Package 'MoBPS'

Title: Modular Breeding Program Simulator
Description: Framework for the simulation framework for the simulation of complex breeding programs and compare their economic and genetic impact. The package is also used as the background simulator for our a web-based interface <http:www.mobps.de>. Associated publication: Pook et al. (2020) <doi:10.1534/g3.120.401193>.
Authors: Torsten Pook
Maintainer: Torsten Pook <[email protected]>
License: GPL (>= 3)
Version: 1.6.64
Built: 2025-02-14 04:12:52 UTC
Source: https://github.com/cran/MoBPS

Help Index

Add a genotyping array

Description

Function to add a genotyping array for the population

Usage

add.array(population, marker.included = TRUE, array.name = NULL)

Arguments

population

population list
marker.included

Vector with number of SNP entries coding if each marker is on the array (TRUE/FALSE)
array.name

Name of the added array

Value

Population list

Examples

data(ex_pop)
population <- add.array(ex_pop, marker.included = c(TRUE, FALSE), array.name="Half-density")

Add a trait as a linear combination of other traits

Description

Function to create an additional trait that is the results of a linear combination of the other traits

Usage

add.combi(population, trait, combi.weights, trait.name = NULL)

Arguments

population

population list
trait

trait nr. for which to implement a combination of other traits
combi.weights

Weights (only linear combinations of other traits are allowed!)
trait.name

Name of the trait generated

Value

Population list

Population list

Examples

data(ex_pop)
population <- creating.trait(ex_pop, n.additive = 100)
population <- add.combi(population, trait = 3, combi.weights = c(1,5))

Add something to the diagonal

Description

Function to add numeric to the diagonal of a matrix

Usage

add.diag(M, d)

Arguments

M

Matrix
d

Vector to add to the diagonal of the matrix

Value

Matrix with increased diagonal elements

Matrix with modified diagonal entries

Examples

A <- matrix(c(1,2,3,4), ncol=2)
B <- add.diag(A, 5)

Add a relationship matrix for founder individuals

Description

Function to relationship matrix for founder individuals that is used for any calculation of the pedigree

Usage

add.founder.kinship(population, founder.kinship = "vanRaden", gen = 1)

Arguments

population

population list
founder.kinship

Default is to use vanRaden relationship. Alternative is to enter a pedigree-matrix (order of individuals is first male then female)
gen

Generation for which to enter the pedigree-matrix

Value

Population list

Examples

data(ex_pop)
population <- add.founder.kinship(ex_pop)

Moore-Penrose-Transfomration

Description

Internal transformation using Moore-Penrose

Usage

alpha_to_beta(alpha, G, Z)

Arguments

alpha

alpha
G

kinship-matrix
Z

genomic information matrix

Value

Vector with single marker effects

Analyze genomic values

Description

Function to analyze correlation between bv/bve/pheno

Usage

analyze.bv(
  population,
  gen = NULL,
  database = NULL,
  cohorts = NULL,
  bvrow = "all",
  advanced = FALSE
)

Arguments

population

Population list
gen

Quick-insert for database (vector of all generations to export)
database

Groups of individuals to consider for the export
cohorts

Quick-insert for database (vector of names of cohorts to export)
bvrow

Which traits to display
advanced

Set to TRUE to also look at offspring pheno

Value

[[1]] Correlation between BV/BVE/Phenotypes [[2]] Genetic variance of the traits

Examples

data(ex_pop)
analyze.bv(ex_pop,gen=1)

Analyze allele frequency of a single marker

Description

Analyze allele frequency of a single marker

Usage

analyze.population(
  population,
  chromosome = NULL,
  snp = NULL,
  snp.name = NULL,
  database = NULL,
  gen = NULL,
  cohorts = NULL
)

Arguments

population

Population list
chromosome

Number of the chromosome of the relevant SNP
snp

Number of the relevant SNP
snp.name

Name of the SNP to analyze
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)

Value

Frequency of AA/AB/BB in selected gen/database/cohorts

Examples

data(ex_pop)
analyze.population(ex_pop, snp=1, chromosome=1, gen=1:5)

Decoding of bitwise-storing in R

Description

Function for decoding in bitwise-storing in R (only 30 of 32 bits are used!)

Usage

bit.snps(bit.seq, nbits, population = NULL, from.p.bit = 1)

Arguments

bit.seq

bitweise gespeicherte SNP-Sequenz
nbits

Number of usable bits (default: 30)
population

Population list
from.p.bit

Bit to start on

Value

De-coded marker sequence

Bitwise-storing in R

Description

Function for bitwise-storing in R (only 30 of 32 bits are used!)

Usage

bit.storing(snpseq, nbits)

Arguments

snpseq

SNP sequence
nbits

Number of usable bits (default: 30)

Value

Bit-wise coded marker sequence

Breeding function

Description

Function to simulate a step in a breeding scheme

Usage

breeding.diploid(
  population,
  mutation.rate = 10^-8,
  remutation.rate = 10^-8,
  recombination.rate = 1,
  selection.m = NULL,
  selection.f = NULL,
  new.selection.calculation = TRUE,
  selection.function.matrix = NULL,
  selection.size = 0,
  ignore.best = 0,
  breeding.size = 0,
  breeding.sex = NULL,
  breeding.sex.random = FALSE,
  relative.selection = FALSE,
  class.m = 0,
  class.f = 0,
  add.gen = 0,
  recom.f.indicator = NULL,
  duplication.rate = 0,
  duplication.length = 0.01,
  duplication.recombination = 1,
  new.class = 0L,
  bve = FALSE,
  sigma.e = NULL,
  sigma.g = 100,
  new.bv.child = NULL,
  phenotyping.child = NULL,
  relationship.matrix = "vanRaden",
  relationship.matrix.ogc = "kinship",
  computation.A = NULL,
  computation.A.ogc = NULL,
  delete.haplotypes = NULL,
  delete.individuals = NULL,
  fixed.breeding = NULL,
  fixed.breeding.best = NULL,
  max.offspring = Inf,
  max.litter = Inf,
  store.breeding.totals = FALSE,
  forecast.sigma.g = TRUE,
  multiple.bve = "add",
  store.bve.data = FALSE,
  fixed.assignment = FALSE,
  reduce.group = NULL,
  reduce.group.selection = "random",
  selection.highest = c(TRUE, TRUE),
  selection.criteria = NULL,
  same.sex.activ = FALSE,
  same.sex.sex = 0.5,
  same.sex.selfing = FALSE,
  selfing.mating = FALSE,
  selfing.sex = 0.5,
  praeimplantation = NULL,
  heritability = NULL,
  repeatability = NULL,
  save.recombination.history = FALSE,
  martini.selection = FALSE,
  BGLR.bve = FALSE,
  BGLR.model = "RKHS",
  BGLR.burnin = 500,
  BGLR.iteration = 5000,
  BGLR.print = FALSE,
  copy.individual = FALSE,
  copy.individual.m = FALSE,
  copy.individual.f = FALSE,
  dh.mating = FALSE,
  dh.sex = 0.5,
  n.observation = NULL,
  bve.0isNA = FALSE,
  phenotype.bv = FALSE,
  delete.same.origin = FALSE,
  remove.effect.position = FALSE,
  estimate.u = FALSE,
  new.phenotype.correlation = NULL,
  new.residual.correlation = NULL,
  new.breeding.correlation = NULL,
  estimate.add.gen.var = FALSE,
  estimate.pheno.var = FALSE,
  best1.from.group = NULL,
  best2.from.group = NULL,
  best1.from.cohort = NULL,
  best2.from.cohort = NULL,
  add.class.cohorts = TRUE,
  store.comp.times = TRUE,
  store.comp.times.bve = TRUE,
  store.comp.times.generation = TRUE,
  import.position.calculation = NULL,
  BGLR.save = "RKHS",
  BGLR.save.random = FALSE,
  ogc = FALSE,
  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,
  emmreml.bve = FALSE,
  rrblup.bve = FALSE,
  sommer.bve = FALSE,
  sommer.multi.bve = FALSE,
  nr.edits = 0,
  gene.editing.offspring = FALSE,
  gene.editing.best = FALSE,
  gene.editing.offspring.sex = c(TRUE, TRUE),
  gene.editing.best.sex = c(TRUE, TRUE),
  gwas.u = FALSE,
  approx.residuals = TRUE,
  sequenceZ = FALSE,
  maxZ = 5000,
  maxZtotal = 0,
  delete.sex = 1:2,
  gwas.group.standard = FALSE,
  y.gwas.used = "pheno",
  gen.architecture.m = 0,
  gen.architecture.f = NULL,
  add.architecture = NULL,
  ncore = 1,
  ncore.generation = 1,
  Z.integer = FALSE,
  store.effect.freq = FALSE,
  backend = "doParallel",
  randomSeed = NULL,
  randomSeed.generation = NULL,
  Rprof = FALSE,
  miraculix = NULL,
  miraculix.cores = 1,
  miraculix.mult = NULL,
  miraculix.chol = TRUE,
  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,
  bve.class = NULL,
  parallel.generation = FALSE,
  name.cohort = NULL,
  display.progress = TRUE,
  combine = FALSE,
  repeat.mating = NULL,
  repeat.mating.copy = NULL,
  repeat.mating.fixed = NULL,
  repeat.mating.overwrite = TRUE,
  time.point = 0,
  creating.type = 0,
  multiple.observation = FALSE,
  new.bv.observation = NULL,
  new.bv.observation.gen = NULL,
  new.bv.observation.cohorts = NULL,
  new.bv.observation.database = NULL,
  phenotyping = NULL,
  phenotyping.gen = NULL,
  phenotyping.cohorts = NULL,
  phenotyping.database = NULL,
  bve.gen = NULL,
  bve.cohorts = NULL,
  bve.database = NULL,
  sigma.e.gen = NULL,
  sigma.e.cohorts = NULL,
  sigma.e.database = NULL,
  sigma.g.gen = NULL,
  sigma.g.cohorts = NULL,
  sigma.g.database = NULL,
  gwas.gen = NULL,
  gwas.cohorts = NULL,
  gwas.database = NULL,
  bve.insert.gen = NULL,
  bve.insert.cohorts = NULL,
  bve.insert.database = NULL,
  reduced.selection.panel.m = NULL,
  reduced.selection.panel.f = NULL,
  breeding.all.combination = FALSE,
  depth.pedigree = 7,
  depth.pedigree.ogc = 7,
  copy.individual.keep.bve = TRUE,
  copy.individual.keep.pheno = TRUE,
  bve.avoid.duplicates = TRUE,
  report.accuracy = TRUE,
  share.genotyped = 1,
  singlestep.active = FALSE,
  remove.non.genotyped = TRUE,
  added.genotyped = 0,
  fast.uhat = TRUE,
  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,
  culling.gen = NULL,
  culling.database = NULL,
  culling.cohort = NULL,
  culling.time = Inf,
  culling.name = "Not_named",
  culling.bv1 = 0,
  culling.share1 = 0,
  culling.bv2 = NULL,
  culling.share2 = NULL,
  culling.index = 0,
  culling.single = TRUE,
  culling.all.copy = TRUE,
  calculate.reliability = FALSE,
  selection.m.gen = NULL,
  selection.f.gen = NULL,
  selection.m.database = NULL,
  selection.f.database = NULL,
  selection.m.cohorts = NULL,
  selection.f.cohorts = NULL,
  selection.m.miesenberger = FALSE,
  selection.f.miesenberger = NULL,
  selection.miesenberger.reliability.est = "estimated",
  miesenberger.trafo = 0,
  multiple.bve.weights.m = 1,
  multiple.bve.weights.f = NULL,
  multiple.bve.scale.m = "bv_sd",
  multiple.bve.scale.f = NULL,
  verbose = TRUE,
  bve.parent.mean = FALSE,
  bve.grandparent.mean = FALSE,
  bve.mean.between = "bvepheno",
  bve.direct.est = TRUE,
  bve.pseudo = FALSE,
  bve.pseudo.accuracy = 1,
  miraculix.destroyA = TRUE,
  mas.bve = FALSE,
  mas.markers = NULL,
  mas.number = 5,
  mas.effects = NULL,
  threshold.selection = NULL,
  threshold.sign = ">",
  input.phenotype = "own",
  bve.ignore.traits = NULL,
  bv.ignore.traits = NULL,
  genotyped.database = NULL,
  genotyped.gen = NULL,
  genotyped.cohorts = NULL,
  genotyped.share = 1,
  genotyped.array = 1,
  sex.s = NULL,
  bve.imputation = TRUE,
  bve.imputation.errorrate = 0,
  share.phenotyped = 1,
  avoid.mating.fullsib = FALSE,
  avoid.mating.halfsib = FALSE,
  max.mating.pair = Inf,
  bve.per.sample.sigma.e = TRUE,
  bve.solve = "exact"
)

Arguments

population

Population list
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)
selection.m

Selection criteria for male individuals (Set to "random" to randomly select individuals - this happens automatically when no the input in selection.criteria has no input ((usually breeding values)))
selection.f

Selection criteria for female individuals (default: selection.m , alt: "random", function")
new.selection.calculation

If TRUE recalculate breeding values obtained by selection.function.matrix
selection.function.matrix

Manuel generation of a temporary selection function (Use BVs instead!)
selection.size

Number of selected individuals for breeding (default: c(0,0) - alt: positive numbers)
ignore.best

Not consider the top individuals of the selected individuals (e.g. to use 2-10 best individuals)
breeding.size

Number of individuals to generate
breeding.sex

Share of female animals (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)
relative.selection

Use best.selection.ratio instead!
class.m

Migrationlevels of male individuals to consider for mating process (default: 0)
class.f

Migrationlevels of female individuals to consider for mating process (default: 0)
add.gen

Generation you want to add the new individuals to (default: New generation)
recom.f.indicator

Use step function for recombination map (transform snp.positions if possible instead)
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)
new.class

Migration level of newly generated individuals (default: 0)
bve

If TRUE perform a breeding value estimation (default: FALSE)
sigma.e

Enviromental variance (default: 100)
sigma.g

Genetic variance (default: 100 - only used if not computed via estimate.sigma.g^2 in der Zuchtwertschaetzung (Default: 100)
new.bv.child

(OLD! - use phenotyping.child) Starting phenotypes of newly generated individuals (default: "mean" of both parents, "obs" - regular observation, "zero" - 0)
phenotyping.child

Starting phenotypes of newly generated individuals (default: "mean" of both parents, "obs" - regular observation, "zero" - 0)
relationship.matrix

Method to calculate relationship matrix for the breeding value estimation (Default: "vanRaden", alt: "kinship", "CE", "non_stand", "CE2", "CM")
relationship.matrix.ogc

Method to calculate relationship matrix for OGC (Default: "kinship", alt: "vanRaden", "CE", "non_stand", "CE2", "CM")
computation.A

(OLD! - use relationship.matrix) Method to calculate relationship matrix for the breeding value estimation (Default: "vanRaden", alt: "kinship", "CE", "non_stand", "CE2", "CM")
computation.A.ogc

(OLD! use relationship.matrix.ogc) Method to calculate pedigree matrix in OGC (Default: "kinship", alt: "vanRaden", "CE", "non_stand", "CE2", "CM")
delete.haplotypes

Generations for with haplotypes of founders can be deleted (only use if storage problem!)
delete.individuals

Generations for with individuals are completley deleted (only use if storage problem!)
fixed.breeding

Set of targeted matings to perform
fixed.breeding.best

Perform targeted matings in the group of selected individuals
max.offspring

Maximum number of offspring per individual (default: c(Inf,Inf) - (m,w))
max.litter

Maximum number of offspring per individual (default: c(Inf,Inf) - (m,w))
store.breeding.totals

If TRUE store information on selected animals in $info$breeding.totals
forecast.sigma.g

Set FALSE to not estimate sigma.g (Default: TRUE)
multiple.bve

Way to handle multiple traits in bv/selection (default: "add", alt: "ranking")
store.bve.data

If TRUE store information of bve in $info$bve.data
fixed.assignment

Set TRUE for targeted mating of best-best individual till worst-worst (of selected). set to "bestworst" for best-worst mating
reduce.group

(OLD! - use culling modules) Groups of animals 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")
selection.highest

If 0 individuals with lowest bve are selected as best individuals (default c(1,1) - (m,w))
selection.criteria

What to use in the selection proces (default: "bve", alt: "bv", "pheno")
same.sex.activ

If TRUE allow matings of individuals of same sex
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
selfing.mating

If TRUE generate new individuals via selfing
selfing.sex

Share of female individuals used for selfing (default: 0.5)
praeimplantation

Only use matings the lead to a specific genotype in a specific marker
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)
save.recombination.history

If TRUE store the time point of each recombination event
martini.selection

If TRUE use the group of non-selected individuals as second parent
BGLR.bve

If TRUE use BGLR to perform breeding value estimation
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
copy.individual

If TRUE copy the selected father for a mating
copy.individual.m

If TRUE generate exactly one copy of all selected male in a new cohort (or more by setting breeding.size)
copy.individual.f

If TRUE generate exactly one copy of all selected female in a new cohort (or more by setting breeding.size)
dh.mating

If TRUE generate a DH-line in mating process
dh.sex

Share of DH-lines generated from selected female individuals
n.observation

Number of phenotypes generated per individuals (influences enviromental variance)
bve.0isNA

Individuals with phenotype 0 are used as NA in breeding value estimation
phenotype.bv

If TRUE use phenotype as estimated breeding value
delete.same.origin

If TRUE delete recombination points when genetic origin of adjacent segments is the same
remove.effect.position

If TRUE remove real QTLs in breeding value estimation
estimate.u

If TRUE estimate u in breeding value estimation (Y = Xb + Zu + e)
new.phenotype.correlation

(OLD! - use new.residual.correlation!) Correlation of the simulated enviromental variance
new.residual.correlation

Correlation of the simulated enviromental variance
new.breeding.correlation

Correlation of the simulated genetic variance (child share! heritage is not influenced!)
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
best1.from.group

(OLD!- use selection.m.database) Groups of individuals to consider as First Parent / Father (also female individuals are possible)
best2.from.group

(OLD!- use selection.f.database) Groups of individuals to consider as Second Parent / Mother (also male individuals are possible)
best1.from.cohort

(OLD!- use selection.m.cohorts) Groups of individuals to consider as First Parent / Father (also female individuals are possible)
best2.from.cohort

(OLD! - use selection.f.cohorts) Groups of individuals to consider as Second Parent / Mother (also male individuals are possible)
add.class.cohorts

Migration levels of all cohorts selected for reproduction are automatically added to class.m/class.f (default: TRUE)
store.comp.times

If TRUE store computation times in $info$comp.times (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)
import.position.calculation

Function to calculate recombination point into adjacent/following SNP
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!)
ogc

If TRUE use optimal genetic contribution theory to perform selection ( This requires the use of the R-package optiSel)
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
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 mulit-trait model in the R-package sommer for BVE
nr.edits

Number of edits to perform per individual
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)
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 (doesnt change order - only p-values)
sequenceZ

Split genomic matric into parts (relevent if high memory usage)
maxZ

Number of SNPs to consider in each part of sequenceZ
maxZtotal

Number of matrix entries to consider jointly (maxZ = maxZtotal/number of animals)
delete.sex

Remove all individuals from these sex from generation delete.individuals (default: 1:2 ; note:delete individuals=NULL)
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")
gen.architecture.m

Genetic architecture for male animal (default: 0 - no transformation)
gen.architecture.f

Genetic architecture for female animal (default: gen.architecture.m - no transformation)
add.architecture

List with two vectors containing (A: length of chromosomes, B: position in cM of SNPs)
ncore

Cores used for parallel computing in compute.snps
ncore.generation

Number of cores to use in parallel generation
Z.integer

If TRUE save Z as a integer in parallel computing
store.effect.freq

If TRUE store the allele frequency of effect markers per generation
backend

Chose the used backend (default: "doParallel", alt: "doMPI")
randomSeed

Set random seed of the process
randomSeed.generation

Set random seed for parallel generation process
Rprof

Store computation times of each function
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)
best.selection.ratio.m

Ratio of the frequency of the selection of the best best animal and the worst best animal (default=1)
best.selection.ratio.f

Ratio of the frequency of the selection of the best best animal and the worst best animal (default=1)
best.selection.criteria.m

Criteria to calculate this ratio (default: "bv", alt: "bve", "pheno")
best.selection.criteria.f

Criteria to calculate this ratio (default: "bv", alt: "bve", "pheno")
best.selection.manual.ratio.m

vector containing probability to draw from for every individual (e.g. c(0.1,0.2,0.7))
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
bve.class

Consider only animals of those class classes in breeding value estimation (default: NULL - use all)
parallel.generation

Set TRUE to active parallel computing in animal generation
name.cohort

Name of the newly added cohort
display.progress

Set FALSE to not display progress bars. Setting verbose to FALSE will automatically deactive progress bars
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
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.individuals.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)
time.point

Time point at which the new individuals are generated
creating.type

Technique to generate new individuals (usage in web-based application)
multiple.observation

Set TRUE to allow for more than one phenotype observation per individual (this will decrease enviromental variance!)
new.bv.observation

(OLD! - use phenotyping) Quick acces to phenotyping for (all: "all", non-phenotyped: "non_obs", non-phenotyped male: "non_obs_m", non-phenotyped female: "non_obs_f")
new.bv.observation.gen

(OLD! use phenotyping.gen) Vector of generation from which to generate additional phenotypes
new.bv.observation.cohorts

(OLD! use phenotyping.cohorts)Vector of cohorts from which to generate additional phenotype
new.bv.observation.database

(OLD! use phenotyping.database) Matrix of groups from which to generate additional phenotypes
phenotyping

Quick acces to phenotyping for (all: "all", non-phenotyped: "non_obs", non-phenotyped male: "non_obs_m", non-phenotyped female: "non_obs_f")
phenotyping.gen

Vector of generation from which to generate additional phenotypes
phenotyping.cohorts

Vector of cohorts from which to generate additional phenotype
phenotyping.database

Matrix of groups from which to generate additional phenotypes
bve.gen

Generations of individuals to consider in breeding value estimation (default: NULL)
bve.cohorts

Cohorts of individuals to consider in breeding value estimation (default: NULL)
bve.database

Groups of individuals to consider in breeding value estimation (default: NULL)
sigma.e.gen

Generations to consider when estimating sigma.e when using hertability
sigma.e.cohorts

Cohorts to consider when estimating sigma.e when using hertability
sigma.e.database

Groups to consider when estimating sigma.e when using hertability
sigma.g.gen

Generations to consider when estimating sigma.g
sigma.g.cohorts

Cohorts to consider when estimating sigma.g
sigma.g.database

Groups to consider when estimating sigma.g
gwas.gen

Generations to consider in GWAS analysis
gwas.cohorts

Cohorts to consider in GWAS analysis
gwas.database

Groups to consider in GWAS analysis
bve.insert.gen

Generations of individuals to compute breeding values for (default: all groups in bve.database)
bve.insert.cohorts

Cohorts of individuals to compute breeding values for (default: all groups in bve.database)
bve.insert.database

Groups of individuals to compute breeding values for (default: all groups in bve.database)
reduced.selection.panel.m

Use only a subset of individuals of the potential selected ones ("Split in user-interface")
reduced.selection.panel.f

Use only a subset of individuals of the potential selected ones ("Split in user-interface")
breeding.all.combination

Set to TRUE to automatically perform each mating combination possible exactly ones.
depth.pedigree

Depth of the pedigree in generations (default: 7)
depth.pedigree.ogc

Depth of the pedigree in generations (default: 7)
copy.individual.keep.bve

Set to FALSE to not keep estimated breeding value in case of use of copy.individuals
copy.individual.keep.pheno

Set to FALSE to not keep estimated breeding values in case of use of copy.individuals
bve.avoid.duplicates

If set to FALSE multiple generatations of the same individual can be used in the bve (only possible by using copy.individual to generate individuals)
report.accuracy

Report the accuracy of the breeding value estimation
share.genotyped

Share of individuals newly generated individuals that are genotyped
singlestep.active

Set TRUE to use single step in breeding value estimation (only implemented for vanRaden- G matrix and without use sequenceZ) (Legarra 2014)
remove.non.genotyped

Set to FALSE to manually include non-genotyped individuals in genetic BVE, single-step will deactive this as well
added.genotyped

Share of individuals that is additionally genotyped (only for copy.individuals)
fast.uhat

Set to FALSE to derive inverse of A in rrBLUP
offspring.bve.parents.gen

Generations to consider to derive phenotype from offspring phenotypes
offspring.bve.parents.database

Groups to consider to derive phenotype from offspring phenotypes
offspring.bve.parents.cohorts

Cohorts to consider to derive phenotype from offspring phenotypes
offspring.bve.offspring.gen

Active generations for import of offspring phenotypes
offspring.bve.offspring.database

Active groups for import of offspring phenotypes
offspring.bve.offspring.cohorts

Active cohorts for import of offspring phenotypes
culling.gen

Generations to consider to culling
culling.database

Groups to consider to culling
culling.cohort

Cohort to consider to culling
culling.time

Age of the individuals at culling
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
calculate.reliability

Set TRUE to calculate a reliability when performing Direct-Mixed-Model BVE
selection.m.gen

Generations available for selection of paternal parent
selection.f.gen

Generations available for selection of maternal parent
selection.m.database

Groups available for selection of paternal parent
selection.f.database

Groups available for selection of maternal parent
selection.m.cohorts

Cohorts available for selection of paternal parent
selection.f.cohorts

Cohorts available for selection of maternal parent
selection.m.miesenberger

Use Weighted selection index according to Miesenberger 1997 for paternal selection
selection.f.miesenberger

Use Weighted selection index according to Miesenberger 1997 for maternal selection
selection.miesenberger.reliability.est

If available reliability estimated are used. If not use default:"estimated" (SD BVE / SD Pheno), alt: "heritability", "derived" (cor(BVE,BV)^2) as replacement
miesenberger.trafo

Ignore all eigenvalues below this threshold and apply dimension reduction (default: 0 - use all)
multiple.bve.weights.m

Weighting between traits when using "add" (default: 1)
multiple.bve.weights.f

Weighting between traits when using "add" (default: same as multiple.bve.weights.m)
multiple.bve.scale.m

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
multiple.bve.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
verbose

Set to FALSE to not display any prints
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.direct.est

If TRUE predict BVEs in direct estimation according to vanRaden 2008 method 2 (default: TRUE)
bve.pseudo

If set to TRUE the breeding value estimation will be simulated with resulting accuracy bve.pseudo.accuracy (default: 1)
bve.pseudo.accuracy

The accuracy to be obtained in the "pseudo" - breeding value estimation
miraculix.destroyA

If FALSE A will not be destroyed in the process of inversion (less computing / more memory)
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())
threshold.selection

Minimum value in the selection index selected individuals have to have
threshold.sign

Pick all individuals above (">") the threshold. Alt: ("<", "=", "<=", ">=")
input.phenotype

Select what to use in BVE (default: own phenotype ("own"), offspring phenotype ("off"), their average ("mean") or a weighted average ("weighted"))
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 multiple.bve.weights.m/.w)
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!)
genotyped.database

Groups to generate genotype data (that can be used in a BVE)
genotyped.gen

Generations to generate genotype data (that can be used in a BVE)
genotyped.cohorts

Cohorts 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
sex.s

Specify which newly added individuals are male (1) or female (2)
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)
share.phenotyped

Share of the individuals to phenotype
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
max.mating.pair

Set to the maximum number of matings between two individuals (default: Inf)
bve.per.sample.sigma.e

Set to FALSE to deactivate the use of a heritablity based on the number of observations generated per sample
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)

Value

Population-list

Examples

population <- creating.diploid(nsnp=1000, nindi=100)
population <- breeding.diploid(population, breeding.size=100, selection.size=c(25,25))

Internal function to simulate one meiosis

Description

Internal function to simulate one meiosis

Usage

breeding.intern(
  info.parent,
  parent,
  population,
  mutation.rate = 10^-5,
  remutation.rate = 10^-5,
  recombination.rate = 1,
  recom.f.indicator = NULL,
  duplication.rate = 0,
  duplication.length = 0.01,
  duplication.recombination = 1,
  delete.same.origin = FALSE,
  gene.editing = FALSE,
  nr.edits = 0,
  gen.architecture = 0,
  decodeOriginsU = MoBPS::decodeOriginsR
)

Arguments

info.parent

position of the parent in the dataset
parent

list of information regarding the parent
population

Population list
mutation.rate

Mutation rate in each marker (default: 10^-5)
remutation.rate

Remutation rate in each marker (default: 10^-5)
recombination.rate

Average number of recombination per 1 length unit (default: 1M)
recom.f.indicator

Use step function for recombination map (transform snp.positions if possible instead)
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)
delete.same.origin

If TRUE delete recombination points when genetic origin of adjacent segments is the same
gene.editing

If TRUE perform gene editing on newly generated individual
nr.edits

Number of edits to perform per individual
gen.architecture

Used underlying genetic architecture (genome length in M)
decodeOriginsU

Used function for the decoding of genetic origins [[5]]/[[6]]

Value

Inherited parent gamete

Examples

data(ex_pop)
child_gamete <- breeding.intern(info.parent = c(1,1,1), parent = ex_pop$breeding[[1]][[1]][[1]],
                                population = ex_pop)

Development of genetic/breeding value

Description

Function to plot genetic/breeding values for multiple generation/cohorts

Usage

bv.development(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  confidence = c(1, 2, 3),
  development = c(1, 2, 3),
  quantile = 0.95,
  bvrow = "all",
  ignore.zero = TRUE,
  json = FALSE,
  display.time.point = FALSE,
  display.creating.type = FALSE,
  display.cohort.name = FALSE,
  display.sex = FALSE,
  equal.spacing = FALSE,
  time_reorder = FALSE,
  display.line = TRUE,
  ylim = NULL,
  fix_mfrow = FALSE
)

Arguments

population

population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
confidence

Draw confidence intervals for (1- bv, 2- bve, 3- pheno; default: c(1,2,3))
development

Include development of (1- bv, 2- bve, 3- pheno; default: c(1,2,3))
quantile

Quantile of the confidence interval to draw (default: 0.05)
bvrow

Which traits to display (for multiple traits separate plots (par(mfrow)))
ignore.zero

Cohorts with only 0 individuals are not displayed (default: TRUE)
json

If TRUE extract which cohorts to plot according to the json-file used in json.simulation
display.time.point

Set TRUE to use time point of generated to sort groups
display.creating.type

Set TRUE to show Breedingtype used in generation (web-interface)
display.cohort.name

Set TRUE to display the name of the cohort in the x-axis
display.sex

Set TRUE to display the creating.type (Shape of Points - web-based-application)
equal.spacing

Equal distance between groups (independent of time.point)
time_reorder

Set TRUE to order cohorts according to the time point of generation
display.line

Set FALSE to not display the line connecting cohorts
ylim

Set this to fix the y-axis of the plot
fix_mfrow

Set TRUE to not use mfrow - use for custom plots

Value

Genomic values of selected gen/database/cohort

Examples

data(ex_pop)
bv.development(ex_pop, gen=1:5)

Development of genetic/breeding value using a boxplot

Description

Function to plot genetic/breeding values for multiple generation/cohorts using box plots

Usage

bv.development.box(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  bvrow = "all",
  json = FALSE,
  display = "bv",
  display.selection = FALSE,
  display.reproduction = FALSE,
  ylim = NULL,
  fix_mfrow = FALSE
)

Arguments

population

population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
bvrow

Which traits to display (for multiple traits separte plots (par(mfrow)))
json

If TRUE extract which cohorts to plot according to the json-file used in json.simulation
display

Choose between "bv", "pheno", "bve" (default: "bv")
display.selection

Display lines between generated cohorts via selection (webinterface)
display.reproduction

Display lines between generated cohorts via reproduction (webinterface)
ylim

Set this to fix the y-axis of the plot
fix_mfrow

Set TRUE to not use mfrow - use for custom plots

Value

Genomic values of selected gen/database/cohort

Examples

data(ex_pop)
bv.development.box(ex_pop, gen=1:5)

BV standardization

Description

Function to get mean and genetic variance of a trait to a fixed value

Usage

bv.standardization(
  population,
  mean.target = 100,
  var.target = 10,
  gen = NULL,
  database = NULL,
  cohorts = NULL,
  adapt.bve = FALSE,
  adapt.pheno = FALSE,
  verbose = FALSE
)

Arguments

population

Population list
mean.target

Target mean
var.target

Target variance
gen

Quick-insert for database (vector of all generations to export)
database

Groups of individuals to consider for the export
cohorts

Quick-insert for database (vector of names of cohorts to export)
adapt.bve

Modify previous breeding value estimations by scaling (default: FALSE)
adapt.pheno

Modify previous phenotypes by scaling (default: FALSE)
verbose

Set to TRUE to display prints

Value

Population-list with scaled QTL-effects

Examples

population <- creating.diploid(nsnp=1000, nindi=100, n.additive=100)
population <- bv.standardization(population, mean.target=200, var.target=5)

Calculate breeding values

Description

Internal function to calculate the breeding value of a given individual

Usage

calculate.bv(
  population,
  gen,
  sex,
  nr,
  activ_bv,
  import.position.calculation = NULL,
  decodeOriginsU = decodeOriginsR,
  store.effect.freq = FALSE,
  bit.storing = FALSE,
  nbits = 30,
  output_compressed = FALSE,
  bv.ignore.traits = NULL
)

Arguments

population

Population list
gen

Generation of the individual of interest
sex

Sex of the individual of interest
nr

Number of the individual of interest
activ_bv

traits to consider
import.position.calculation

Function to calculate recombination point into adjacent/following SNP
decodeOriginsU

Used function for the decoding of genetic origins [[5]]/[[6]]
store.effect.freq

If TRUE store the allele frequency of effect markers per generation
bit.storing

Set to TRUE if the MoBPS (not-miraculix! bit-storing is used)
nbits

Bits available in MoBPS-bit-storing
output_compressed

Set to TRUE to get a miraculix-compressed genotype/haplotype
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!)

Value

[[1]] true genomic value [[2]] allele frequency at QTL markers

Examples

data(ex_pop)
calculate.bv(ex_pop, gen=1, sex=1, nr=1, activ_bv = 1)

Cattle chip

Description

Genome for cattle according to Ma et al.

Usage

cattle_chip

Author(s)

Torsten Pook [email protected]

Source

Ma et al 2015

Relatedness check between two individuals

Description

Internal function to check the relatedness between two individuals

Usage

check.parents(population, info.father, info.mother, max.rel = 2)

Arguments

population

Population list
info.father

position of the first parent in the dataset
info.mother

position of the second parent in the dataset
max.rel

maximal allowed relationship (default: 2, alt: 1 no full-sibs, 0 no half-sibs)

Value

logical with TRUE if relatedness does not excced max.rel / FALSE otherwise.

Examples

data(ex_pop)
check.parents(ex_pop, info.father=c(4,1,1,1), info.mother=c(4,2,1,1))

chicken chip

Description

Genome for chicken according to Groenen et al.

Usage

chicken_chip

Author(s)

Torsten Pook [email protected]

Source

Groenen et al 2009

Clean-up recombination points

Description

Function to remove recombination points + origins with no influence on markers

Usage

clean.up(population, gen = "all", database = NULL, cohorts = NULL)

Arguments

population

Population list
gen

Generations to clean up (default: "current")
database

Groups of individuals to consider
cohorts

Quick-insert for database (vector of names of cohorts to export)

Value

Population-list with deleted irrelevant recombination points

Examples

data(ex_pop)
ex_pop <- clean.up(ex_pop)

Origins-coding(R)

Description

R-Version of the internal bitwise-coding of origins

Usage

codeOriginsR(M)

Arguments

M

Origins matrix

Value

Bit-wise coded origins

Examples

codeOriginsR(cbind(1,1,1,1))

Combine traits

Description

Function to combine traits in the BVE

Usage

combine.traits(
  population,
  combine.traits = NULL,
  combine.name = NULL,
  remove.combine = NULL,
  remove.all = FALSE
)

Arguments

population

Population list
combine.traits

Vector containing the traits (numbers) to combine into a joined trait
combine.name

Name of the combined trait
remove.combine

Remove a selected previously generated combined trait
remove.all

Set TRUE to remove all previously generated combined traits

Value

Population-list

Examples

population <- creating.diploid(nsnp=100, nindi=100, n.additive = c(50,50))
population <- combine.traits(population, combine.traits=1:2)
population <- breeding.diploid(population, bve=TRUE, phenotyping.gen=1, heritability=0.3)

Compute costs of a breeding program

Description

Function to derive the costs of a breeding program / population-list

Usage

compute.costs(
  population,
  phenotyping.costs = 10,
  genotyping.costs = 100,
  fix.costs = 0,
  fix.costs.annual = 0,
  profit.per.bv = 1,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  interest.rate = 1,
  base.gen = 1
)

Arguments

population

population-list
phenotyping.costs

Costs for the generation of a phenotype
genotyping.costs

Costs for the geneation of a genotype
fix.costs

one time occuring fixed costs
fix.costs.annual

annually occuring fixed costs
profit.per.bv

profit generated by bv per animal
database

Groups of individuals to consider
gen

Quick-insert for database (vector of all generations to consider)
cohorts

Quick-insert for database (vector of names of cohorts to consider)
interest.rate

Applied yearly interest rate
base.gen

Base generation (application of interest rate)

Value

Cost-table for selected gen/database/cohorts of a population-list

Examples

data(ex_pop)
compute.costs(ex_pop, gen=1:5)

Compute costs of a breeding program by cohorts

Description

Function to derive the costs of a breeding program / population-list by cohorts

Usage

compute.costs.cohorts(
  population,
  gen = NULL,
  database = NULL,
  cohorts = NULL,
  json = TRUE,
  phenotyping.costs = NULL,
  genotyping.costs = 0,
  housing.costs = NULL,
  fix.costs = 0,
  fix.costs.annual = 0,
  profit.per.bv = 1,
  interest.rate = 1,
  verbose = TRUE
)

Arguments

population

population-list
gen

Quick-insert for database (vector of all generations to consider)
database

Groups of individuals to consider
cohorts

Quick-insert for database (vector of names of cohorts to consider)
json

If TRUE extract which cohorts to plot according to the json-file used in json.simulation
phenotyping.costs

Costs for the generation of a phenotype
genotyping.costs

Costs for the geneation of a genotype
housing.costs

Costs for housing
fix.costs

one time occuring fixed costs
fix.costs.annual

annually occuring fixed costs
profit.per.bv

profit generated by bv per animal
interest.rate

Applied yearly interest rate
verbose

Set to FALSE to not display any prints

Value

Cost-table for selected gen/database/cohorts of a population-list

Examples

data(ex_pop)
compute.costs.cohorts(ex_pop, gen=1:5, genotyping.costs=25, json=FALSE)

Compute genotype/haplotype

Description

Internal function for the computation of genotypes & haplotypes

Usage

compute.snps(
  population,
  gen,
  sex,
  nr,
  faster = TRUE,
  import.position.calculation = NULL,
  from_p = 1,
  to_p = Inf,
  decodeOriginsU = decodeOriginsR,
  bit.storing = FALSE,
  nbits = 30,
  output_compressed = FALSE
)

Arguments

population

Population list
gen

Generation of the individual to compute
sex

Gender of the individual to compute
nr

Number of the individual to compute
faster

If FALSE use slower version to compute markers between recombination points
import.position.calculation

Function to calculate recombination point into adjacent/following SNP
from_p

First SNP to consider
to_p

Last SNP to consider
decodeOriginsU

Used function for the decoding of genetic origins [[5]]/[[6]]
bit.storing

Set to TRUE if the MoBPS (not-miraculix! bit-storing is used)
nbits

Bits available in MoBPS-bit-storing
output_compressed

Set to TRUE to get a miraculix-compressed genotype/haplotype

Value

haplotypes for the selected individual

Examples

data(ex_pop)
compute.snps(ex_pop, gen=1, sex=1, nr=1)

Compute genotype/haplotype in gene editing application

Description

Internal function for the computation of genotypes & haplotypes in gene editing application

Usage

compute.snps_single(
  population,
  current.recombi,
  current.mut,
  current.ursprung,
  faster = TRUE,
  import.position.calculation = NULL,
  decodeOriginsU = decodeOriginsR
)

Arguments

population

Population list
current.recombi

vector of currently activ recombination points
current.mut

vector of currently activ mutations
current.ursprung

vector of currently activ origins
faster

If FALSE use slower version to compute markers between recombination points
import.position.calculation

Function to calculate recombination point into adjacent/following SNP
decodeOriginsU

Used function for the decoding of genetic origins [[5]]/[[6]]

Value

haplotypes for the selected individual

Generation of the starting population

Description

Generation of the starting population

Usage

creating.diploid(
  dataset = NULL,
  vcf = NULL,
  chr.nr = NULL,
  bp = NULL,
  snp.name = NULL,
  hom0 = NULL,
  hom1 = NULL,
  bpcm.conversion = 0,
  nsnp = 0,
  nindi = 0,
  freq = "beta",
  population = NULL,
  sex.s = "fixed",
  add.chromosome = FALSE,
  generation = 1,
  class = 0L,
  sex.quota = 0.5,
  chromosome.length = NULL,
  length.before = 5,
  length.behind = 5,
  real.bv.add = NULL,
  real.bv.mult = NULL,
  real.bv.dice = NULL,
  snps.equidistant = NULL,
  change.order = FALSE,
  bv.total = 0,
  polygenic.variance = 100,
  bve.mult.factor = NULL,
  bve.poly.factor = NULL,
  base.bv = NULL,
  add.chromosome.ends = TRUE,
  new.phenotype.correlation = NULL,
  new.residual.correlation = NULL,
  new.breeding.correlation = NULL,
  add.architecture = NULL,
  snp.position = NULL,
  position.scaling = FALSE,
  bit.storing = FALSE,
  nbits = 30,
  randomSeed = NULL,
  miraculix = TRUE,
  miraculix.dataset = TRUE,
  n.additive = 0,
  n.equal.additive = 0,
  n.dominant = 0,
  n.equal.dominant = 0,
  n.qualitative = 0,
  n.quantitative = 0,
  dominant.only.positive = FALSE,
  var.additive.l = NULL,
  var.dominant.l = NULL,
  var.qualitative.l = NULL,
  var.quantitative.l = NULL,
  effect.size.equal.add = 1,
  effect.size.equal.dom = 1,
  exclude.snps = NULL,
  replace.real.bv = FALSE,
  shuffle.traits = NULL,
  shuffle.cor = NULL,
  skip.rest = FALSE,
  enter.bv = TRUE,
  name.cohort = NULL,
  template.chip = NULL,
  beta.shape1 = 1,
  beta.shape2 = 1,
  time.point = 0,
  creating.type = 0,
  trait.name = NULL,
  share.genotyped = 1,
  genotyped.s = NULL,
  map = NULL,
  remove.invalid.qtl = TRUE,
  verbose = TRUE,
  bv.standard = FALSE,
  mean.target = NULL,
  var.target = NULL,
  is.maternal = NULL,
  is.paternal = NULL,
  vcf.maxsnp = Inf,
  internal = FALSE
)

Arguments

dataset

SNP dataset, use "random", "allhetero" "all0" when generating a dataset via nsnp,nindi
vcf

Path to a vcf-file used as input genotypes (correct haplotype phase is assumed!)
chr.nr

Vector containing the assosiated chromosome for each marker (default: all on the same)
bp

Vector containing the physical position (bp) for each marker (default: 1,2,3...)
snp.name

Vector containing the name of each marker (default ChrXSNPY - XY chosen accordingly)
hom0

Vector containing the first allelic variant in each marker (default: 0)
hom1

Vector containing the second allelic variant in each marker (default: 1)
bpcm.conversion

Convert physical position (bp) into a cM position (default: 0 - not done)
nsnp

number of markers to generate in a random dataset
nindi

number of inidividuals to generate in a random dataset
freq

frequency of allele 1 when randomly generating a dataset
population

Population list
sex.s

Specify which newly added individuals are male (1) or female (2)
add.chromosome

If TRUE add an additional chromosome to the dataset
generation

Generation of the newly added individuals (default: 1)
class

Migration level of the newly added individuals
sex.quota

Share of newly added female individuals (deterministic if sex.s="fixed", alt: sex.s="random")
chromosome.length

Length of the newly added chromosome (default: 5)
length.before

Length before the first SNP of the dataset (default: 5)
length.behind

Length after the last SNP of the dataset (default: 5)
real.bv.add

Single Marker effects
real.bv.mult

Two Marker effects
real.bv.dice

Multi-marker effects
snps.equidistant

Use equidistant markers (computationally faster! ; default: TRUE)
change.order

If TRUE sort markers according to given marker positions
bv.total

Number of traits (If more than traits via real.bv.X use traits with no directly underlying QTL)
polygenic.variance

Genetic variance of traits with no underlying QTL
bve.mult.factor

Multiplicate trait value times this
bve.poly.factor

Potency trait value over this
base.bv

Average genetic value of a trait
add.chromosome.ends

Add chromosome ends as recombination points
new.phenotype.correlation

(OLD! - use new.residual.correlation) Correlation of the simulated enviromental variance
new.residual.correlation

Correlation of the simulated enviromental variance
new.breeding.correlation

Correlation of the simulated genetic variance (child share! heritage is not influenced!
add.architecture

Add genetic architecture (marker positions)
snp.position

Location of each marker on the genetic map
position.scaling

Manual scaling of snp.position
bit.storing

Set to TRUE if the MoBPS (not-miraculix! bit-storing is used)
nbits

Bits available in MoBPS-bit-storing
randomSeed

Set random seed of the process
miraculix

If TRUE use miraculix package for data storage, computations and dataset generation
miraculix.dataset

Set FALSE to deactive miraculix package for dataset generation
n.additive

Number of additive QTL with effect size drawn from a gaussian distribution
n.equal.additive

Number of additive QTL with equal effect size (effect.size)
n.dominant

Number of dominant QTL with effect size drawn from a gaussian distribution
n.equal.dominant

Number of n.equal.dominant QTL with equal effect size
n.qualitative

Number of qualitative epistatic QTL
n.quantitative

Number of quantitative epistatic QTL
dominant.only.positive

Set to TRUE to always asign the heterozygous variant with the higher of the two homozygous effects (e.g. hybrid breeding); default: FALSE
var.additive.l

Variance of additive QTL
var.dominant.l

Variance of dominante QTL
var.qualitative.l

Variance of qualitative epistatic QTL
var.quantitative.l

Variance of quantitative epistatic QTL
effect.size.equal.add

Effect size of the QTLs in n.equal.additive
effect.size.equal.dom

Effect size of the QTLs in n.equal.dominant
exclude.snps

Marker were no QTL are simulated on
replace.real.bv

If TRUE delete the simulated traits added before
shuffle.traits

Combine different traits into a joined trait
shuffle.cor

Target Correlation between shuffeled traits
skip.rest

Internal variable needed when adding multipe chromosomes jointly
enter.bv

Internal parameter
name.cohort

Name of the newly added cohort
template.chip

Import genetic map and chip from a species ("cattle", "chicken", "pig")
beta.shape1

First parameter of the beta distribution for simulating allele frequencies
beta.shape2

Second parameter of the beta distribution for simulating allele frequencies
time.point

Time point at which the new individuals are generated
creating.type

Technique to generate new individuals (usage in web-based application)
trait.name

Name of the trait generated
share.genotyped

Share of individuals genotyped in the founders
genotyped.s

Specify with newly added individuals are genotyped (1) or not (0)
map

map-file that contains up to 5 colums (Chromsome, SNP-id, M-position, Bp-position, allele freq - Everything not provides it set to NA). A map can be imported via MoBPSmaps::ensembl.map()
remove.invalid.qtl

Set to FALSE to deactive the automatic removal of QTLs on markers that do not exist
verbose

Set to FALSE to not display any prints
bv.standard

Set TRUE to standardize trait mean and variance via bv.standardization() - automatically set to TRUE when mean/var.target are used
mean.target

Target mean
var.target

Target variance
is.maternal

Vector coding if a trait is caused by a maternal effect (Default: all FALSE)
is.paternal

Vector coding if a trait is caused by a paternal effect (Default: all FALSE)
vcf.maxsnp

Maximum number of SNPs to include in the genotype file (default: Inf)
internal

Dont touch!

Value

Population-list

Examples

population <- creating.diploid(nsnp=1000, nindi=100)

Create a phenotypic transformation

Description

Function to perform create a transformation of phenotypes

Usage

creating.phenotypic.transform(
  population,
  phenotypic.transform.function = NULL,
  trait = 1
)

Arguments

population

Population list
phenotypic.transform.function

Phenotypic transformation to apply
trait

Trait for which a transformation is to be applied data(ex_pop) trafo <- function(x) return(x^2) ex_pop <- creating.phenotypic.transform(ex_pop, phenotypic.transform.function=trafo)

Value

Population-list with a new phenotypic transformation function

Generation of genomic traits

Description

Generation of the trait in a starting population

Usage

creating.trait(
  population,
  real.bv.add = NULL,
  real.bv.mult = NULL,
  real.bv.dice = NULL,
  bv.total = 0,
  polygenic.variance = 100,
  bve.mult.factor = NULL,
  bve.poly.factor = NULL,
  base.bv = NULL,
  new.phenotype.correlation = NULL,
  new.residual.correlation = NULL,
  new.breeding.correlation = NULL,
  n.additive = 0,
  n.equal.additive = 0,
  n.dominant = 0,
  n.equal.dominant = 0,
  n.qualitative = 0,
  n.quantitative = 0,
  dominant.only.positive = FALSE,
  var.additive.l = NULL,
  var.dominant.l = NULL,
  var.qualitative.l = NULL,
  var.quantitative.l = NULL,
  effect.size.equal.add = 1,
  effect.size.equal.dom = 1,
  exclude.snps = NULL,
  randomSeed = NULL,
  shuffle.traits = NULL,
  shuffle.cor = NULL,
  replace.traits = FALSE,
  trait.name = NULL,
  remove.invalid.qtl = TRUE,
  bv.standard = FALSE,
  mean.target = NULL,
  var.target = NULL,
  verbose = TRUE,
  is.maternal = NULL,
  is.paternal = NULL
)

Arguments

population

Population list
real.bv.add

Single Marker effects
real.bv.mult

Two Marker effects
real.bv.dice

Multi-marker effects
bv.total

Number of traits (If more than traits via real.bv.X use traits with no directly underlying QTL)
polygenic.variance

Genetic variance of traits with no underlying QTL
bve.mult.factor

Multiplicate trait value times this
bve.poly.factor

Potency trait value over this
base.bv

Average genetic value of a trait
new.phenotype.correlation

(OLD! - use new.residual.correlation) Correlation of the simulated enviromental variance
new.residual.correlation

Correlation of the simulated enviromental variance
new.breeding.correlation

Correlation of the simulated genetic variance (child share! heritage is not influenced!
n.additive

Number of additive QTL with effect size drawn from a gaussian distribution
n.equal.additive

Number of additive QTL with equal effect size (effect.size)
n.dominant

Number of dominant QTL with effect size drawn from a gaussian distribution
n.equal.dominant

Number of n.equal.dominant QTL with equal effect size
n.qualitative

Number of qualitative epistatic QTL
n.quantitative

Number of quantitative epistatic QTL
dominant.only.positive

Set to TRUE to always asign the heterozygous variant with the higher of the two homozygous effects (e.g. hybrid breeding); default: FALSE
var.additive.l

Variance of additive QTL
var.dominant.l

Variance of dominante QTL
var.qualitative.l

Variance of qualitative epistatic QTL
var.quantitative.l

Variance of quantitative epistatic QTL
effect.size.equal.add

Effect size of the QTLs in n.equal.additive
effect.size.equal.dom

Effect size of the QTLs in n.equal.dominant
exclude.snps

Marker were no QTL are simulated on
randomSeed

Set random seed of the process
shuffle.traits

Combine different traits into a joined trait
shuffle.cor

Target Correlation between shuffeled traits
replace.traits

If TRUE delete the simulated traits added before
trait.name

Name of the trait generated
remove.invalid.qtl

Set to FALSE to deactive the automatic removal of QTLs on markers that do not exist
bv.standard

Set TRUE to standardize trait mean and variance via bv.standardization()
mean.target

Target mean
var.target

Target variance
verbose

Set to FALSE to not display any prints
is.maternal

Vector coding if a trait is caused by a maternal effect (Default: all FALSE)
is.paternal

Vector coding if a trait is caused by a paternal effect (Default: all FALSE)

Value

Population-list with one or more additional new traits

Examples

population <- creating.diploid(nsnp=1000, nindi=100)
population <- creating.trait(population, n.additive=100)

Origins-Decoding(R)

Description

R-Version of the internal bitwise-decoding of origins

Usage

decodeOriginsR(P, row)

Arguments

P

coded origins vector
row

row to decode

Value

de-coded origins

Examples

decodeOriginsR(0L)

Remove miraculix-coding for genotypes

Description

Internal function to decode all genotypes to non-miraculix objects

Usage

demiraculix(population)

Arguments

population

Population list

Value

Population list

Examples

# This is only relevant with the package miraculix is installed and used
population <- creating.diploid(nsnp=100, nindi=50)
population <- demiraculix(population)

Derive loop elements

Description

Internal function to derive the position of all individuals to consider for BVE/GWAS

Usage

derive.loop.elements(
  population,
  bve.database,
  bve.class,
  bve.avoid.duplicates,
  store.adding = FALSE,
  store.which.adding = FALSE,
  list.of.copys = FALSE
)

Arguments

population

Population list
bve.database

Groups of individuals to consider in breeding value estimation
bve.class

Consider only animals of those class classes in breeding value estimation (default: NULL - use all)
bve.avoid.duplicates

If set to FALSE multiple generatations of the same individual can be used in the bve (only possible by using copy.individual to generate individuals)
store.adding

Internal parameter to derive number of added individuals per database entry (only relevant internally for GWAS)
store.which.adding

Internal parameter to derive which individuals are copy entries
list.of.copys

Internal parameter to derive further information on the copies individuals

Value

Matrix of individuals in the entered database

Examples

data(ex_pop)
derive.loop.elements(ex_pop, bve.database=get.database(ex_pop, gen=2),
bve.class=NULL, bve.avoid.duplicates=TRUE)

Add a genotyping array

Description

Function to add a genotyping array for the population

Usage

diag.mobps(elements)

Arguments

elements

vector with entries to put on the diagonal of a matrix

Value

Diagonal matrix

Examples

diag.mobps(5)

Detection of parental/child nodes

Description

Internal function to extract parental/child node of an edge

Usage

edges.fromto(edges)

Arguments

edges

Edges of the json-file generated via the web-interface

Value

Matrix of Parent/Child-nodes for the considered edges

Internal gene editing function

Description

Internal function to perform gene editing

Usage

edit_animal(
  population,
  gen,
  sex,
  nr,
  nr.edits,
  decodeOriginsU = decodeOriginsR,
  bit.storing = FALSE,
  nbits = 30
)

Arguments

population

Population list
gen

Generation of the individual to edit
sex

Gender of the individual to edit
nr

Number of the individual to edit
nr.edits

Number of edits to perform
decodeOriginsU

Used function for the decoding of genetic origins [[5]]/[[6]]
bit.storing

Set to TRUE if the MoBPS (not-miraculix! bit-storing is used)
nbits

Bits available in MoBPS-bit-storing

Value

animal after genome editing

Estimation of marker effects

Description

Function to estimate marker effects

Usage

effect.estimate.add(geno, pheno, map = NULL, scaling = TRUE)

Arguments

geno

genotype dataset (marker x individuals)
pheno

phenotype dataset (each phenotype in a row)
map

genomic map
scaling

Set FALSE to not perform variance scaling

Value

Empirical kinship matrix (IBD-based since Founders)

Examples

data(ex_pop)
pheno <- get.pheno(ex_pop, gen=1:5)
geno <- get.geno(ex_pop, gen=1:5)
map <- get.map(ex_pop, use.snp.nr=TRUE)
real.bv.add <- effect.estimate.add(geno, pheno, map)

Estimate effective population size

Description

Internal function to estimate the effective population size

Usage

effective.size(ld, dist, n)

Arguments

ld

ld between markers
dist

distance between markers in Morgan
n

Population size

Value

Estimated effective population size

Martini-Test function

Description

Internal function to perform martini test

Usage

epi(y, Z, G = NULL)

Arguments

y

y
Z

genomic information matrix
G

kinship matrix

Value

Estimated breeding values

ex_json

Description

Exemplary json-data

Usage

ex_json

Author(s)

Torsten Pook [email protected]

Source

Web-interface

ex_pop

Description

Exemplary population-list

Usage

ex_pop

Author(s)

Torsten Pook [email protected]

Source

MoBPS

Position detection (chromosome)

Description

Internal function for the detection on which chromosome each marker is

Usage

find.chromo(position, length.total)

Arguments

position

position in the genome
length.total

Length of each chromosome

Value

Chromosome the marker is part of

Position detection (SNPs)

Description

Internal function for the detection on which position each marker is

Usage

find.snpbefore(position, snp.position)

Arguments

position

Position on the genome
snp.position

Position of the SNPs on the genome

Value

SNP-position of the target position

Founder simulation

Description

Function to generate founder genotypes

Usage

founder.simulation(
  nindi = 100,
  sex.quota = 0.5,
  nsnp = 0,
  n.gen = 100,
  nfinal = NULL,
  sex.quota.final = NULL,
  big.output = FALSE,
  plot = TRUE,
  display.progress = TRUE,
  depth.pedigree = 7,
  dataset = NULL,
  vcf = NULL,
  chr.nr = NULL,
  bp = NULL,
  snp.name = NULL,
  hom0 = NULL,
  hom1 = NULL,
  bpcm.conversion = 0,
  freq = "beta",
  sex.s = "fixed",
  chromosome.length = NULL,
  length.before = 5,
  length.behind = 5,
  snps.equidistant = NULL,
  change.order = FALSE,
  snp.position = NULL,
  position.scaling = FALSE,
  bit.storing = FALSE,
  nbits = 30,
  randomSeed = NULL,
  miraculix = TRUE,
  miraculix.dataset = TRUE,
  template.chip = NULL,
  beta.shape1 = 1,
  beta.shape2 = 1,
  map = NULL,
  verbose = TRUE,
  vcf.maxsnp = Inf
)

Arguments

nindi

number of inidividuals to generate in a random dataset
sex.quota

Share of newly added female individuals (deterministic if sex.s="fixed", alt: sex.s="random")
nsnp

number of markers to generate in a random dataset
n.gen

Number of generations to simulate (default: 100)
nfinal

Number of final individuals to include (default: nindi)
sex.quota.final

Share of female individuals in the final generation
big.output

Set to TRUE to export map, population list and pedigree relationship
plot

Set to FALSE to not generate LD-decay plot and allele frequency spectrum
display.progress

Set FALSE to not display progress bars. Setting verbose to FALSE will automatically deactive progress bars
depth.pedigree

Depth of the pedigree in generations (default: 7)
dataset

SNP dataset, use "random", "allhetero" "all0" when generating a dataset via nsnp,nindi
vcf

Path to a vcf-file used as input genotypes (correct haplotype phase is assumed!)
chr.nr

Vector containing the assosiated chromosome for each marker (default: all on the same)
bp

Vector containing the physical position (bp) for each marker (default: 1,2,3...)
snp.name

Vector containing the name of each marker (default ChrXSNPY - XY chosen accordingly)
hom0

Vector containing the first allelic variant in each marker (default: 0)
hom1

Vector containing the second allelic variant in each marker (default: 1)
bpcm.conversion

Convert physical position (bp) into a cM position (default: 0 - not done)
freq

frequency of allele 1 when randomly generating a dataset
sex.s

Specify which newly added individuals are male (1) or female (2)
chromosome.length

Length of the newly added chromosome (default: 5)
length.before

Length before the first SNP of the dataset (default: 5)
length.behind

Length after the last SNP of the dataset (default: 5)
snps.equidistant

Use equidistant markers (computationally faster! ; default: TRUE)
change.order

If TRUE sort markers according to given marker positions
snp.position

Location of each marker on the genetic map
position.scaling

Manual scaling of snp.position
bit.storing

Set to TRUE if the MoBPS (not-miraculix! bit-storing is used)
nbits

Bits available in MoBPS-bit-storing
randomSeed

Set random seed of the process
miraculix

If TRUE use miraculix package for data storage, computations and dataset generation
miraculix.dataset

Set FALSE to deactive miraculix package for dataset generation
template.chip

Import genetic map and chip from a species ("cattle", "chicken", "pig")
beta.shape1

First parameter of the beta distribution for simulating allele frequencies
beta.shape2

Second parameter of the beta distribution for simulating allele frequencies
map

map-file that contains up to 5 colums (Chromsome, SNP-id, M-position, Bp-position, allele freq - Everything not provides it set to NA). A map can be imported via MoBPSmaps::ensembl.map()
verbose

Set to FALSE to not display any prints
vcf.maxsnp

Maximum number of SNPs to include in the genotype file (default: Inf)

Examples

population <- founder.simulation(nindi=100, nsnp=1000, n.gen=5)

Function to generate a new individual

Description

Function to generate a new individual

Usage

generation.individual(
  indexb,
  population,
  info_father_list,
  info_mother_list,
  copy.individual,
  mutation.rate,
  remutation.rate,
  recombination.rate,
  recom.f.indicator,
  duplication.rate,
  duplication.length,
  duplication.recombination,
  delete.same.origin,
  gene.editing,
  nr.edits,
  gen.architecture.m,
  gen.architecture.f,
  decodeOriginsU,
  current.gen,
  save.recombination.history,
  new.bv.child,
  dh.mating,
  share.genotyped,
  added.genotyped,
  genotyped.array,
  dh.sex,
  n.observation
)

Arguments

indexb

windows parallel internal test
population

windows parallel internal test
info_father_list

windows parallel internal test
info_mother_list

windows parallel internal test
copy.individual

windows parallel internal test
mutation.rate

windows parallel internal test
remutation.rate

windows parallel internal test
recombination.rate

windows parallel internal test
recom.f.indicator

windows parallel internal test
duplication.rate

windows parallel internal test
duplication.length

windows parallel internal test
duplication.recombination

windows parallel internal test
delete.same.origin

windows parallel internal test
gene.editing

windows parallel internal test
nr.edits

windows parallel internal test
gen.architecture.m

windows parallel internal test
gen.architecture.f

windows parallel internal test
decodeOriginsU

windows parallel internal test
current.gen

windows parallel internal test
save.recombination.history

windows parallel internal test
new.bv.child

windows parallel internal test
dh.mating

windows parallel internal test
share.genotyped

windows parallel internal test
added.genotyped

windows parallel internal test
genotyped.array

windows parallel internal test
dh.sex

windows parallel internal test
n.observation

windows parallel internal test

Value

Offspring individual

Admixture Plot

Description

Function to generate admixture plots

Usage

get.admixture(
  population,
  geno = NULL,
  gen = NULL,
  database = NULL,
  cohorts = NULL,
  d = NULL,
  verbose = TRUE,
  plot = TRUE,
  sort = FALSE,
  sort.cutoff = 0.01
)

Arguments

population

Population list
geno

Manually provided genotype dataset to use instead of gen/database/cohorts
gen

Quick-insert for database (vector of all generations to consider)
database

Groups of individuals to consider
cohorts

Quick-insert for database (vector of names of cohorts to consider)
d

dimensions to consider in admixture plot (default: automatically estimate a reasonable number)
verbose

Set to FALSE to not display any prints
plot

Set to FALSE to not generate an admixture plot
sort

Set to TRUE to sort individuals according to contributes from the first dimension
sort.cutoff

Skip individuals with contributions under this threshold (and use next dimension instead) data(ex_pop) get.admixture(ex_pop, gen=4:6, d=2, sort=TRUE)

Value

Matrix with admixture proportion

Derive age point

Description

Function to devide age point for each individual (Same as time.point unless copy.individual is used for aging)

Usage

get.age.point(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  use.id = FALSE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names (default: FALSE)

Value

Time point selected gen/database/cohorts-individuals are born

Examples

data(ex_pop)
get.age.point(ex_pop, gen=2)

Export underlying true breeding values

Description

Function to export underlying true breeding values

Usage

get.bv(population, database = NULL, gen = NULL, cohorts = NULL, use.id = FALSE)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names (default: FALSE)

Value

Genomic value of in gen/database/cohorts selected individuals

Examples

data(ex_pop)
get.bv(ex_pop, gen=2)

Export estimated breeding values

Description

Function to export estimated breeding values

Usage

get.bve(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  use.id = FALSE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names (default: FALSE)

Value

Estimated breeding value of in gen/database/cohorts selected individuals

Examples

data(ex_pop)
get.bve(ex_pop, gen=2)

Derive class

Description

Function to devide the class for each individual

Usage

get.class(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  use.id = FALSE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names (default: FALSE)

Value

Class of in gen/database/cohorts selected individuals

Examples

data(ex_pop)
get.class(ex_pop, gen=2)

Export Cohort-names

Description

Function to export cohort names for the population list

Usage

get.cohorts(population, extended = FALSE)

Arguments

population

Population list
extended

extended cohorts

Value

List of all cohorts in the population-list

Examples

data(ex_pop)
get.cohorts(ex_pop)

Derive creating type

Description

Function to devide creating type for each individual

Usage

get.creating.type(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  use.id = FALSE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names (default: FALSE)

Value

Creating type of in gen/database/cohorts selected individuals

Examples

data(ex_pop)
get.creating.type(ex_pop, gen=2)

Derive time of culling

Description

Function to devide the time of culling for all individuals

Usage

get.cullingtime(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  use.id = FALSE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names (default: FALSE)

Value

Time of death of in gen/database/cohorts selected individuals

Examples

data(ex_pop)
get.cullingtime(ex_pop, gen=2)

gen/database/cohorts conversion

Description

Function to derive a database based on gen/database/cohorts

Usage

get.database(
  population,
  gen = NULL,
  database = NULL,
  cohorts = NULL,
  avoid.merging = FALSE
)

Arguments

population

Population list
gen

Quick-insert for database (vector of all generations to export)
database

Groups of individuals to consider for the export
cohorts

Quick-insert for database (vector of names of cohorts to export)
avoid.merging

Set to TRUE to avoid different cohorts to be merged in a joint group when possible

Value

Combine gen/database/cohorts to a joined database

Examples

data(ex_pop)
get.database(ex_pop, gen=2)

Derive death point

Description

Function to devide the time of death for each individual (NA for individuals that are still alive))

Usage

get.death.point(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  use.id = FALSE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names (default: FALSE)

Value

Time of death of in gen/database/cohorts selected individuals

Examples

data(ex_pop)
get.death.point(ex_pop, gen=2)

Dendrogram

Description

Function calculate a dendogram

Usage

get.dendrogram(
  population,
  path = NULL,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  method = NULL,
  individual.names = NULL
)

Arguments

population

Population list
path

provide a path if the dendrogram would be saved as a png-file
database

Groups of individuals to consider
gen

Quick-insert for database (vector of all generations to consider)
cohorts

Quick-insert for database (vector of names of cohorts to consider)
method

Method used to calculate genetic distances (default: "Nei", alt: "Rogers", "Prevosti", "Modified Rogers"
individual.names

Names of the individuals in the database ((default are MoBPS internal names based on position))

Value

Dendrogram plot for genotypes

Examples

data(ex_pop)
get.dendrogram(ex_pop, gen=2)

Dendrogram Heatmap

Description

Function calculate a dendogram

Usage

get.dendrogram.heatmap(
  population,
  path = NULL,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  method = NULL,
  individual.names = NULL,
  traits = NULL,
  type = "pheno"
)

Arguments

population

Population list
path

provide a path if the dendrogram would be saved as a png-file
database

Groups of individuals to consider
gen

Quick-insert for database (vector of all generations to consider)
cohorts

Quick-insert for database (vector of names of cohorts to consider)
method

Method used to calculate genetic distances (default: "Nei", alt: "Rogers", "Prevosti", "Modified Rogers"
individual.names

Names of the individuals in the database ((default are MoBPS internal names based on position))
traits

Traits to include in the dendrogram (default: all traits)
type

Which traits values to consider (default: "pheno", alt: "bv", "bve")

Value

Dendrogram plot of genotypes vs phenotypes

Examples

population <- creating.diploid(nsnp=1000, nindi=40, n.additive = c(100,100,100),
               shuffle.cor = matrix(c(1,0.8,0.2,0.8,1,0.2,0.2,0.2,1), ncol=3), shuffle.traits = 1:3)
population <- breeding.diploid(population, phenotyping = "all", heritability = 0.5)
get.dendrogram.heatmap(population, gen=1, type="pheno")

Dendrogram

Description

Function calculate a dendogram for the traits

Usage

get.dendrogram.trait(
  population,
  path = NULL,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  traits = NULL,
  type = "pheno"
)

Arguments

population

Population list
path

provide a path if the dendrogram would be saved as a png-file
database

Groups of individuals to consider
gen

Quick-insert for database (vector of all generations to consider)
cohorts

Quick-insert for database (vector of names of cohorts to consider)
traits

Traits to include in the dendrogram (default: all traits)
type

Which traits values to consider (default: "pheno", alt: "bv", "bve")

Value

Dendrogram plot for traits

Examples

population <- creating.diploid(nsnp=1000, nindi=100, n.additive = c(100,100,100),
               shuffle.cor = matrix(c(1,0.8,0.2,0.8,1,0.2,0.2,0.2,1), ncol=3), shuffle.traits = 1:3)
population <- breeding.diploid(population, phenotyping = "all", heritability = 0.5)
get.dendrogram.trait(population, gen=1, type="pheno")

Calculate Nei distance between two or more population

Description

Function to calculate Nei's distance between two or more population

Usage

get.distance(
  population,
  type = "nei",
  marker = "all",
  per.marker = FALSE,
  gen1 = NULL,
  database1 = NULL,
  cohorts1 = NULL,
  gen2 = NULL,
  database2 = NULL,
  cohorts2 = NULL,
  database.list = NULL,
  gen.list = NULL,
  cohorts.list = NULL
)

Arguments

population

population list
type

Chose type of distance to compute (default: Neis standard genetic distance "nei"). Alt: Reynolds distance ("reynold"), Cavalli-Sforza ("cavalli"), Neis distance ("nei_distance"), Neis minimum distance ("nei_minimum")
marker

Vector with SNPs to consider (Default: "all" - use of all markers)
per.marker

Set to TRUE to return per marker statistics on genetic distances
gen1

Quick-insert for database (vector of all generations to consider)
database1

First Groups of individuals to consider
cohorts1

Quick-insert for database (vector of names of cohorts to consider)
gen2

Quick-insert for database (vector of all generations to consider)
database2

Second Groups of individuals to consider
cohorts2

Quick-insert for database (vector of names of cohorts to consider)
database.list

List of databases to consider (use when working with more than 2 populations)
gen.list

Quick-insert for database (vector of all generations to consider)
cohorts.list

Quick-insert for database (vector of names of cohorts to consider)

Value

Population list

Examples

data(ex_pop)
get.distance(ex_pop, database1 = cbind(1,1), database2 = cbind(1,2))

Compute marker frequency in QTL-markers

Description

Function to compute marker frequency in QTL-markers

Usage

get.effect.freq(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  sort = FALSE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
sort

Set to FALSE to not sort markers according to position on the genome

Value

Matrix with allele frequencies in the QTLs

Examples

data(ex_pop)
get.effect.freq(ex_pop, gen=1)

Estimate effective population size

Description

Function to estimate the effective population size

Usage

get.effective.size(population, gen = NULL, database = NULL, cohorts = NULL)

Arguments

population

Population list
gen

Quick-insert for database (vector of all generations to export)
database

Groups of individuals to consider for the export
cohorts

Quick-insert for database (vector of names of cohorts to export)

Value

Estimated effective population size

Examples

data(ex_pop)
get.effective.size(population=ex_pop, gen=5)

Derive genotypes of selected individuals

Description

Function to devide genotypes of selected individuals

Usage

get.geno(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  chromosomen = "all",
  export.alleles = FALSE,
  non.genotyped.as.missing = FALSE,
  use.id = FALSE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
chromosomen

Beschraenkung des Genotypen auf bestimmte Chromosomen (default: 1)
export.alleles

If TRUE export underlying alleles instead of just 012
non.genotyped.as.missing

Set to TRUE to replace non-genotyped markers with NA
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names (default: FALSE)

Value

Genotype data for in gen/database/cohorts selected individuals

Examples

data(ex_pop)
geno <- get.geno(ex_pop, gen=2)

Derive genotyping status

Description

Function to if selected individuals are genotyped

Usage

get.genotyped(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  use.id = FALSE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names (default: FALSE)

Value

Check if in gen/database/cohorts selected individuals are genotyped

Examples

data(ex_pop)
get.genotyped(ex_pop, gen=2)

Derive which markers are genotyped of selected individuals

Description

Function to devide which markers are genotyped for the selected individuals

Usage

get.genotyped.snp(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  export.alleles = FALSE,
  use.id = FALSE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
export.alleles

If TRUE export underlying alleles instead of just 012
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names (default: FALSE)

Value

Binary Coded is/isnot genotyped level for in gen/database/cohorts selected individuals

Examples

data(ex_pop)
genotyped.snps <- get.genotyped.snp(ex_pop, gen=2)

Derive haplotypes of selected individuals

Description

Function to devide haplotypes of selected individuals

Usage

get.haplo(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  chromosomen = "all",
  export.alleles = FALSE,
  non.genotyped.as.missing = FALSE,
  use.id = FALSE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
chromosomen

Beschraenkung der Haplotypen auf bestimmte Chromosomen (default: 1)
export.alleles

If TRUE export underlying alleles instead of just 012
non.genotyped.as.missing

Set to TRUE to replace non-genotyped markers with NA
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names (default: FALSE)

Value

Haplotype data for in gen/database/cohorts selected individuals

Examples

data(ex_pop)
haplo <- get.haplo(ex_pop, gen=2)

Derive ID on an individual

Description

Function to derive the internal ID given to each individual

Usage

get.id(population, database = NULL, gen = NULL, cohorts = NULL, use.id = FALSE)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names

Value

Individual ID for in gen/database/cohorts selected individuals

Examples

data(ex_pop)
get.id(ex_pop, gen=2)

Export location of individuals from the population list

Description

Export location of individuals from the population list

Usage

get.individual.loc(population, database = NULL, gen = NULL, cohorts = NULL)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)

Value

Storage Position for in gen/database/cohorts selected individuals (Generation/Sex/IndividualNr)

Examples

data(ex_pop)
get.individual.loc(ex_pop, gen=2)

Extract bv/pheno/geno of selected individuals

Description

Function to extract bv/pheno/geno of selected individuals

Usage

get.infos(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  use.id = FALSE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names (default: FALSE)

Value

Info list [[1]] phenotypes [[2]] genomic values [[3]] Z [[4/5/6]] additive/epistatic/dice marker effects

Examples

data(ex_pop)
get.infos(ex_pop, gen=2)

Map generation

Description

Function to derive the genomic map for a given population list

Usage

get.map(population, use.snp.nr = FALSE)

Arguments

population

Population list
use.snp.nr

Set to TRUE to display SNP number and not SNP name

Value

Genomic map of the population list

Examples

data(ex_pop)
map <- get.map(ex_pop)

Export underlying number of observations per phenotype

Description

Function to export the number of observation of each underlying phenotype

Usage

get.npheno(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  use.all.copy = FALSE,
  use.id = FALSE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
use.all.copy

Set to TRUE to extract phenotyping
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names (default: FALSE)

Value

Phenotypes for in gen/database/cohorts selected individuals

Examples

data(ex_pop)
get.pheno(ex_pop, gen=2)

Principle components analysis

Description

Function to perform a principle component analysis

Usage

get.pca(
  population,
  path = NULL,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  coloring = "group",
  components = c(1, 2),
  plot = TRUE,
  pch = 1,
  export.color = FALSE
)

Arguments

population

Population list
path

Location were to save the PCA-plot
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
coloring

Coloring by "group", "sex", "plain"
components

Default: c(1,2) for the first two principle components
plot

Set to FALSE to not generate a plot
pch

Point type in the PCA plot
export.color

Set to TRUE to export the per point coloring

Value

Genotype data for in gen/database/cohorts selected individuals

Examples

data(ex_pop)
get.pca(ex_pop, gen=2)

Derive pedigree

Description

Derive pedigree for selected individuals

Usage

get.pedigree(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  founder.zero = TRUE,
  raw = FALSE,
  id = FALSE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
founder.zero

Parents of founders are displayed as "0" (default: TRUE)
raw

Set to TRUE to not convert numbers into Sex etc.
id

Set to TRUE to extract individual IDs

Value

Pedigree-file for in gen/database/cohorts selected individuals

Examples

data(ex_pop)
get.pedigree(ex_pop, gen=2)

Derive pedigree including grandparents

Description

Derive pedigree for selected individuals including grandparents

Usage

get.pedigree2(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  shares = FALSE,
  founder.zero = TRUE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
shares

Determine actual inherited shares of grandparents
founder.zero

Parents of founders are displayed as "0" (default: TRUE)

Value

Pedigree-file (grandparents) for in gen/database/cohorts selected individuals

Examples

data(ex_pop)
get.pedigree2(ex_pop, gen=2)

Derive pedigree parents and grandparents

Description

Derive pedigree for selected individuals including parents/grandparents

Usage

get.pedigree3(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  founder.zero = TRUE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
founder.zero

Parents of founders are displayed as "0" (default: TRUE)

Value

Pedigree-file (parents + grandparents) for in gen/database/cohorts selected individuals

Examples

data(ex_pop)
get.pedigree3(ex_pop, gen=3)

Generate plink-file (pedmap)

Description

Generate a ped and map file (PLINK format) for selected groups and chromosome

Usage

get.pedmap(
  population,
  path = NULL,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  non.genotyped.as.missing = FALSE,
  use.id = FALSE
)

Arguments

population

Population list
path

Location to save pedmap-file
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
non.genotyped.as.missing

Set to TRUE to replaced non-genotyped entries with "./."
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names

Value

Ped and map-file for in gen/database/cohorts selected individuals

Examples

data(ex_pop)

file_path <- tempdir()
get.pedmap(path=file_path, ex_pop, gen=2)
file.remove(paste0(file_path, ".ped"))
file.remove(paste0(file_path, ".map"))

Export underlying phenotypes

Description

Function to export underlying phenotypes

Usage

get.pheno(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  use.all.copy = FALSE,
  use.id = FALSE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
use.all.copy

Set to TRUE to extract phenotyping
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names (default: FALSE)

Value

Phenotypes for in gen/database/cohorts selected individuals

Examples

data(ex_pop)
get.pheno(ex_pop, gen=2)

Export underlying offspring phenotypes

Description

Function to export offspring phenotypes

Usage

get.pheno.off(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  use.id = FALSE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names (default: FALSE)

Value

Avg. phenotype of the offspring of in gen/database/cohorts selected individuals

Examples

data(ex_pop)
get.pheno.off(ex_pop, gen=2)

Export underlying number of used offspring for offspring phenotypes

Description

Function to export number of observations used for offspring phenotypes

Usage

get.pheno.off.count(population, database = NULL, gen = NULL, cohorts = NULL)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)

Value

Number of offspring with phenotypes for in gen/database/cohorts selected individuals

Examples

data(ex_pop)
get.pheno.off.count(ex_pop, gen=2)

Phylogenetic Tree

Description

Function calculate a phylogenetic tree

Usage

get.phylogenetic.tree(
  population,
  path = NULL,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  method = NULL,
  individual.names = NULL,
  circular = FALSE
)

Arguments

population

Population list
path

provide a path if the dendrogram would be saved as a png-file
database

Groups of individuals to consider
gen

Quick-insert for database (vector of all generations to consider)
cohorts

Quick-insert for database (vector of names of cohorts to consider)
method

Method used to calculate genetic distances (default: "Nei", alt: "Rogers", "Prevosti", "Modified Rogers"
individual.names

Names of the individuals in the database ((default are MoBPS internal names based on position))
circular

Set to TRUE to generate a fan/circular layout tree

Value

Dendrogram plot for traits

Examples

data(ex_pop)
get.phylogenetic.tree(ex_pop, gen=1, circular=TRUE)

QTL extraction

Description

Function to the position of QTLs (for snp/chr use get.qtl.effects()

Usage

get.qtl(population)

Arguments

population

Population list

Value

Vector of SNP positions

Examples

data(ex_pop)
positions <- get.qtl(ex_pop)

QTL effect extraction

Description

Function to extract QTL effect sizes

Usage

get.qtl.effects(population)

Arguments

population

Population list

Value

List with [[1]] single SNP QTLs [[2]] epistatic SNP QTLs [[3]] dice QTL

Examples

data(ex_pop)
effects <- get.qtl.effects(ex_pop)

QTL effect variance extraction

Description

Function to extract QTL effect variance for single SNP QTLs in a given gen/database/cohort

Usage

get.qtl.variance(population, gen = NULL, database = NULL, cohorts = NULL)

Arguments

population

Population list
gen

Quick-insert for database (vector of all generations to consider)
database

Groups of individuals to consider
cohorts

Quick-insert for database (vector of names of cohorts to consider)

Value

matrix with SNP / Chr / estimated effect variance

Examples

data(ex_pop)
effects <- get.qtl.variance(ex_pop)

Derive genetic origins

Description

Function to derive genetic origin

Usage

get.recombi(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  use.id = FALSE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names (default: FALSE)

Value

Recombination points for in gen/database/cohorts selected individuals

Examples

data(ex_pop)
get.recombi(ex_pop, gen=2)

Export underlying reliabilities

Description

Function to export underlying reliabilities

Usage

get.reliabilities(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  use.id = FALSE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names (default: FALSE)

Value

Estimated reliability for BVE for in gen/database/cohorts selected individuals

Examples

data(ex_pop)
get.reliabilities(ex_pop, gen=2)

Export derived breeding values based on the selection index

Description

Function to export last breeding values based on the selection index

Usage

get.selectionbve(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  use.id = FALSE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names (default: FALSE)

Value

Last applied selection index for in gen/database/cohorts selected individuals

Examples

data(ex_pop)
get.selectionindex(ex_pop, gen=2)

Export underlying last used selection index

Description

Function to export last used selection index (mostly relevant for Miesenberger 1997 stuff)

Usage

get.selectionindex(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  use.id = FALSE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names (default: FALSE)

Value

Last applied selection index for in gen/database/cohorts selected individuals

Examples

data(ex_pop)
get.selectionindex(ex_pop, gen=2)

Derive time point

Description

Function to devide time point for each individual

Usage

get.time.point(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  use.id = FALSE
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names (default: FALSE)

Value

Time point of generation for in gen/database/cohorts selected individuals

Examples

data(ex_pop)
get.time.point(ex_pop, gen=2)

Generate vcf-file

Description

Generate a vcf-file for selected groups and chromosome

Usage

get.vcf(
  population,
  path = NULL,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  chromosomen = "all",
  non.genotyped.as.missing = FALSE,
  use.id = FALSE
)

Arguments

population

Population list
path

Location to save vcf-file
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
chromosomen

Beschraenkung des Genotypen auf bestimmte Chromosomen (default: 1)
non.genotyped.as.missing

Set to TRUE to replaced non-genotyped entries with "./."
use.id

Set to TRUE to use MoBPS ids instead of Sex_Nr_Gen based names

Value

VCF-file for in gen/database/cohorts selected individuals

Examples

data(ex_pop)
data(ex_pop)

file_path <- tempdir()
get.vcf(path=file_path, ex_pop, gen=2)
file.remove(paste0(file_path, ".vcf"))

Function to exclude individuals from a database

Description

Function to exclude individuals from a database

Usage

group.diff(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  remove.gen = NULL,
  remove.database = NULL,
  remove.cohorts = NULL
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
remove.gen

Generations of individuals to remove from the database (same IDs!)
remove.database

Groups of individuals to remove from the database (same IDs!)
remove.cohorts

Cohorts of individuals to remove from the database (same IDs!)

Value

Database excluding removals

Examples

data(ex_pop)
database <- group.diff(ex_pop, gen=1, remove.database=cbind(1,1))

Manually enter estimated breeding values

Description

Function to manually enter estimated breeding values

Usage

insert.bve(
  population,
  bves,
  type = "bve",
  na.override = FALSE,
  count = 1,
  count.only.increase = TRUE
)

Arguments

population

Population list
bves

Matrix of breeding values to enter (one row per individual with 1 element coding individual name)
type

which time of values to input (default: "bve", alt: "bv", "pheno")
na.override

Set to TRUE to also enter NA values (Default: FALSE - those entries will be skipped)
count

Counting for economic cost calculation (default: 1 - (one observation (for "pheno"), one genotyping (for "bve")))
count.only.increase

Set to FALSE to reduce the number of observation for a phenotype to "count" (default: TRUE)

Value

Population-List with newly entered estimated breeding values

Examples

data(ex_pop)
bv <- get.bv(ex_pop, gen=2)
new.bve <- cbind( colnames(bv), bv[,1]) ## Unrealistic but you do not get better than this!
ex_pop <- insert.bve(ex_pop, bves=new.bve)

Simulation of a breeding program based on a JSON-file from MoBPSweb

Description

Function to simulate a breeding program based on a JSON-file from MoBPSweb

Usage

json.simulation(
  file = NULL,
  log = NULL,
  total = NULL,
  fast.mode = FALSE,
  progress.bars = FALSE,
  size.scaling = NULL,
  rep.max = 1,
  verbose = TRUE,
  miraculix.cores = NULL,
  miraculix.chol = NULL,
  skip.population = FALSE,
  time.check = FALSE,
  time.max = 7200,
  export.population = FALSE,
  export.gen = NULL,
  export.timepoint = NULL,
  fixed.generation.order = NULL
)

Arguments

file

Path to a json-file generated by the user-interface
log

Provide Path where to write a log-file of your simulation (or false to not write a log-file)
total

Json-file imported via jsonlite::read_json
fast.mode

Set to TRUE work on a small genome with few markers
progress.bars

Set to TRUE to display progress bars
size.scaling

Scale the size of nodes by this factor (especially for testing smaller examples)
rep.max

Maximum number of repeats to use in fast.mode
verbose

Set to FALSE to not display any prints
miraculix.cores

Number of cores used in miraculix applications (default: 1)
miraculix.chol

Set to FALSE to manually deactive the use of miraculix for any cholesky decompostion even though miraculix is actived
skip.population

Set to TRUE to not execute breeding actions (only cost/time estimation will be performed)
time.check

Set to TRUE to automatically check simulation run-time before executing breeding actions
time.max

Maximum length of the simulation in seconds when time.check is active
export.population

Path were to export the population to (at state selected in export.gen/timepoint)
export.gen

Last generation to simulate before exporting population to file
export.timepoint

Last timepoint to simulate before exporting population to file
fixed.generation.order

Vector containing the order of cohorts to generate (Advanced // Testing Parameter!)

Value

Population-list

Examples

data(ex_json)
population <- json.simulation(total=ex_json)

Devolopment of genetic/breeding value

Description

Function to plot genetic/breeding values for multiple generation/cohorts

Usage

kinship.development(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  json = FALSE,
  ibd.obs = 50,
  hbd.obs = 10,
  display.cohort.name = FALSE,
  display.time.point = FALSE,
  equal.spacing = FALSE,
  time_reorder = FALSE,
  display.hbd = FALSE
)

Arguments

population

population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
json

If TRUE extract which cohorts to plot according to the json-file used in json.simulation
ibd.obs

Number of Individual pairs to sample for IBD estimation
hbd.obs

Number of Individuals to sample for HBD estimation
display.cohort.name

Set TRUE to display the name of the cohort in the x-axis
display.time.point

Set TRUE to use time point of generated to sort groups
equal.spacing

Equal distance between groups (independent of time.point)
time_reorder

Set TRUE to order cohorts according to the time point of generation
display.hbd

Set to TRUE to also display HBD in plot

Value

Estimated of avg. kinship/inbreeding based on IBD/HBD

Examples

data(ex_pop)
kinship.development(ex_pop,gen=1:5)

Empirical kinship

Description

Function to compute empirical kinship for a set of individuals)

Usage

kinship.emp(
  animals = NULL,
  population = NULL,
  gen = NULL,
  database = NULL,
  cohorts = NULL,
  sym = FALSE
)

Arguments

animals

List of animals to compute kinship for
population

Population list
gen

Quick-insert for database (vector of all generations to export)
database

Groups of individuals to consider for the export
cohorts

Quick-insert for database (vector of names of cohorts to export)
sym

If True derive matrix entries below principle-diagonal

Value

Empirical kinship matrix (IBD-based since Founders)

Examples

data(ex_pop)
kinship <- kinship.emp(population=ex_pop, database=cbind(2,1,1,25))

Approximate empirical kinship

Description

Function to compute empirical kinship for a set of individuals (not all pairs of individuals are evaluated)

Usage

kinship.emp.fast(
  animals = NULL,
  population = NULL,
  gen = NULL,
  database = NULL,
  cohorts = NULL,
  sym = FALSE,
  ibd.obs = 50,
  hbd.obs = 10
)

Arguments

animals

List of animals to compute kinship for
population

Population list
gen

Quick-insert for database (vector of all generations to export)
database

Groups of individuals to consider for the export
cohorts

Quick-insert for database (vector of names of cohorts to export)
sym

If True derive matrix entries below principle-diagonal
ibd.obs

Number of Individual pairs to sample for IBD estimation
hbd.obs

Number of Individuals to sample for HBD estimation

Value

Empirical kinship matrix (IBD-based since Founders) per gen/database/cohort

Examples

data(ex_pop)
kinship.emp.fast(population=ex_pop,gen=2)

Derive expected kinship

Description

Function to derive expected kinship

Usage

kinship.exp(
  population,
  gen = NULL,
  database = NULL,
  cohorts = NULL,
  depth.pedigree = 7,
  start.kinship = NULL,
  elements = NULL,
  mult = 2,
  storage.save = 1.5,
  verbose = TRUE
)

Arguments

population

Population list
gen

Quick-insert for database (vector of all generations to export)
database

Groups of individuals to consider for the export
cohorts

Quick-insert for database (vector of names of cohorts to export)
depth.pedigree

Depth of the pedigree in generations
start.kinship

Relationship matrix of the individuals in the first considered generation
elements

Vector of individuals from the database to include in pedigree matrix
mult

Multiplicator of kinship matrix (default: 2)
storage.save

Lower numbers will lead to less memory but slightly higher computing time (default: 1.5, min: 1)
verbose

Set to FALSE to not display any prints

Value

Pedigree-based kinship matrix for in gen/database/cohort selected individuals

Examples

data(ex_pop)
kinship <- kinship.exp(population=ex_pop, gen=2)

Generate LD plot

Description

Generate LD pot

Usage

ld.decay(
  population,
  genotype.dataset = NULL,
  chromosomen = 1,
  dist = NULL,
  step = 5,
  max = 500,
  max.cases = 100,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  type = "snp",
  plot = FALSE
)

Arguments

population

Population list
genotype.dataset

Genotype dataset (default: NULL - just to save computation time when get.geno was already run)
chromosomen

Only consider a specific chromosome in calculations (default: 1)
dist

Manuel input of marker distances to analyse
step

Stepsize to calculate LD
max

Maximum distance between markers to consider for LD-plot
max.cases

Maximum number of marker pairs to consider of each distance (default: 100; randomly sampled!)
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
type

Compute LD decay according to following distance measure between markers (default: "snp", alt: "bp", "cM")
plot

Set to FALSE to not generate an LD plot

Value

LD-decay plot for in gen/database/cohorts selected individuals

Examples

data(ex_pop)
ld.decay(population=ex_pop, gen=5)

maize chip

Description

Genome for maize according to Lee et al.

Usage

maize_chip

Author(s)

Torsten Pook [email protected]

Source

Lee et al 2002

Miesenberger Index

Description

Function to selection index weights according to Miesenberger 1997

Usage

miesenberger.index(V, G, V1 = NULL, RG = NULL, r, w, zw = NULL)

Arguments

V

Phenotypic covarianz matrix
G

Genomic covarianz matrix
V1

Inverted phenotypic covarianz matrix
RG

Genomic correlation matrix
r

reliability for the breeding value estimation
w

relative weighting of each trait (per genetic SD)
zw

Estimated breeding value

Value

weights of the selection index

Add miraculix-coding for genotypes

Description

Internal function to store genotypes bit-wise

Usage

miraculix(population)

Arguments

population

Population list

Value

Population list

Examples

# This is only relevant with the package miraculix is installed and used
population <- creating.diploid(nsnp=100, nindi=50, miraculix=FALSE)
population <- miraculix(population)

Mutation intro

Description

Function to change the base-pair in a specific loci

Usage

mutation.intro(population, gen, sex, individual.nr, qtl.posi, haplo.set = 1)

Arguments

population

Population list
gen

Generation of the individual to introduce a mutation in
sex

Sex of the individual to introduce a mutation in
individual.nr

Individual Nr. of the individual to introduce a mutation in
qtl.posi

Marker number to mutate
haplo.set

Select chromosome set (default: 1 , alt: 2)

Value

Population-List with mutated marker for the selected individual

Examples

data(ex_pop)
ex_pop <- mutation.intro(ex_pop, 1,1,1, qtl.posi=100)

Set new base generation

Description

Function to set a new base generation for the population

Usage

new.base.generation(
  population,
  base.gen = NULL,
  delete.previous.gen = FALSE,
  delete.breeding.totals = FALSE,
  delete.bve.data = FALSE,
  add.chromosome.ends = TRUE
)

Arguments

population

Population list
base.gen

Vector containing all new base generations
delete.previous.gen

Delete all data before base.gen (default: FALSE)
delete.breeding.totals

Delete all breeding totals before base.gen (default: FALSE)
delete.bve.data

Deleta all previous bve data (default: FALSE)
add.chromosome.ends

Add chromosome ends as recombination points

Value

Population-List with mutated marker for the selected individual

Examples

data(ex_pop)
ex_pop <- new.base.generation(ex_pop, base.gen=2)

Optimal genetic contribution

Description

In this function the OGC selection according to Meuwissen 1997 is performed

Usage

OGC(
  A,
  u,
  Q,
  cAc = NA,
  single = TRUE,
  verbose = FALSE,
  max_male = Inf,
  max_female = Inf
)

Arguments

A

relationship matrix
u

breeding values
Q

sex indicator
cAc

target gain in inbreeding
single

If FALSE multiple individuals can be removed at the same type (this is faster but potentially inaccurate!)
verbose

Set to FALSE to not display any prints
max_male

maximum number of male with positive contributions
max_female

maximum number of females with positive contributions

Value

[[1]] Contributions [[2]] expected inbreeding gain

Simulation of a given pedigree

Description

Function to simulate a given pedigree

Usage

pedigree.simulation(
  pedigree,
  keep.ids = FALSE,
  plot = TRUE,
  dataset = NULL,
  vcf = NULL,
  chr.nr = NULL,
  bp = NULL,
  snp.name = NULL,
  hom0 = NULL,
  hom1 = NULL,
  bpcm.conversion = 0,
  nsnp = 0,
  freq = "beta",
  sex.s = "fixed",
  chromosome.length = NULL,
  length.before = 5,
  length.behind = 5,
  real.bv.add = NULL,
  real.bv.mult = NULL,
  real.bv.dice = NULL,
  snps.equidistant = NULL,
  change.order = FALSE,
  bv.total = 0,
  polygenic.variance = 100,
  bve.mult.factor = NULL,
  bve.poly.factor = NULL,
  base.bv = NULL,
  add.chromosome.ends = TRUE,
  new.phenotype.correlation = NULL,
  new.residual.correlation = NULL,
  new.breeding.correlation = NULL,
  add.architecture = NULL,
  snp.position = NULL,
  position.scaling = FALSE,
  bit.storing = FALSE,
  nbits = 30,
  randomSeed = NULL,
  miraculix = TRUE,
  miraculix.dataset = TRUE,
  n.additive = 0,
  n.dominant = 0,
  n.qualitative = 0,
  n.quantitative = 0,
  var.additive.l = NULL,
  var.dominant.l = NULL,
  var.qualitative.l = NULL,
  var.quantitative.l = NULL,
  exclude.snps = NULL,
  replace.real.bv = FALSE,
  shuffle.traits = NULL,
  shuffle.cor = NULL,
  skip.rest = FALSE,
  enter.bv = TRUE,
  name.cohort = NULL,
  template.chip = NULL,
  beta.shape1 = 1,
  beta.shape2 = 1,
  time.point = 0,
  creating.type = 0,
  trait.name = NULL,
  share.genotyped = 1,
  genotyped.s = NULL,
  map = NULL,
  remove.invalid.qtl = TRUE,
  verbose = TRUE,
  bv.standard = FALSE,
  mean.target = NULL,
  var.target = NULL,
  is.maternal = NULL,
  is.paternal = NULL,
  vcf.maxsnp = Inf
)

Arguments

pedigree

Pedigree-file (matrix with 3 columns (Individual ID, Father ID, Mother ID), optional forth columns with earliest generations to generate an individual)
keep.ids

Set to TRUE to keep the IDs from the pedigree-file instead of the default MoBPS ids
plot

Set to FALSE to not generate an overview of inbreeding and number of individuals over time
dataset

SNP dataset, use "random", "allhetero" "all0" when generating a dataset via nsnp,nindi
vcf

Path to a vcf-file used as input genotypes (correct haplotype phase is assumed!)
chr.nr

Vector containing the assosiated chromosome for each marker (default: all on the same)
bp

Vector containing the physical position (bp) for each marker (default: 1,2,3...)
snp.name

Vector containing the name of each marker (default ChrXSNPY - XY chosen accordingly)
hom0

Vector containing the first allelic variant in each marker (default: 0)
hom1

Vector containing the second allelic variant in each marker (default: 1)
bpcm.conversion

Convert physical position (bp) into a cM position (default: 0 - not done)
nsnp

number of markers to generate in a random dataset
freq

frequency of allele 1 when randomly generating a dataset
sex.s

Specify which newly added individuals are male (1) or female (2)
chromosome.length

Length of the newly added chromosome (default: 5)
length.before

Length before the first SNP of the dataset (default: 5)
length.behind

Length after the last SNP of the dataset (default: 5)
real.bv.add

Single Marker effects
real.bv.mult

Two Marker effects
real.bv.dice

Multi-marker effects
snps.equidistant

Use equidistant markers (computationally faster! ; default: TRUE)
change.order

If TRUE sort markers according to given marker positions
bv.total

Number of traits (If more than traits via real.bv.X use traits with no directly underlying QTL)
polygenic.variance

Genetic variance of traits with no underlying QTL
bve.mult.factor

Multiplicate trait value times this
bve.poly.factor

Potency trait value over this
base.bv

Average genetic value of a trait
add.chromosome.ends

Add chromosome ends as recombination points
new.phenotype.correlation

(OLD! - use new.residual.correlation) Correlation of the simulated enviromental variance
new.residual.correlation

Correlation of the simulated enviromental variance
new.breeding.correlation

Correlation of the simulated genetic variance (child share! heritage is not influenced!
add.architecture

Add genetic architecture (marker positions)
snp.position

Location of each marker on the genetic map
position.scaling

Manual scaling of snp.position
bit.storing

Set to TRUE if the MoBPS (not-miraculix! bit-storing is used)
nbits

Bits available in MoBPS-bit-storing
randomSeed

Set random seed of the process
miraculix

If TRUE use miraculix package for data storage, computations and dataset generation
miraculix.dataset

Set FALSE to deactive miraculix package for dataset generation
n.additive

Number of additive QTL
n.dominant

Number of dominante QTL
n.qualitative

Number of qualitative epistatic QTL
n.quantitative

Number of quantitative epistatic QTL
var.additive.l

Variance of additive QTL
var.dominant.l

Variance of dominante QTL
var.qualitative.l

Variance of qualitative epistatic QTL
var.quantitative.l

Variance of quantitative epistatic QTL
exclude.snps

Marker were no QTL are simulated on
replace.real.bv

If TRUE delete the simulated traits added before
shuffle.traits

Combine different traits into a joined trait
shuffle.cor

Target Correlation between shuffeled traits
skip.rest

Internal variable needed when adding multipe chromosomes jointly
enter.bv

Internal parameter
name.cohort

Name of the newly added cohort
template.chip

Import genetic map and chip from a species ("cattle", "chicken", "pig")
beta.shape1

First parameter of the beta distribution for simulating allele frequencies
beta.shape2

Second parameter of the beta distribution for simulating allele frequencies
time.point

Time point at which the new individuals are generated
creating.type

Technique to generate new individuals (usage in web-based application)
trait.name

Name of the trait generated
share.genotyped

Share of individuals genotyped in the founders
genotyped.s

Specify with newly added individuals are genotyped (1) or not (0)
map

map-file that contains up to 5 colums (Chromsome, SNP-id, M-position, Bp-position, allele freq - Everything not provides it set to NA). A map can be imported via MoBPSmaps::ensembl.map()
remove.invalid.qtl

Set to FALSE to deactive the automatic removal of QTLs on markers that do not exist
verbose

Set to FALSE to not display any prints
bv.standard

Set TRUE to standardize trait mean and variance via bv.standardization() - automatically set to TRUE when mean/var.target are used
mean.target

Target mean
var.target

Target variance
is.maternal

Vector coding if a trait is caused by a maternal effect (Default: all FALSE)
is.paternal

Vector coding if a trait is caused by a paternal effect (Default: all FALSE)
vcf.maxsnp

Maximum number of SNPs to include in the genotype file (default: Inf)
add.chromosome

If TRUE add an additional chromosome to the dataset

Value

Population-list

Examples

pedigree <- matrix(c(1,0,0,
2,0,0,
3,0,0,
4,1,2,
5,1,3,
6,1,3,
7,1,3,
8,4,6,
9,4,7), ncol=3, byrow=TRUE)
population <- pedigree.simulation(pedigree, nsnp=1000)

Internal function to perform imputing/phasing

Description

Internal function to perform imputing/phasing (path chosen for the web-based application)

Usage

pedmap.to.phasedbeaglevcf(
  ped_path = NULL,
  map_path = NULL,
  vcf_path = NULL,
  beagle_jar = "/home/nha/beagle.03Jul18.40b.jar",
  plink_dir = "/home/nha/Plink/plink",
  db_dir = "/home/nha/Plink/DB/",
  verbose = TRUE
)

Arguments

ped_path

Directory of the ped-file
map_path

Directory of the map-file
vcf_path

Directory of the vcf-file (this will override any ped/map-file input)
beagle_jar

Directory of BEAGLE
plink_dir

Directory of Plink
db_dir

Directory to save newly generated files (ped/map will be stored in the original folder)
verbose

Set to FALSE to not display any prints

Value

Phased vcf file in vcf_path

pig chip

Description

Genome for pig according to Rohrer et al.

Usage

pig_chip

Author(s)

Torsten Pook [email protected]

Source

Rohrer et al 1994

Plot Population

Description

Basic plot of the population list

Usage

## S3 method for class 'population'
plot(x, type = "bve", gen = NULL, database = NULL, cohorts = NULL, ...)

Arguments

x

Population-list
type

Default "bve" - bv.development, alt: "kinship" - kinship.development(), "pca" - get.pca()
gen

generations to consider
database

groups to consider
cohorts

cohorts to consider
...

remaining stuff

Value

Summary of the population list including number of individuals, genone length and trait overview

Examples

data(ex_pop)
plot(ex_pop)

Export estimated breeding values

Description

Function to export estimated breeding values

Usage

set.class(
  population,
  database = NULL,
  gen = NULL,
  cohorts = NULL,
  new.class = 0
)

Arguments

population

Population list
database

Groups of individuals to consider for the export
gen

Quick-insert for database (vector of all generations to export)
cohorts

Quick-insert for database (vector of names of cohorts to export)
new.class

Class to change to (either single character or vector for each individual when just a single group is selected)

Value

Population-List with newly entered class values

Examples

data(ex_pop)
population <- set.class(ex_pop, database=cbind(1,1), new.class = 2)

Set defaults

Description

Set default parameter values in breeding.diploid

Usage

set.default(
  population,
  parameter.name = NULL,
  parameter.value = NULL,
  parameter.remove = NULL,
  reset.all = FALSE
)

Arguments

population

Population list
parameter.name

Number of traits (If more than traits via real.bv.X use traits with no directly underlying QTL)
parameter.value

Genetic variance of traits with no underlying QTL
parameter.remove

Remove a specific previously generated parameter default
reset.all

Set to TRUE to remove all prior parameter values

Value

Population-list with one or more additional new traits

Examples

data(ex_pop)
population <- set.default(ex_pop, parameter.name="heritability", parameter.value=0.3)

sheep chip

Description

Genome for sheep according to Prieur et al.

Usage

sheep_chip

Author(s)

Torsten Pook [email protected]

Source

Prieur et al 2017

Apply sort and unique

Description

Efficient function to perform sort(unique(v))

Usage

sortd(v)

Arguments

v

Vector

Value

numerical sorted vector without duplicates

Examples

v <- c(1,1,4,5)
sortd(v)

Single Step GBLUP

Description

Function to perform single step GBLUP according to Legarra 2014

Usage

ssGBLUP(A11, A12, A22, G)

Arguments

A11

pedigree relationship matrix of non-genotyped individuals
A12

pedigree relationship matrix between non-genotyped and genotyped individuals
A22

pedigree relationship matrix of genotyped individuals
G

genomic relationship matrix of genotyped individuals

Value

Single step relationship matrix

Summary Population

Description

Summary of the population list

Usage

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

Arguments

object

Population-list
...

additional arguments affecting the summary produced

Value

Summary of the population list including number of individuals, genone length and trait overview

Examples

data(ex_pop)
summary(ex_pop)

Generation of a sublist

Description

Internal function to write a couple of list entries in a new list

Usage

vlist(list, skip = NULL, first = NULL, select = NULL)

Arguments

list

list you want to print details of
skip

Skip first that many list-elements
first

Only display first that many list-elements
select

Display only selected list-elements

Value

Selected elements of a list

Examples

data(ex_pop)
vlist(ex_pop$breeding[[1]], select=3:10)