
ogc.plots = TRUE,
ogc.weight = NULL,
ogc.freq = NULL,
selection.skip = FALSE,
breeding.size = 0,
breeding.size.litter = NULL,
name.cohort = NULL,
breeding.sex = NULL,
breeding.sex.random = FALSE,
sex.s = NULL,
add.gen = 0,
share.genotyped = 0,
phenotyping.child = NULL,
fixed.effects.p = NULL,
fixed.effects.freq = NULL,
new.class = 0L,
max.offspring = Inf,
max.offspring.individual.m = NULL,
max.offspring.individual.f = NULL,
max.offspring.individual.gen = NULL,
max.offspring.individual.database = NULL,
max.offspring.individual.cohorts = NULL,
max.litter = Inf,
max.mating.pair = Inf,
avoid.mating.fullsib = FALSE,
avoid.mating.halfsib = FALSE,
avoid.mating.parent = FALSE,
avoid.mating.inb = NULL,
avoid.mating.inb.quantile = NULL,
avoid.mating.inb.min = 0,
avoid.mating.kinship = NULL,
avoid.mating.kinship.quantile = NULL,
avoid.mating.kinship.gen = NULL,
avoid.mating.kinship.database = NULL,
avoid.mating.kinship.cohorts = NULL,
avoid.mating.kinship.min = 0,
avoid.mating.kinship.median = FALSE,
avoid.mating.depth.pedigree = 7,
avoid.mating.remove = FALSE,
avoid.mating.ignore = 0,
avoid.mating.resampling = 1000,
fixed.breeding = NULL,
fixed.breeding.best = NULL,
fixed.breeding.id = NULL,
fixed.assignment = FALSE,
breeding.all.combination = FALSE,
repeat.mating = NULL,
repeat.mating.copy = NULL,
repeat.mating.fixed = NULL,
repeat.mating.overwrite = TRUE,
repeat.mating.trait = 1,
repeat.mating.max = NULL,
repeat.mating.s = NULL,
same.sex.activ = FALSE,
same.sex.sex = 0.5,
same.sex.selfing = FALSE,
selfing.mating = FALSE,
selfing.sex = 0.5,
dh.mating = FALSE,
dh.sex = 0.5,
combine = FALSE,
copy.individual = FALSE,
copy.individual.m = FALSE,
copy.individual.f = FALSE,
copy.individual.keep.bve = TRUE,
copy.individual.keep.pheno = TRUE,
added.genotyped = NULL,
bv.ignore.traits = NULL,
generation.cores = NULL,
generation.core.make.small = FALSE,
pedigree.error = 0,
pedigree.unknown = 0,
genotyped.database = NULL,
genotyped.gen = NULL,
genotyped.cohorts = NULL,
genotyped.share = 1,
genotyped.array = 1,
genotyped.remove.gen = NULL,
genotyped.remove.database = NULL,
genotyped.remove.cohorts = NULL,
genotyped.remove.all.copy = TRUE,
genotyped.selected = FALSE,
phenotyping = NULL,
phenotyping.gen = NULL,
phenotyping.cohorts = NULL,
phenotyping.database = NULL,
n.observation = NULL,
phenotyping.class = NULL,
heritability = NULL,
repeatability = NULL,
multiple.observation = FALSE,
phenotyping.selected = FALSE,
share.phenotyped = 1,
offpheno.parents.gen = NULL,
offpheno.parents.database = NULL,
offpheno.parents.cohorts = NULL,
offpheno.offspring.gen = NULL,
offpheno.offspring.database = NULL,
offpheno.offspring.cohorts = NULL,
sigma.e = NULL,
sigma.e.gen = NULL,
sigma.e.cohorts = NULL,
sigma.e.database = NULL,
new.residual.correlation = NULL,
new.breeding.correlation = NULL,
phenotyping.trafo.parameter = NULL,
bve = FALSE,
bve.gen = NULL,
bve.cohorts = NULL,
bve.database = NULL,
relationship.matrix = "GBLUP",
depth.pedigree = 7,
singlestep.active = TRUE,
bve.ignore.traits = NULL,
bve.array = NULL,
bve.imputation = TRUE,
bve.imputation.errorrate = 0,
bve.all.genotyped = FALSE,
bve.insert.gen = NULL,
bve.insert.cohorts = NULL,
bve.insert.database = NULL,
variance.correction = "none",
bve.class = NULL,
sigma.g = NULL,
sigma.g.gen = NULL,
sigma.g.cohorts = NULL,
sigma.g.database = NULL,
forecast.sigma.g = NULL,
remove.effect.position = FALSE,
estimate.add.gen.var = FALSE,
estimate.pheno.var = FALSE,
bve.avoid.duplicates = TRUE,
calculate.reliability = FALSE,
estimate.reliability = FALSE,
bve.input.phenotype = "own",
mas.bve = FALSE,
mas.markers = NULL,
mas.number = 5,
mas.effects = NULL,
mas.geno = NULL,
bve.parent.mean = FALSE,
bve.grandparent.mean = FALSE,
bve.mean.between = "bvepheno",
bve.exclude.fixed.effects = NULL,
bve.beta.hat.approx = TRUE,
bve.per.sample.sigma.e = TRUE,
bve.p_i.list = NULL,
bve.p_i.gen = NULL,
bve.p_i.database = NULL,
bve.p_i.cohorts = NULL,
bve.p_i.exclude.nongenotyped = FALSE,
bve.use.all.copy = FALSE,
bve.pedigree.error = TRUE,
mobps.bve = TRUE,
mixblup.bve = FALSE,
blupf90.bve = FALSE,
mixblup.reliability = FALSE,
emmreml.bve = FALSE,
rrblup.bve = FALSE,
sommer.bve = FALSE,
sommer.multi.bve = FALSE,
BGLR.bve = FALSE,
pseudo.bve = FALSE,
pseudo.bve.accuracy = 1,
bve.solve = "exact",
mixblup.jeremie = FALSE,
mixblup.hpblup = FALSE,
mixblup.pedfile = TRUE,
mixblup.parfile = TRUE,
mixblup.datafile = TRUE,
mixblup.inputfile = TRUE,
mixblup.genofile = TRUE,
mixblup.path = NULL,
mixblup.path.pedfile = NULL,
mixblup.path.parfile = NULL,
mixblup.path.datafile = NULL,
mixblup.path.inputfile = NULL,
mixblup.path.genofile = NULL,
mixblup.full.path.genofile = NULL,
mixblup.files = "MiXBLUP_files",
mixblup.verbose = TRUE,
blupf90.verbose = TRUE,
mixblup.genetic.cov = NULL,
mixblup.residual.cov = NULL,
mixblup.lambda = 1,
mixblup.alpha = NULL,
mixblup.beta = NULL,
mixblup.omega = NULL,
mixblup.maxit = 5000,
mixblup.stopcrit = NULL,
mixblup.maf = 0.005,
mixblup.numproc = NULL,
mixblup.apy = FALSE,
mixblup.apy.core = NULL,
mixblup.ta = FALSE,
mixblup.tac = FALSE,
mixblup.skip = FALSE,
blupf90.skip = FALSE,
mixblup.restart = FALSE,
mixblup.nopeek = FALSE,
mixblup.calcinbr.s = FALSE,
mixblup.multiple.records = FALSE,
mixblup.attach = FALSE,
mixblup.debug = FALSE,
mixblup.plink = FALSE,
mixblup.cleanup = Inf,
blupf90.pedfile = TRUE,
blupf90.parfile = TRUE,
blupf90.datafile = TRUE,
blupf90.inputfile = TRUE,
blupf90.genofile = TRUE,
mixblup.dgv = FALSE,
mixblup.dgv.freq = NULL,
mixblup.dgv.effect = NULL,
blupf90.path = NULL,
renumf90.path = NULL,
blupf90.path.pedfile = NULL,
blupf90.path.parfile = NULL,
blupf90.path.datafile = NULL,
blupf90.path.inputfile = NULL,
blupf90.path.genofile = NULL,
blupf90.files = "blupf90_files",
blupf90.blksize = NULL,
blupf90.no.quality = FALSE,
blupf90.conv_crit = NULL,
BGLR.model = "RKHS",
BGLR.burnin = 500,
BGLR.iteration = 5000,
BGLR.print = TRUE,
BGLR.save = "RKHS",
BGLR.save.random = FALSE,
miraculix = NULL,
miraculix.cores = 1,
miraculix.mult = NULL,
miraculix.chol = TRUE,
miraculix.destroyA = TRUE,
estimate.u = FALSE,
fast.uhat = TRUE,
gwas.u = FALSE,
approx.residuals = TRUE,
gwas.gen = NULL,
gwas.cohorts = NULL,
gwas.database = NULL,
gwas.group.standard = FALSE,
y.gwas.used = "pheno",
gene.editing.offspring = FALSE,
gene.editing.best = FALSE,
gene.editing.offspring.sex = TRUE,
gene.editing.best.sex = TRUE,
nr.edits = 0,
culling.non.selected = FALSE,
culling.gen = NULL,
culling.database = NULL,
culling.cohorts = NULL,
culling.type = 0,
culling.time = Inf,
culling.name = "Not_named",
culling.bv1 = 0,
culling.share1 = NULL,
culling.bv2 = NULL,
culling.share2 = NULL,
culling.index = 0,
culling.single = TRUE,
culling.all.copy = TRUE,
mutation.rate = 10^-8,
remutation.rate = 10^-8,
recombination.rate = 1,
recombination.rate.trait = 0,
recombination.function = NULL,
recombination.minimum.distance = NULL,
recombination.distance.penalty = NULL,
recombination.distance.penalty.2 = NULL,
recom.f.indicator = NULL,
import.position.calculation = NULL,
duplication.rate = 0,
duplication.length = 0.01,
duplication.recombination = 1,
gen.architecture.m = 0,
gen.architecture.f = NULL,
add.architecture = NULL,
intern.func = 0,
delete.haplotypes = NULL,
delete.recombi = NULL,
delete.recombi.only.non.genotyped = FALSE,
delete.recombi.class = NULL,
delete.individuals = NULL,
delete.gen = NULL,
delete.sex = 1:2,
delete.same.origin = FALSE,
save.recombination.history = FALSE,
store.sparse = FALSE,
storage.save = 1.05,
verbose = TRUE,
report.accuracy = TRUE,
store.breeding.totals = FALSE,
store.bve.data = FALSE,
store.comp.times = TRUE,
store.comp.times.bve = TRUE,
store.comp.times.generation = TRUE,
store.effect.freq = FALSE,
Rprof = FALSE,
randomSeed = NULL,
display.progress = NULL,
time.point = 0,
age.point = NULL,
creating.type = 0,
import.relationship.matrix = NULL,
export.selected = FALSE,
export.selected.database = FALSE,
export.relationship.matrix = FALSE,
pen.assignments = NULL,
pen.size = NULL,
pen.by.sex = TRUE,
pen.by.litter = FALSE,
pen.size.overwrite = TRUE,
selection.m = NULL,
selection.f = NULL,
new.bv.observation.gen = NULL,
new.bv.observation.cohorts = NULL,
new.bv.observation.database = NULL,
best1.from.group = NULL,
best2.from.group = NULL,
best1.from.cohort = NULL,
best2.from.cohort = NULL,
new.bv.observation = NULL,
reduce.group = NULL,
reduce.group.selection = "random",
new.bv.child = NULL,
computation.A = NULL,
computation.A.ogc = NULL,
new.phenotype.correlation = NULL,
offspring.bve.parents.gen = NULL,
offspring.bve.parents.database = NULL,
offspring.bve.parents.cohorts = NULL,
offspring.bve.offspring.gen = NULL,
offspring.bve.offspring.database = NULL,
offspring.bve.offspring.cohorts = NULL,
input.phenotype = NULL,
multiple.bve.weights.m = 1,
multiple.bve.weights.f = NULL,
multiple.bve.scale.m = "bv_sd",
multiple.bve.scale.f = NULL,
use.recalculate.manual = NULL,
recalculate.manual.subset = 5000,
compute.grandparent.contribution = FALSE,
size.scaling = NULL,
parallel.internal = FALSE,
varg = FALSE,
gain.stats = FALSE,
next.id = NULL,
copy.individual.use = NULL,
copy.individual.use2 = NULL
)
Arguments
population |
Population list |
selection.size |
Number of selected individuals as parents (default: all individuals in selection.m/f.gen/database/gen - alt: positive numbers) |
selection.criteria |
What to use in the selection process (default: "bve", alt: "bv", "pheno", "random", "offpheno") |
selection.m.gen, selection.m.cohorts, selection.m.database |
Generations/cohorts/groups available for selection of first/paternal parent |
selection.f.gen, selection.f.cohorts, selection.f.database |
Generations available for selection of maternal parent |
max.selection.fullsib |
Maximum number of individual to select from the same family (same sire & dam) |
max.selection.halfsib |
Maximum number of individual to select from the same family (same sire or same dam) |
class.m, class.f |
For selection only individuals from this class (included in selection.m/f.gen/database/cohorts) will be considered for selection (default: 0 - which is all individuals if never used class elsewhere) |
add.class.cohorts |
Initial classes of cohorts used in selection.m/f.cohorts are automatically added to class.m/f (default: TRUE) |
multiple.bve |
Way to handle multiple traits in selection (default: "add" - use values directly in an index, alt: "ranking" - ignore values but only use ranking per trait) |
selection.index.weights.m, selection.index.weights.f |
Weighting between traits (default: 1) |
selection.index.scale.m, selection.index.scale.f |
Default: "bv_sd"; Set to "pheno_sd" when using gains per phenotypic SD, "unit" when using gains per unit, "bve" when using estimated breeding values |
selection.index.kinship |
Include avg. kinship to a reference population (selection.index.gen/database/cohorts) as part of the selection index (Default: 0) |
selection.index.gen, selection.index.database, selection.index.cohorts |
Generation/cohorts/groups to use as a reference of the kinship value in the selection index |
selection.highest |
If FALSE to select individuals with lowest value for the selection criterium (default c(TRUE,TRUE) - (m,w)) |
ignore.best |
Not consider the top individuals of the selected individuals (e.g. to use 2-10 best individuals) |
best.selection.ratio.m, best.selection.ratio.f |
Ratio of the frequency of the selection of the best best individual and the worst best individual (default=1) |
best.selection.criteria.m, best.selection.criteria.f |
Criteria to calculate this ratio (default: "bv", alt: "bve", "pheno") |
best.selection.manual.ratio.m, best.selection.manual.ratio.f |
vector containing probability to draw from for every individual (e.g. c(0.1,0.2,0.7)) |
best.selection.manual.reorder |
Set to FALSE to not use the order from best to worst selected individual but plain order based on database-order |
selection.m.random.prob, selection.f.random.prob |
Use this parameter to control the probability of each individual to be selected when doing random selection |
reduced.selection.panel.m, reduced.selection.panel.f |
Use only a subset of individuals of the potential selected ones ("Split in user-interface") |
threshold.selection.index |
Selection index on which to access (matrix which one index per row) |
threshold.selection.value |
Minimum value in the selection index selected individuals have to have |
threshold.selection.sign |
Pick all individuals above (">") the threshold. Alt: ("<", "=", "<=", ">=") |
threshold.selection.criteria |
Criterium on which to evaluate the index (default: "bve", alt: "bv", "pheno") |
threshold.selection |
Minimum value in the selection index selected individuals have to have |
threshold.sign |
Pick all individuals above (">") the threshold. Alt: ("<", "=", "<=", ">=") |
remove.duplicates |
Set to FALSE to select the same individual multiple times when the gen/database/cohorts for selection contains it multiple times |
selection.m.miesenberger, selection.f.miesenberger |
Use Weighted selection index according to Miesenberger 1997 for paternal/maternal selection |
selection.miesenberger.reliability.est |
If available reliability estimated are used. If not use default: "derived" (cor(BVE,BV)^2) , alt: "heritability", "estimated" (SD BVE / SD Pheno) as replacement |
miesenberger.trafo |
Ignore all eigenvalues below this threshold and apply dimension reduction (default: 0 - use all) |
sort.selected.pos |
Set to TRUE to arrange selected individuals according to position in the database (not by breeding value) |
ogc |
If TRUE use optimal genetic contribution theory to perform selection ( This requires the use of the R-package optiSel) |
relationship.matrix.ogc |
Method to calculate relationship matrix for OGC (Default: "pedigree", alt: "vanRaden", "CE", "non_stand", "CE2", "CM") |
depth.pedigree.ogc |
Depth of the pedigree in generations (default: 7) |
ogc.target |
Target of OGC (default: "min.sKin" - minimize inbreeding; alt: "max.BV" / "min.BV" - maximize genetic gain; both under constrains selected below) |
ogc.uniform |
This corresponds to the uniform constrain in optiSel |
ogc.ub |
This corresponds to the ub constrain in optiSel |
ogc.lb |
This corresponds to the lb constrain in optiSel |
ogc.ub.sKin |
This corresponds to the ub.sKin constrain in optiSel |
ogc.lb.BV |
This corresponds to the lb.BV constrain in optiSel |
ogc.ub.BV |
This corresponds to the ub.BV constrain in optiSel |
ogc.eq.BV |
This corresponds to the eq.BV constrain in optiSel |
ogc.ub.sKin.increase |
This corresponds to the upper bound (current sKin + ogc.ub.sKin.increase) as ub.sKin in optiSel |
ogc.lb.BV.increase |
This corresponds to the lower bound (current BV + ogc.lb.BV.increase) as lb.BV in optiSel |
ogc.c1 |
Only applicable when TN-version of OGC is available |
ogc.isCandidate |
Only applicable when TN-version of OGC is available |
ogc.plots |
Only applicable when TN-version of OGC is available |
ogc.weight |
Only applicable when B4F-version of OGC is available |
ogc.freq |
Only applicable when B4F-version of OGC is available |
selection.skip |
Set to FALSE in case no selection of individuals should be performed (just skips some unneccessary computations) |
breeding.size |
Number of individuals to generate (default: 0, use vector with two entries to control offspring per sex) |
breeding.size.litter |
Number of litters to generate (default: NULL - use breeding.size; only single positive number input allowed) |
name.cohort |
Name of the newly added cohort |
breeding.sex |
Share of female individuals (if single value is used for breeding size; default: 0.5) |
breeding.sex.random |
If TRUE randomly chose sex of new individuals (default: FALSE - use expected values) |
sex.s |
Specify which newly added individuals are male (1) or female (2) |
add.gen |
Generation you want to add the new individuals to (default: New generation) |
share.genotyped |
Share of individuals newly generated individuals that are genotyped (Default: 0). Also applies if individuals are copied with copy.individual |
phenotyping.child |
Starting phenotypes of newly generated individuals (default: "zero", alt: "mean" of both parents, "obs" - regular observation) |
fixed.effects.p |
Parametrization for the fixed effects (default: c(0,0..,0), if multiple different parametrizations are possible use a matrix with one parametrization per row) |
fixed.effects.freq |
Frequency of each different parametrization of the fixed effects |
new.class |
Migration level of newly generated individuals (default: 0 / use vector for different classes for different sexes) |
max.offspring |
Maximum number of offspring per individual (default: c(Inf,Inf) - (m,w)) |
max.offspring.individual.m, max.offspring.individual.f |
Vector with maximum number of offspring for first/second parent (default: NULL). Order in the vector by order of selection |
max.offspring.individual.gen |
matrix with first column generation with limited number offspring, second column number of allowed offspring |
max.offspring.individual.database |
matrix with first four columns database with limited number offspring, fifth column number of allowed offspring |
max.offspring.individual.cohorts |
matrix with first column cohort with limited number offspring, second column number of allowed offspring |
max.litter |
Maximum number of litters per individual (default: c(Inf,Inf) - (m,w)) |
max.mating.pair |
Maximum number of matings between two specific individuals (default: Inf) |
avoid.mating.fullsib |
Set to TRUE to not generate offspring of full siblings |
avoid.mating.halfsib |
Set to TRUE to not generate offspring from half or full siblings |
avoid.mating.parent |
Set to TRUE to not generate offspring from parent / sibling matings |
avoid.mating.inb |
Maximum allowed expected inbreeding to allow a mating combination (based on kinships) |
avoid.mating.inb.quantile |
Use this to not perform mating between more related potential parents (quantile of all expected inbreeding levels) |
avoid.mating.inb.min, avoid.mating.kinship.min |
Share of mating to at minimum perform for each individual (default: 0) |
avoid.mating.kinship |
Maximum allowed expected kinship of an offspring to a reference group of individuals |
avoid.mating.kinship.quantile |
Maximum allowed expected kinship of an offspring to a reference group of individuals |
avoid.mating.kinship.gen, avoid.mating.kinship.database, avoid.mating.kinship.cohorts |
Gen/database/cohorts of individuals to consider as a reference pool in avoid.mating.kinship |
avoid.mating.kinship.median |
Set to TRUE to use median kinship instead of mean kinship in avoid.mating.kinship (default: FALSE) |
avoid.mating.depth.pedigree |
Depth of the pedigree to calculate expected inbreeding levels / kinships |
avoid.mating.remove |
Set to TRUE to automatically exclude any selected individuals from the sample of parents |
avoid.mating.ignore |
Set to value higher 0 for avoid.mating.inb/kinship restrictions to not always be applied |
avoid.mating.resampling |
Number of sampling attempts to avoid unwanted matings (( last couple of individuals otherwise can have unwanted relatedness, default = 1000)) |
fixed.breeding |
Set of targeted matings to perform (matrix with 7 columns: database position first parent (gen, sex, nr), database position second parent (gen,sex,nr), likelihood to be female (optional)) |
fixed.breeding.best |
Perform targeted matings in the group of selected individuals (matrix with 5 columns: position first parent (male/female pool of selected individuals, ranking in selected animals), position second parent (male/female pool of selected individuals, ranking in selected animals), likelihood to be female (optional)) |
fixed.breeding.id |
Set of target matings to perform (matrix with 3 columns: id first parent, id second parent, likelihood to be female (optional)) |
fixed.assignment |
Set to "bestbest" / TRUE for targeted mating of best-best individual till worst-worst (of selected). set to "bestworst" for best-worst mating |
breeding.all.combination |
Set to TRUE to automatically perform each mating combination possible exactly ones. |
repeat.mating |
Generate multiple mating from the same dam/sire combination (first column: number of offspring; second column: probability) |
repeat.mating.copy |
Generate multiple copies from a copy action (combine / copy.individual.m/f) (first column: number of offspring; second column: probability) |
repeat.mating.fixed |
Vector containing number of times each mating is repeated. This will overwrite sampling from repeat.mating / repeat.mating.copy (default: NULL) |
repeat.mating.overwrite |
Set to FALSE to not use the current repeat.mating / repeat.mating.copy input as the new standard values (default: TRUE) |
repeat.mating.trait |
Trait that should be linked to the litter size |
repeat.mating.max |
Maximum number of individuals in a litter |
repeat.mating.s |
Use this parameter to manually provide the size of each litter generated |
same.sex.activ |
If TRUE allow matings of individuals of same sex (Sex here is a general term with the first sex referring to the first parent, second sex second parent) |
same.sex.sex |
Probability to use female individuals as parents (default: 0.5) |
same.sex.selfing |
Set to TRUE to allow for selfing when using same.sex matings (default: FALSE) |
selfing.mating |
If TRUE generate new individuals via selfing |
selfing.sex |
Share of female individuals used for selfing (default: 0.5) |
dh.mating |
If TRUE generate a DH-line in mating process |
dh.sex |
Share of DH-lines generated from selected female individuals |
combine |
Copy existing individuals (e.g. to merge individuals from different groups in a joined cohort). Individuals to use are used as the first parent |
copy.individual |
Set TRUE to generate a copy of an already existing individual. If only one of the sexes has individuals to select from it will automatically detect with sex to chose. Otherwise the first/male parent will be copied |
copy.individual.m, copy.individual.f |
If TRUE generate exactly one copy of all selected male/female in a new cohort (or more by setting breeding.size) |
copy.individual.keep.bve |
Set to FALSE to not keep estimated breeding value in case of use of copying individuals instead of regular meiosis |
copy.individual.keep.pheno |
Set to FALSE to not keep phenotypes in case of use of copying individuals instead of regular meiosis |
added.genotyped |
(OLD! use share.genotyped) Share of individuals that is additionally genotyped (only for copy.individual, default: 0) |
bv.ignore.traits |
Vector of traits to ignore in the calculation of the genomic value (default: NULL; Only recommended for high number of traits and experienced users!) |
generation.cores |
Number of cores used for the generation of new individuals (This will only be active when generating more than 500 individuals) |
generation.core.make.small |
Set to TRUE to delete not necessary individuals during parallelization |
pedigree.error |
Share of errors in the pedigree (default: 0; vector with two entries for errors on male/female side) |
pedigree.unknown |
Share of individuals with unknown parents (default: 0; vector with two entries for differences in unknown-share between male/female side) |
genotyped.gen, genotyped.cohorts, genotyped.database |
Generations/cohorts/groups to generate genotype data (that can be used in a BVE) |
genotyped.share |
Share of individuals in genotyped.gen/database/cohort to generate genotype data from (default: 1) |
genotyped.array |
Genotyping array used |
genotyped.remove.gen, genotyped.remove.database, genotyped.remove.cohorts |
Generations/cohorts/groups from which to remove genotyping information (this will affect all copies of an individual unless genotyped.remove.all.copy is set to FALSE) |
genotyped.remove.all.copy |
Set to FALSE to only change the genotyping state of this particular copy of an individual (default: TRUE) |
genotyped.selected |
Set to TRUE to genotype all selected individuals |
phenotyping |
Quick access to phenotyping for (all: "all", non-phenotyped: "non_obs", non-phenotyped male: "non_obs_m", non-phenotyped female: "non_obs_f") |
phenotyping.gen, phenotyping.cohorts, phenotyping.database |
Generations/cohorts/groups from which to generate additional phenotypes |
n.observation |
Number of phenotypic observations generated per trait and per individuals (use repeatability to control correlation between observations) |
phenotyping.class |
Classes of individuals for which to generate phenotypes (default: NULL –> all classes) |
heritability |
Use sigma.e to obtain a certain heritability (default: NULL) |
repeatability |
Set this to control the share of the residual variance (sigma.e) that is permanent (there for each observation) |
multiple.observation |
If an already phenotyped trait is phenotyped again this will on NOT lead to an additional phenotyped observation unless this is set to TRUE |
phenotyping.selected |
Set to TRUE to phenotype all selected individuals |
share.phenotyped |
Share of the individuals to phenotype (use vector for different probabilities for different traits) |
offpheno.parents.gen, offpheno.parents.database, offpheno.parents.cohorts |
Generations/groups/cohorts to consider to derive phenotype from offspring phenotypes |
offpheno.offspring.gen, offpheno.offspring.cohorts, offpheno.offspring.database |
Active generations/cohorts/groups for import of offspring phenotypes |
sigma.e |
Enviromental standard deviation (default: use sigma.e from last run / usually fit by use of heritability; if never provided: 10; used in BVE for variance components if manually set) |
sigma.e.gen, sigma.e.cohorts, sigma.e.database |
Generations/cohorts/groups to consider when estimating sigma.e when using heritability |
new.residual.correlation |
Correlation of the simulated residual variance |
new.breeding.correlation |
Correlation of the simulated genetic variance (only impacts non-QTL based traits. Needs to be fit in creating.diploid/trait for QTL-based traits) |
phenotyping.trafo.parameter |
Additional input parameter for phenotypic transformation function |
bve |
If TRUE perform a breeding value estimation (default: FALSE) |
bve.gen, bve.cohorts, bve.database |
Generations/Groups/Cohorts of individuals to consider in breeding value estimation (default: NULL) |
relationship.matrix |
Method to calculate relationship matrix for the breeding value estimation. This will automatically chosen between GBLUP, ssGBLUP, pBLUP based on if genotyped individuals are available (Default: "GBLUP", alt: "pedigree", "CE", "non_stand", "CE2", "CM") |
depth.pedigree |
Depth of the pedigree in generations (default: 7) |
singlestep.active |
Set FALSE remove all individuals without genomic data from the breeding value estimation |
bve.ignore.traits |
Vector of traits to ignore in the breeding value estimation (default: NULL, use: "zero" to not consider traits with 0 index weight in selection.index.weights.m/.w) |
bve.array |
Array to use in the breeding value estimation (default: NULL; chose largest possible based on used individuals in BVE) |
bve.imputation |
Set to FALSE to not perform imputation up to the highest marker density of genotyping data that is available |
bve.imputation.errorrate |
Share of errors in the imputation procedure (default: 0) |
bve.all.genotyped |
Set to TRUE to act as if every individual in the breeding value estimation has been genotyped |
bve.insert.gen, bve.insert.cohorts, bve.insert.database |
Generations/Groups/Cohorts of individuals to compute breeding values for (default: all groups in bve.database) |
variance.correction |
Correct for "parental.mean" or "generation.mean" in the estimation of sigma.g for BVE / sigma.e estimation (default: "none") |
bve.class |
Consider only individuals of those class classes in breeding value estimation (default: NULL - use all) |
sigma.g |
Genetic standard deviation (default: calculated based on individuals in BVE ; used in BVE for variance components if manually set; mostly recommended to be used for non-QTL based traits) |
sigma.g.gen, sigma.g.cohorts, sigma.g.database |
Generations/cohorts/groups to consider when estimating sigma.g |
forecast.sigma.g |
Set FALSE to not estimate sigma.g (Default: TRUE // in case sigma.g is set this is automatically set to FALSE) |
remove.effect.position |
If TRUE remove real QTLs in breeding value estimation |
estimate.add.gen.var |
If TRUE estimate additive genetic variance and heritability based on parent model |
estimate.pheno.var |
If TRUE estimate total variance in breeding value estimation |
bve.avoid.duplicates |
If set to FALSE multiple generations of the same individual can be used in the bve (only possible by using copy.individual to generate individuals) |
calculate.reliability |
Set TRUE to calculate a reliability when performing Direct-Mixed-Model BVE |
estimate.reliability |
Set TRUE to estimate the reliability in the BVE by calculating the correlation between estimated and real breeding values |
bve.input.phenotype |
Select what to use in BVE (default: own phenotype ("own"), offspring phenotype ("off"), their average ("mean") or a weighted average ("weighted")) |
mas.bve |
If TRUE use marker assisted selection in the breeding value estimation |
mas.markers |
Vector containing markers to be used in marker assisted selection |
mas.number |
If no markers are provided this nr of markers is selected (if single marker QTL are present highest effect markers are prioritized) |
mas.effects |
Effects assigned to the MAS markers (Default: estimated via lm()) |
mas.geno |
Genotype dataset used in MAS (default: NULL, automatic internal calculation) |
bve.parent.mean |
Set to TRUE to use the average parental performance as the breeding value estimate |
bve.grandparent.mean |
Set to TRUE to use the average grandparental performance as the breeding value estimate |
bve.mean.between |
Select if you want to use the "bve", "bv", "pheno" or "bvepheno" to form the mean (default: "bvepheno" - if available bve, else pheno) |
bve.exclude.fixed.effects |
Vector of fixed effects to ignore in the BVE (default: NULL) |
bve.beta.hat.approx |
Set to FALSE to use the true underlying value for beta_hat for the fixed effect in the direct BVE model. rrBLUP, BGLR, sommer will always estimate beta_hat. |
bve.per.sample.sigma.e |
Set to FALSE to deactivate the use of a heritability based on the number of observations generated per sample |
bve.p_i.list |
Vector of allele frequencies to be used when calculating the genomic relationship matrix (default: calculate them based on Z) |
bve.p_i.gen, bve.p_i.database, bve.p_i.cohorts |
Generations/cohorts/groups to use when manually calculating allele frequencies for genomic relationship matrix |
bve.p_i.exclude.nongenotyped |
Set to TRUE to exclude non-genotyped individuals when calculating allele frequencies for genomic relationship matrix standardization |
bve.use.all.copy |
Set to TRUE to use phenotypes and genotyped status from all copies of an individual instead of just the provided ones in the bve.gen/database/cohorts (default: FALSE) |
bve.pedigree.error |
Set to FALSE to ignore/correct for any pedigree errors |
mobps.bve |
If TRUE predict BVEs in direct estimation with assumed known heritability (default: TRUE; activating use of any other BVE method to TRUE will overwrite this) |
mixblup.bve |
Set to TRUE to activate breeding value estimation via MiXBLUP (requires MiXBLUP license!) |
blupf90.bve |
Set to TRUE to activate breeding value estimation via BLUPF90 (requires blupf90 software!) |
mixblup.reliability |
Set to TRUE to activate breeding value estimation via MiXBLUP (requires MiXBLUP license!) |
emmreml.bve |
If TRUE use REML estimator from R-package EMMREML in breeding value estimation |
rrblup.bve |
If TRUE use REML estimator from R-package rrBLUP in breeding value estimation |
sommer.bve |
If TRUE use REML estimator from R-package sommer in breeding value estimation |
sommer.multi.bve |
Set TRUE to use a multi-trait model in the R-package sommer for BVE |
BGLR.bve |
If TRUE use BGLR to perform breeding value estimation |
pseudo.bve |
If set to TRUE the breeding value estimation will be simulated with resulting accuracy pseudo.bve.accuracy (default: 1) |
pseudo.bve.accuracy |
The accuracy to be obtained in the "pseudo" - breeding value estimation |
bve.solve |
Provide solver to be used in BVE (default: "exact" solution via inversion, alt: "pcg", function with inputs A, b and output y_hat) |
mixblup.jeremie |
Set to TRUE to use Jeremies suggested MiXBLUP settings |
mixblup.hpblup |
Set to TRUE to use hpblup in MiXBLUP (default: FALSE) |
mixblup.pedfile |
Set to FALSE to manually generate your MiXBLUP pedfile |
mixblup.parfile |
Set to FALSE to manually generate your MiXBLUP parfile |
mixblup.datafile |
Set to FALSE to manually write your MiXBLUP datafile |
mixblup.inputfile |
Set to FALSE to manually write your MiXBLUP inputfile |
mixblup.genofile |
Set to FALSE to manually write the MiXBLUP genotypefile |
mixblup.path |
Provide path to MiXBLUP.exe (default is your working directory: Windows: MixBLUP; Linux ./MixBLUP.exe) |
mixblup.path.pedfile |
Path from where to import the MiXBLUP pedfile |
mixblup.path.parfile |
Path from where to import the MiXBLUP parfile |
mixblup.path.datafile |
Path from where to import the MiXBLUP datafile |
mixblup.path.inputfile |
Path from where to import the MiXBLUP inputfile |
mixblup.path.genofile |
Path from where to import the MiXBLUP genofile |
mixblup.full.path.genofile |
Path from where to import the MiXBLUP genofile |
mixblup.files |
Directory to generate all files generated when using MiXBLUP (default: MiXBLUP_files/ ) |
mixblup.verbose |
Set to TRUE to display MiXBLUP prints |
blupf90.verbose |
Set to TRUE to display blupf90 prints |
mixblup.genetic.cov |
Provide genetic covariance matrix to be used in MiXBLUP (lower-triangle is sufficent) (default: underlying true values) |
mixblup.residual.cov |
Provide residual covariance matrix to be used in MiXBLUP (lower-triangle is sufficent) (default: underlying true values) |
mixblup.lambda |
Lambda parameter in MiXBLUP (default: 1) |
mixblup.alpha |
Alpha parameter in MiXBLUP (default: 0.95, with alpha + beta = 1 , warning: MiXBLUP software this is 1) |
mixblup.beta |
Beta parameter in MiXBLUP (default: 0.05, with alpha + beta = 1 , warning: MiXBLUP software this is 0) |
mixblup.omega |
Omega parameter in MiXBLUP (default: mixblup.lambda) |
mixblup.maxit |
!Maxit qualifier in MiXBLUP (default: 5.000) |
mixblup.stopcrit |
!STOPCRIT qualifier in MiXBLUP (default: not used, suggested value 1.E-4 for ssGBLUP) // will overwrite maxit |
mixblup.maf |
!MAF qualifier in MiXBLUP (default: 0.005) |
mixblup.numproc |
Numproc parameter in MiXBLUP (default: not set // 1) |
mixblup.apy |
Set to TRUE to use APY inverse in MiXBLUP (default: FALSE) |
mixblup.apy.core |
Number of core individuals in the APY algorithm (default: 5000) |
mixblup.ta |
Set to TRUE to use the !Ta flag in MixBLUP |
mixblup.tac |
Set to TRUE to use the !TAC flag in MixBLUP |
mixblup.skip |
Set to TRUE to skip the actually system call to MiXBLUP and only write the MiXBLUP files |
blupf90.skip |
Set to TRUE to skip the actually system calls of blupf90 and only write the blupf90 input files |
mixblup.restart |
Set to TRUE to set the !RESTART flag in MiXBLUP (requires a "Solunf" file in the working directory) |
mixblup.nopeek |
Set to TRUE to set the !NOPEEK flag in MiXBLUP |
mixblup.calcinbr.s |
Set to TRUE to set the !CalcInbr flag to S |
mixblup.multiple.records |
Set to TRUE to write multiple phenotypic records for an individual |
mixblup.attach |
Set TRUE to just extent the existing genotype file instead of writting it completely new |
mixblup.debug |
Set TRUE to set debugging flags for mixblup call (-Dmst > mixblup_debug.log) (default: FALSE) |
mixblup.plink |
Set TRUE to write genotype files in PLINK format (requires R-package genio, default: FALSE) |
mixblup.cleanup |
Delete all mixblup output files above the indicated size after MiXBLUP run completes (default: Inf) |
blupf90.pedfile |
Set to FALSE to manually generate your MiXBLUP pedfile |
blupf90.parfile |
Set to FALSE to manually generate your MiXBLUP parfile |
blupf90.datafile |
Set to FALSE to manually generate your blupf90 datafile |
blupf90.inputfile |
Set to FALSE to manually write your MiXBLUP inputfile |
blupf90.genofile |
Set to FALSE to manually write the blupf90 genotypefile |
mixblup.dgv |
Set TRUE to use DGV-PBLUP (Only applicable with TAC-BLUP) |
mixblup.dgv.freq |
Path of allele frequency file for DGV-PBLUP |
mixblup.dgv.effect |
Path of SNP effect file for DGV-PBLUP |
blupf90.path |
Provide path to blupf90 (default is your working directory: Windows: ./blupf90+.exe ; Linux ./blupf90+.exe) |
renumf90.path |
Provide path to blupf90 (default is your working directory: Windows: ./renumf90.exe ; Linux ./renumf90.exe) |
blupf90.path.pedfile |
Path from where to import the blupf90 pedfile |
blupf90.path.parfile |
Path from where to import the blupf90 parfile |
blupf90.path.datafile |
Path from where to import the blupf90 data file |
blupf90.path.inputfile |
Path from where to import the blupf90 inputfile |
blupf90.path.genofile |
Path from where to import the blupf90 genotype file |
blupf90.files |
Directory to generate all files generated when using blupf90 (default: blupf90_files/ ) |
blupf90.blksize |
blupf90 parameter blksize (Default: number of traits) |
blupf90.no.quality |
blupf90 setting OPTION no_quality_control (Default: FALSE) |
blupf90.conv_crit |
blupf90 parameter conv_crit (Default: blupf90 default) |
BGLR.model |
Select which BGLR model to use (default: "RKHS", alt: "BRR", "BL", "BayesA", "BayesB", "BayesC") |
BGLR.burnin |
Number of burn-in steps in BGLR (default: 1000) |
BGLR.iteration |
Number of iterations in BGLR (default: 5000) |
BGLR.print |
If TRUE set verbose to TRUE in BGLR |
BGLR.save |
Method to use in BGLR (default: "RKHS" - alt: NON currently) |
BGLR.save.random |
Add random number to store location of internal BGLR computations (only needed when simulating a lot in parallel!) |
miraculix |
If TRUE use miraculix to perform computations (ideally already generate population in creating.diploid with this; default: automatic detection from population list) |
miraculix.cores |
Number of cores used in miraculix applications (default: 1) |
miraculix.mult |
If TRUE use miraculix for matrix multiplications even if miraculix is not used for storage |
miraculix.chol |
Set to FALSE to deactive miraculix based Cholesky-decomposition (default: TRUE) |
miraculix.destroyA |
If FALSE A will not be destroyed in the process of inversion (less computing / more memory) |
estimate.u |
If TRUE estimate u in breeding value estimation (Y = Xb + Zu + e) |
fast.uhat |
Set to FALSE to derive inverse of A in rrBLUP (only required when this becomes numerical unstable otherwise) |
gwas.u |
If TRUE estimate u via GWAS (relevant for gene editing) |
approx.residuals |
If FALSE calculate the variance for each marker separatly instead of using a set variance (does not change order - only p-values) |
gwas.gen, gwas.cohorts, gwas.database |
Generations/cohorts/groups to consider in GWAS analysis |
gwas.group.standard |
If TRUE standardize phenotypes by group mean |
y.gwas.used |
What y value to use in GWAS study (Default: "pheno", alt: "bv", "bve") |
gene.editing.offspring |
If TRUE perform gene editing on newly generated individuals |
gene.editing.best |
If TRUE perform gene editing on selected individuals |
gene.editing.offspring.sex |
Which sex to perform editing on (Default c(TRUE,TRUE), mw) |
gene.editing.best.sex |
Which sex to perform editing on (Default c(TRUE,TRUE), mw) |
nr.edits |
Number of edits to perform per individual |
culling.non.selected |
Set TRUE to cull all non-selected individuals (default: FALSE) |
culling.gen, culling.cohorts, culling.database |
Generations/cohorst/groups to consider to culling |
culling.type |
Default: 0, can be set to code different type of culling reasons (e.g. 0 - aging, 1 - selection, 2 - health) |
culling.time |
Age of the individuals at culling // use time.point if the age of individuals is variable and culling is executed on individuals of different ages culled at the same time |
culling.name |
Name of the culling action (user-interface stuff) |
culling.bv1 |
Reference Breeding value |
culling.share1 |
Probability of death for individuals with bv1 |
culling.bv2 |
Alternative breeding value (linear extended for other bvs) |
culling.share2 |
Probability of death for individuals with bv2 |
culling.index |
Genomic index (default:0 - no genomic impact, use: "lastindex" to use the last selection index applied in selection) |
culling.single |
Set to FALSE to not apply the culling module on all individuals of the cohort |
culling.all.copy |
Set to FALSE to not kill copies of the same individual in the culling module |
mutation.rate |
Mutation rate in each marker (default: 10^-8) |
remutation.rate |
Remutation rate in each marker (default: 10^-8) |
recombination.rate |
Average number of recombination per 1 length unit (default: 1M) |
recombination.rate.trait |
Select a trait which BV will be used as a scalar for the expected number of recombination (default: 0) |
recombination.function |
Function used to calculate position of recombination events (default: MoBPS::recombination.function.haldane()) |
recombination.minimum.distance |
Minimum distance between two points of recombination (default: 0) |
recombination.distance.penalty |
Reduced probability for recombination events closer than this value - linear penalty (default: 0) |
recombination.distance.penalty.2 |
Reduced probability for recombination events closer than this value - quadratic penalty (default: 0) |
recom.f.indicator |
Use step function for recombination map (transform snp.positions if possible instead) |
import.position.calculation |
Function to calculate recombination point into adjacent/following SNP |
duplication.rate |
Share of recombination points with a duplication (default: 0 - DEACTIVATED) |
duplication.length |
Average length of a duplication (Exponentially distributed) |
duplication.recombination |
Average number of recombinations per 1 length uit of duplication (default: 1) |
gen.architecture.m, gen.architecture.f |
Genetic architecture for male/female individuals (default: 0 - no transformation) |
add.architecture |
List with two vectors containing (A: length of chromosomes, B: position in cM of SNPs) |
intern.func |
Chose which function will be used for simulation of meiosis (default: 0, alt: 1,2) - can be faster for specific cases |
delete.haplotypes |
Generations for with haplotypes of founders can be deleted from population list for memory reduction (default: NULL) |
delete.recombi |
Generations for which recombination points can be deleted from the population list for memory reduction (default: NULL) |
delete.recombi.only.non.genotyped |
Set TRUE to only remove points of recombination for non-genotyped individuals |
delete.recombi.class |
Set TRUE to only remove points of recombination for individuals from a specific class |
delete.individuals |
Generations for with individuals are completely deleted from population list for memory reduction (default: NULL) |
delete.gen |
Generations to entirely deleted fro population list for memory reduction (default: NULL) |
delete.sex |
Remove all individuals from these sex from generation delete.individuals (default: 1:2 ; note:delete individuals=NULL) |
delete.same.origin |
If TRUE delete recombination points when genetic origin of adjacent segments is the same |
save.recombination.history |
If TRUE store the time point of each recombination event |
store.sparse |
Set to TRUE to store the pedigree relationship matrix as a sparse matrix |
storage.save |
Lower numbers will lead to less memory but slightly higher computing time for calculation of the pedigree relationship matrix (default: 1.5, min: 1) |
verbose |
Set to FALSE to not display any prints |
report.accuracy |
Report the accuracy of the breeding value estimation |
store.breeding.totals |
If TRUE store information on selected individuals in $info$breeding.totals (default: FALSE) |
store.bve.data |
If TRUE store information of bve in $info$bve.data |
store.comp.times |
If TRUE store computation times in $info$comp.times.general (default: TRUE) |
store.comp.times.bve |
If TRUE store computation times of breeding value estimation in $info$comp.times.bve (default: TRUE) |
store.comp.times.generation |
If TRUE store computation times of mating simulations in $info$comp.times.generation (default: TRUE) |
store.effect.freq |
If TRUE store the allele frequency of effect markers per generation |
Rprof |
Store computation times of each function |
randomSeed |
Set random seed of the process |
display.progress |
Set FALSE to not display progress bars. Setting verbose to FALSE will automatically deactive progress bars |
time.point |
Time point at which the new individuals are generated |
age.point |
Time point at which the new individuals are born (default: time.point - mostly useful in the founder generation) |
creating.type |
Technique to generate new individuals (use mostly intended for web-based application) |
import.relationship.matrix |
Input the wanted relationship matrix with this parameter (default: NULL - relationship matrix will be calculated from other sources) |
export.selected |
Set to TRUE to export the list of selected individuals |
export.selected.database |
Set to TRUE to export a database of the selected individuals |
export.relationship.matrix |
Export the relationship matrix used in the breeding value estimation |
pen.assignments |
This is a placeholder to deactivate this module for now |
pen.size |
Pen size. When different types of pen are used: use a matrix with two columns coding Number of individuals per pen, Probability for each pen size |
pen.by.sex |
Only individuals of the same sex are put in the same pen (default: TRUE) |
pen.by.litter |
Only individuals of the same litter are put in the same pen (default: FALSE) |
pen.size.overwrite |
Set to FALSE to not use the input for pen.size for down-stream use of breeding.diploid (default: TRUE) |
selection.m, selection.f |
(OLD! use selection criteria) Selection criteria for male/female individuals (Set to "random" to randomly select individuals - default: "function" based on selection.criteria ((usually breeding values))) |
new.bv.observation.gen, new.bv.observation.cohorts, new.bv.observation.database |
(OLD! use phenotyping.gen/cohorts/database) Vector of generation from which to generate additional phenotypes |
best1.from.group, best1.from.cohort |
(OLD!- use selection.m.database/cohorts) Groups of individuals to consider as First Parent / Father (also female individuals are possible) |
best2.from.group, best2.from.cohort |
(OLD!- use selection.f.database/cohorts) Groups of individuals to consider as Second Parent / Mother (also male individuals are possible) |
new.bv.observation |
(OLD! - use phenotyping) Quick access to phenotyping for (all: "all", non-phenotyped: "non_obs", non-phenotyped male: "non_obs_m", non-phenotyped female: "non_obs_f") |
reduce.group |
(OLD! - use culling modules) Groups of individuals for reduce to a new size (by changing class to -1) |
reduce.group.selection |
(OLD! - use culling modules) Selection criteria for reduction of groups (cf. selection.m / selection.f - default: "random") |
new.bv.child |
(OLD! - use phenotyping.child) Starting phenotypes of newly generated individuals (default: "zero", alt: "mean" of both parents, "obs" - regular observation) |
computation.A |
(OLD! - use relationship.matrix) Method to calculate relationship matrix for the breeding value estimation (Default: "vanRaden", alt: "pedigree", "CE", "non_stand", "CE2", "CM") |
computation.A.ogc |
(OLD! use relationship.matrix.ogc) Method to calculate pedigree matrix in OGC (Default: "pedigree", alt: "vanRaden", "CE", "non_stand", "CE2", "CM") |
new.phenotype.correlation |
(OLD! - use new.residual.correlation!) Correlation of the simulated enviromental variance |
offspring.bve.parents.gen, offspring.bve.parents.cohorts, offspring.bve.parents.database |
(OLD! use offpheno.parents.gen/database/cohorts) Generations/cohorts/groups to consider to derive phenotype from offspring phenotypes |
offspring.bve.offspring.gen, offspring.bve.offspring.cohorts, offspring.bve.offspring.database |
(OLD! use offpheno.offspring.gen/database/cohorts) Active generations/cohorts/groups for import of offspring phenotypes |
input.phenotype |
(OLD! use bve.input.phenotype) Select what to use in BVE (default: own phenotype ("own"), offspring phenotype ("off"), their average ("mean") or a weighted average ("weighted")) |
multiple.bve.weights.m, multiple.bve.weights.f |
(OLD! use selection.index.weights.m/f) Weighting between traits (default: 1) |
multiple.bve.scale.m, multiple.bve.scale.f |
(OLD! use selection.index.scale.m/f) Default: "bv_sd"; Set to "pheno_sd" when using gains per phenotypic SD, "unit" when using gains per unit, "bve" when using estimated breeding values |
use.recalculate.manual |
Set to TRUE to use recalculate.manual to calculate genomic values (all individuals and traits jointly, default: FALSE) |
recalculate.manual.subset |
Maximum number of individuals to process at the same time (( genotypes are in memory )) |
compute.grandparent.contribution |
compute share of genome inherited from each grandparent based on recombination points (default: FALSE) |
size.scaling |
Set to value to scale all input for breeding.size / selection.size (This will not work for all breeding programs / less general than json.simulation) |
parallel.internal |
Internal parameter for the parallelization |
varg |
Experimental parameter for Tobias Niehoff (do not touch!) |
gain.stats |
Set to FALSE to not compute genetic gains compared to previous generation (selection) |
next.id |
Id to assign to first next individual generated |
copy.individual.use, copy.individual.use2 |
Use this to skip copying some entries from the internal storage ((minor speed up)) |
Value
Population-list
Examples
population <- creating.diploid(nsnp=1000, nindi=100)
population <- breeding.diploid(population, breeding.size=100, selection.size=c(25,25))
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.