Type:	Package
Title:	Breeding Program Simulations
Version:	1.6.1
Date:	2024-11-01
Description:	The successor to the 'AlphaSim' software for breeding program simulation [Faux et al. (2016) <doi:10.3835/plantgenome2016.02.0013>]. Used for stochastic simulations of breeding programs to the level of DNA sequence for every individual. Contained is a wide range of functions for modeling common tasks in a breeding program, such as selection and crossing. These functions allow for constructing simulations of highly complex plant and animal breeding programs via scripting in the R software environment. Such simulations can be used to evaluate overall breeding program performance and conduct research into breeding program design, such as implementation of genomic selection. Included is the 'Markovian Coalescent Simulator' ('MaCS') for fast simulation of biallelic sequences according to a population demographic history [Chen et al. (2009) <doi:10.1101/gr.083634.108>].
License:	MIT + file LICENSE
URL:	https://github.com/gaynorr/AlphaSimR, https://gaynorr.github.io/AlphaSimR/, https://www.edx.org/learn/animal-breeding/the-university-of-edinburgh-breeding-programme-modelling-with-alphasimr
Encoding:	UTF-8
Depends:	R (≥ 4.0.0), methods, R6
Imports:	Rcpp (≥ 0.12.7), Rdpack
RdMacros:	Rdpack
LinkingTo:	Rcpp, RcppArmadillo (≥ 0.7.500.0.0), BH
RoxygenNote:	7.3.2
Suggests:	knitr, rmarkdown, testthat
VignetteBuilder:	knitr
NeedsCompilation:	yes
Packaged:	2024-11-01 14:23:49 UTC; gmjwm
Author:	Chris Gaynor [aut, cre], Gregor Gorjanc [ctb], John Hickey [ctb], Daniel Money [ctb], David Wilson [ctb], Thiago Oliveira [ctb], Audrey Martin [ctb], Philip Greenspoon [ctb]
Maintainer:	Chris Gaynor <gaynor.robert@hotmail.com>
Repository:	CRAN
Date/Publication:	2024-11-01 15:10:06 UTC

AlphaSimR: Breeding Program Simulations

Description

The successor to the 'AlphaSim' software for breeding program simulation [Faux et al. (2016) <doi:10.3835/plantgenome2016.02.0013>]. Used for stochastic simulations of breeding programs to the level of DNA sequence for every individual. Contained is a wide range of functions for modeling common tasks in a breeding program, such as selection and crossing. These functions allow for constructing simulations of highly complex plant and animal breeding programs via scripting in the R software environment. Such simulations can be used to evaluate overall breeding program performance and conduct research into breeding program design, such as implementation of genomic selection. Included is the 'Markovian Coalescent Simulator' ('MaCS') for fast simulation of biallelic sequences according to a population demographic history [Chen et al. (2009) <doi:10.1101/gr.083634.108>].

Please see the introductory vignette for instructions for using this package. The vignette can be viewed using the following command: vignette("intro",package="AlphaSimR")

Author(s)

Maintainer: Chris Gaynor gaynor.robert@hotmail.com (ORCID)

Other contributors:

• Gregor Gorjanc (ORCID) [contributor]

• John Hickey (ORCID) [contributor]

• Daniel Money (ORCID) [contributor]

• David Wilson [contributor]

• Thiago Oliveira (ORCID) [contributor]

• Audrey Martin (ORCID) [contributor]

• Philip Greenspoon (ORCID) [contributor]

See Also

Useful links:

• https://github.com/gaynorr/AlphaSimR

• https://gaynorr.github.io/AlphaSimR/

• https://www.edx.org/learn/animal-breeding/the-university-of-edinburgh-breeding-programme-modelling-with-alphasimr

Create new population (internal)

Description

Creates a new Pop-class from an object of of the Pop superclass.

Usage

.newPop(
  rawPop,
  id = NULL,
  mother = NULL,
  father = NULL,
  iMother = NULL,
  iFather = NULL,
  isDH = NULL,
  femaleParentPop = NULL,
  maleParentPop = NULL,
  hist = NULL,
  simParam = NULL,
  ...
)

Arguments

Value

Returns an object of Pop-class

Hybrid population

Description

A lightweight version of Pop-class for hybrid lines. Memory is saved by not storing genotypic data.

Usage

## S4 method for signature 'HybridPop'
x[i]

## S4 method for signature 'HybridPop'
c(x, ...)

isHybridPop(x)

Arguments

Methods (by generic)

• [: Extract HybridPop using index or id

• c(HybridPop): Combine multiple HybridPops

Functions

• isHybridPop(): Test if object is of a HybridPop class

Slots

nInd

number of individuals

id

an individual's identifier

mother

the identifier of the individual's mother

father

the identifier of the individual's father

nTraits

number of traits

gv

matrix of genetic values. When using GxE traits, gv reflects gv when p=0.5. Dimensions are nInd by nTraits.

pheno

matrix of phenotypic values. Dimensions are nInd by nTraits.

gxe

list containing GxE slopes for GxE traits

Loci metadata

Description

used for both SNPs and QTLs

Slots

nLoci

total number of loci

lociPerChr

number of loci per chromosome

lociLoc

physical position of loci

name

optional name for LociMap object

Raw population with genetic map

Description

Extends RawPop-class to add a genetic map. This is the first object created in a simulation. It is used for creating initial populations and setting traits in the SimParam.

Usage

## S4 method for signature 'MapPop'
x[i]

## S4 method for signature 'MapPop'
c(x, ...)

isMapPop(x)

Arguments

Methods (by generic)

• [: Extract MapPop by index

• c(MapPop): Combine multiple MapPops

Functions

• isMapPop(): Test if object is of a MapPop class

Slots

genMap

list of chromosome genetic maps

centromere

vector of centromere positions

inbred

indicates whether the individuals are fully inbred

Multi-Population

Description

The mega-population represents a population of populations. It is designed to behave like a list of populations.

Usage

## S4 method for signature 'MultiPop'
x[i]

## S4 method for signature 'MultiPop'
x[[i]]

## S4 method for signature 'MultiPop'
c(x, ...)

## S4 method for signature 'MultiPop'
length(x)

isMultiPop(x)

Arguments

Methods (by generic)

• [: Extract MultiPop by index

• [[: Extract Pop by index

• c(MultiPop): Combine multiple MultiPops

• length(MultiPop): Number of pops in MultiPop

Functions

• isMultiPop(): Test if object is of a MultiPop class

Slots

pops

list of Pop-class and/or MultiPop-class

Raw population with genetic map and id

Description

Extends MapPop-class to add id, mother and father.

Usage

## S4 method for signature 'NamedMapPop'
x[i]

## S4 method for signature 'NamedMapPop'
c(x, ...)

isNamedMapPop(x)

Arguments

Methods (by generic)

• [: Extract NamedMapPop by index

• c(NamedMapPop): Combine multiple NamedMapPops

Functions

• isNamedMapPop(): Test if object is a NamedMapPop class

Slots

id

an individual's identifier

mother

the identifier of the individual's mother

father

the identifier of the individual's father

Population

Description

Extends RawPop-class to add sex, genetic values, phenotypes, and pedigrees.

Usage

## S4 method for signature 'Pop'
x[i]

## S4 method for signature 'Pop'
c(x, ...)

## S4 method for signature 'Pop'
show(object)

## S4 method for signature 'Pop'
length(x)

Arguments

Methods (by generic)

• [: Extract Pop by index or id

• c(Pop): Combine multiple Pops

• show(Pop): Show population summary

• length(Pop): Number of individuals in Pop (the same as nInd())

Slots

id

an individual's identifier

iid

an individual's internal identifier

mother

the identifier of the individual's mother

father

the identifier of the individual's father

sex

sex of individuals: "M" for males, "F" for females, and "H" for hermaphrodites

nTraits

number of traits

gv

matrix of genetic values. When using GxE traits, gv reflects gv when p=0.5. Dimensions are nInd by nTraits.

pheno

matrix of phenotypic values. Dimensions are nInd by nTraits.

ebv

matrix of estimated breeding values. Dimensions are nInd rows and a variable number of columns.

gxe

list containing GxE slopes for GxE traits

fixEff

a fixed effect relating to the phenotype. Used by genomic selection models but otherwise ignored.

misc

a list whose elements correspond to additional miscellaneous nodes with the items for individuals in the population (see example in newPop) - we support vectors and matrices or objects that have a generic length and subset method. This list is normally empty and exists solely as an open slot available for uses to store extra information about individuals.

miscPop

a list of any length containing optional meta data for the population (see example in newPop). This list is empty unless information is supplied by the user. Note that the list is emptied every time the population is subsetted or combined because the meta data for old population might not be valid anymore.

See Also

newPop, newEmptyPop, resetPop

RR-BLUP Model

Description

Fits an RR-BLUP model for genomic predictions.

Usage

RRBLUP(
  pop,
  traits = 1,
  use = "pheno",
  snpChip = 1,
  useQtl = FALSE,
  maxIter = 1000L,
  simParam = NULL,
  ...
)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=20)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)
SP$setVarE(h2=0.5)
SP$addSnpChip(10)

#Create population
pop = newPop(founderPop, simParam=SP)

#Run GS model and set EBV
ans = RRBLUP(pop, simParam=SP)
pop = setEBV(pop, ans, simParam=SP)

#Evaluate accuracy
cor(gv(pop), ebv(pop))

RR-BLUP Model 2

Description

Fits an RR-BLUP model for genomic predictions. This implementation is meant for situations where RRBLUP is too slow. Note that RRBLUP2 is only faster in certain situations, see details below. Most users should use RRBLUP.

Usage

RRBLUP2(
  pop,
  traits = 1,
  use = "pheno",
  snpChip = 1,
  useQtl = FALSE,
  maxIter = 10,
  Vu = NULL,
  Ve = NULL,
  useEM = TRUE,
  tol = 1e-06,
  simParam = NULL,
  ...
)

Arguments

Details

The RRBLUP2 function works best when the number of markers is not too large. This is because it solves the RR-BLUP problem by setting up and solving Henderson's mixed model equations. Solving these equations involves a square matrix with dimensions equal to the number of fixed effects plus the number of random effects (markers). Whereas the RRBLUP function solves the RR-BLUP problem using the EMMA approach. This approach involves a square matrix with dimensions equal to the number of phenotypic records. This means that the RRBLUP2 function uses less memory than RRBLUP when the number of markers is approximately equal to or smaller than the number of phenotypic records.

The RRBLUP2 function is not recommend for cases where the variance components are unknown. This is uses the EM algorithm to solve for unknown variance components, which is generally considerably slower than the EMMA approach of RRBLUP. The number of iterations for the EM algorithm is set by maxIter. The default value is typically too small for convergence. When the algorithm fails to converge a warning is displayed, but results are given for the last iteration. These results may be "good enough". However we make no claim to this effect, because we can not generalize to all possible use cases.

The RRBLUP2 function can quickly solve the mixed model equations without estimating variance components. The variance components are set by defining Vu and Ve. Estimation of components is suppressed by setting useEM to false. This may be useful if the model is being retrained multiple times during the simulation. You could run RRBLUP function the first time the model is trained, and then use the variance components from this output for all future runs with the RRBLUP2 functions. Again, we can make no claim to the general robustness of this approach.

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=20)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)
SP$setVarE(h2=0.5)
SP$addSnpChip(10)

#Create population
pop = newPop(founderPop, simParam=SP)

#Run GS model and set EBV
ans = RRBLUP2(pop, simParam=SP)
pop = setEBV(pop, ans, simParam=SP)

#Evaluate accuracy
cor(gv(pop), ebv(pop))

RRBLUP Memory Usage

Description

Estimates the amount of RAM needed to run the RRBLUP and its related functions for a given training population size. Note that this function may underestimate total usage.

Usage

RRBLUPMemUse(nInd, nMarker, model = "REG")

Arguments

Value

Returns an estimate for the required gigabytes of RAM

Examples

RRBLUPMemUse(nInd=1000, nMarker=5000)

RR-BLUP Model with Dominance

Description

Fits an RR-BLUP model for genomic predictions that includes dominance effects.

Usage

RRBLUP_D(
  pop,
  traits = 1,
  use = "pheno",
  snpChip = 1,
  useQtl = FALSE,
  maxIter = 40L,
  simParam = NULL,
  ...
)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=20)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitAD(10, meanDD=0.5)
SP$setVarE(h2=0.5)
SP$addSnpChip(10)

#Create population
pop = newPop(founderPop, simParam=SP)

#Run GS model and set EBV
ans = RRBLUP_D(pop, simParam=SP)
pop = setEBV(pop, ans, simParam=SP)

#Evaluate accuracy
cor(gv(pop), ebv(pop))

RR-BLUP with Dominance Model 2

Description

Fits an RR-BLUP model for genomic predictions that includes dominance effects. This implementation is meant for situations where RRBLUP_D is too slow. Note that RRBLUP_D2 is only faster in certain situations. Most users should use RRBLUP_D.

Usage

RRBLUP_D2(
  pop,
  traits = 1,
  use = "pheno",
  snpChip = 1,
  useQtl = FALSE,
  maxIter = 10,
  Va = NULL,
  Vd = NULL,
  Ve = NULL,
  useEM = TRUE,
  tol = 1e-06,
  simParam = NULL,
  ...
)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=20)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitAD(10, meanDD=0.5)
SP$setVarE(h2=0.5)
SP$addSnpChip(10)

#Create population
pop = newPop(founderPop, simParam=SP)

#Run GS model and set EBV
ans = RRBLUP_D2(pop, simParam=SP)
pop = setEBV(pop, ans, simParam=SP)

#Evaluate accuracy
cor(gv(pop), ebv(pop))

RR-BLUP GCA Model

Description

Fits an RR-BLUP model that estimates seperate marker effects for females and males. Useful for predicting GCA of parents in single cross hybrids. Can also predict performance of specific single cross hybrids.

Usage

RRBLUP_GCA(
  pop,
  traits = 1,
  use = "pheno",
  snpChip = 1,
  useQtl = FALSE,
  maxIter = 40L,
  simParam = NULL,
  ...
)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=20)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)
SP$setVarE(h2=0.5)
SP$addSnpChip(10)

#Create population
pop = newPop(founderPop, simParam=SP)

#Run GS model and set EBV
ans = RRBLUP_GCA(pop, simParam=SP)
pop = setEBV(pop, ans, simParam=SP)

#Evaluate accuracy
cor(gv(pop), ebv(pop))

RR-BLUP GCA Model 2

Description

Fits an RR-BLUP model that estimates seperate marker effects for females and males. This implementation is meant for situations where RRBLUP_GCA is too slow. Note that RRBLUP_GCA2 is only faster in certain situations. Most users should use RRBLUP_GCA.

Usage

RRBLUP_GCA2(
  pop,
  traits = 1,
  use = "pheno",
  snpChip = 1,
  useQtl = FALSE,
  maxIter = 10,
  VuF = NULL,
  VuM = NULL,
  Ve = NULL,
  useEM = TRUE,
  tol = 1e-06,
  simParam = NULL,
  ...
)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=20)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)
SP$setVarE(h2=0.5)
SP$addSnpChip(10)

#Create population
pop = newPop(founderPop, simParam=SP)

#Run GS model and set EBV
ans = RRBLUP_GCA2(pop, simParam=SP)
pop = setEBV(pop, ans, simParam=SP)

#Evaluate accuracy
cor(gv(pop), ebv(pop))

RR-BLUP SCA Model

Description

An extention of RRBLUP_GCA that adds dominance effects. Note that we have not seen any consistent benefit of this model over RRBLUP_GCA.

Usage

RRBLUP_SCA(
  pop,
  traits = 1,
  use = "pheno",
  snpChip = 1,
  useQtl = FALSE,
  maxIter = 40L,
  simParam = NULL,
  ...
)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=2, nChr=1, segSites=20)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)
SP$setVarE(h2=0.5)
SP$addSnpChip(10)

#Create population
pop = newPop(founderPop, simParam=SP)

#Run GS model and set EBV
ans = RRBLUP_SCA(pop, simParam=SP)
pop = setEBV(pop, ans, simParam=SP)

#Evaluate accuracy
cor(gv(pop), ebv(pop))

RR-BLUP SCA Model 2

Description

Fits an RR-BLUP model that estimates seperate additive effects for females and males and a dominance effect. This implementation is meant for situations where RRBLUP_SCA is too slow. Note that RRBLUP_SCA2 is only faster in certain situations. Most users should use RRBLUP_SCA.

Usage

RRBLUP_SCA2(
  pop,
  traits = 1,
  use = "pheno",
  snpChip = 1,
  useQtl = FALSE,
  maxIter = 10,
  VuF = NULL,
  VuM = NULL,
  VuD = NULL,
  Ve = NULL,
  useEM = TRUE,
  tol = 1e-06,
  simParam = NULL,
  ...
)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=20)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)
SP$setVarE(h2=0.5)
SP$addSnpChip(10)

#Create population
pop = newPop(founderPop, simParam=SP)

#Run GS model and set EBV
ans = RRBLUP_SCA2(pop, simParam=SP)
pop = setEBV(pop, ans, simParam=SP)

#Evaluate accuracy
cor(gv(pop), ebv(pop))

RR-BLUP Solution

Description

Contains output from AlphaSimR's genomic selection functions.

Slots

gv

Trait(s) for estimating genetic values

bv

Trait(s) for estimating breeding values

female

Trait(s) for estimating GCA in the female pool

male

Trait(s) for estimating GCA in the male pool

Vu

Estimated marker variance(s)

Ve

Estimated error variance

Raw Population

Description

The raw population class contains only genotype data.

Usage

## S4 method for signature 'RawPop'
x[i]

## S4 method for signature 'RawPop'
c(x, ...)

## S4 method for signature 'RawPop'
show(object)

isRawPop(x)

Arguments

Methods (by generic)

• [: Extract RawPop by index

• c(RawPop): Combine multiple RawPops

• show(RawPop): Show population summary

Functions

• isRawPop(): Test if object is of a RawPop class

Slots

nInd

number of individuals

nChr

number of chromosomes

ploidy

level of ploidy

nLoci

number of loci per chromosome

geno

list of nChr length containing chromosome genotypes. Each element is a three dimensional array of raw values. The array dimensions are nLoci by ploidy by nInd.

Simulation parameters

Description

Container for global simulation parameters. Saving this object as SP will allow it to be accessed by function defaults.

Public fields

Active bindings

Methods

Public methods

• SimParam$new()

• SimParam$setTrackPed()

• SimParam$setTrackRec()

• SimParam$resetPed()

• SimParam$restrSegSites()

• SimParam$setSexes()

• SimParam$setFounderHap()

• SimParam$addSnpChip()

• SimParam$addSnpChipByName()

• SimParam$addStructuredSnpChip()

• SimParam$addTraitA()

• SimParam$addTraitAD()

• SimParam$altAddTraitAD()

• SimParam$addTraitAG()

• SimParam$addTraitADG()

• SimParam$addTraitAE()

• SimParam$addTraitADE()

• SimParam$addTraitAEG()

• SimParam$addTraitADEG()

• SimParam$manAddTrait()

• SimParam$importTrait()

• SimParam$switchTrait()

• SimParam$removeTrait()

• SimParam$setVarE()

• SimParam$setCorE()

• SimParam$rescaleTraits()

• SimParam$setRecombRatio()

• SimParam$switchGenMap()

• SimParam$switchFemaleMap()

• SimParam$switchMaleMap()

• SimParam$addToRec()

• SimParam$ibdHaplo()

• SimParam$updateLastId()

• SimParam$addToPed()

• SimParam$clone()

Method new()

Starts the process of building a new simulation by creating a new SimParam object and assigning a founder population to the class. It is recommended that you save the object with the name "SP", because subsequent functions will check your global environment for an object of this name if their simParam arguments are NULL. This allows you to call these functions without explicitly supplying a simParam argument with every call.

Usage

SimParam$new(founderPop)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

Method setTrackPed()

Sets pedigree tracking for the simulation. By default pedigree tracking is turned off. When turned on, the pedigree of all individuals created will be tracked, except those created by hybridCross. Turning off pedigree tracking will turn off recombination tracking if it is turned on.

Usage

SimParam$setTrackPed(isTrackPed, force = FALSE)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
\dontshow{SP$nThreads = 1L}
SP$setTrackPed(TRUE)

Method setTrackRec()

Sets recombination tracking for the simulation. By default recombination tracking is turned off. When turned on recombination tracking will also turn on pedigree tracking. Recombination tracking keeps records of all individuals created, except those created by hybridCross, because their pedigree is not tracked.

Usage

SimParam$setTrackRec(isTrackRec, force = FALSE)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
\dontshow{SP$nThreads = 1L}
SP$setTrackRec(TRUE)

Method resetPed()

Resets the internal lastId, the pedigree and recombination tracking (if in use) to the supplied lastId. Be careful using this function because it may introduce a bug if you use individuals from the deleted portion of the pedigree.

Usage

SimParam$resetPed(lastId = 0L)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
\dontshow{SP$nThreads = 1L}

#Create population
pop = newPop(founderPop, simParam=SP)
pop@id # 1:10

#Create another population after reseting pedigree
SP$resetPed()
pop2 = newPop(founderPop, simParam=SP)
pop2@id # 1:10

Method restrSegSites()

Sets restrictions on which segregating sites can serve as a SNP and/or QTL.

Usage

SimParam$restrSegSites(
  minQtlPerChr = NULL,
  minSnpPerChr = NULL,
  excludeQtl = NULL,
  excludeSnp = NULL,
  overlap = FALSE,
  minSnpFreq = NULL
)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
\dontshow{SP$nThreads = 1L}
SP$restrSegSites(minQtlPerChr=5, minSnpPerChr=5)

Method setSexes()

Changes how sexes are determined in the simulation. The default sexes is "no", indicating all individuals are hermaphrodites. To add sexes to the simulation, run this function with "yes_sys" or "yes_rand". The value "yes_sys" will systematically assign sexes to newly created individuals as first male and then female. Populations with an odd number of individuals will have one more male than female. The value "yes_rand" will randomly assign a sex to each individual.

Usage

SimParam$setSexes(sexes, force = FALSE)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
\dontshow{SP$nThreads = 1L}
SP$setSexes("yes_sys")

Method setFounderHap()

Allows for the manual setting of founder haplotypes. This functionality is not fully documented, because it is still experimental.

Usage

SimParam$setFounderHap(hapMap)

Arguments

Method addSnpChip()

Randomly assigns eligible SNPs to a SNP chip

Usage

SimParam$addSnpChip(nSnpPerChr, minSnpFreq = NULL, refPop = NULL, name = NULL)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
\dontshow{SP$nThreads = 1L}
SP$addSnpChip(10)

Method addSnpChipByName()

Assigns SNPs to a SNP chip by supplying marker names. This function does check against excluded SNPs and will not add the SNPs to the list of excluded QTL for the purpose of avoiding overlap between SNPs and QTL. Excluding these SNPs from being used as QTL can be accomplished using the excludeQtl argument in SimParam's restrSegSites function.

Usage

SimParam$addSnpChipByName(markers, name = NULL)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addSnpChipByName(c("1_1","1_3"))

Method addStructuredSnpChip()

Randomly selects the number of snps in structure and then assigns them to chips based on structure

Usage

SimParam$addStructuredSnpChip(nSnpPerChr, structure, force = FALSE)

Arguments

Method addTraitA()

Randomly assigns eligible QTLs for one or more additive traits. If simulating more than one trait, all traits will be pleiotropic with correlated additive effects.

Usage

SimParam$addTraitA(
  nQtlPerChr,
  mean = 0,
  var = 1,
  corA = NULL,
  gamma = FALSE,
  shape = 1,
  force = FALSE,
  name = NULL
)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
\dontshow{SP$nThreads = 1L}
SP$addTraitA(10)

Method addTraitAD()

Randomly assigns eligible QTLs for one or more traits with dominance. If simulating more than one trait, all traits will be pleiotropic with correlated effects.

Usage

SimParam$addTraitAD(
  nQtlPerChr,
  mean = 0,
  var = 1,
  meanDD = 0,
  varDD = 0,
  corA = NULL,
  corDD = NULL,
  useVarA = TRUE,
  gamma = FALSE,
  shape = 1,
  force = FALSE,
  name = NULL
)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
\dontshow{SP$nThreads = 1L}
SP$addTraitAD(10, meanDD=0.5)

Method altAddTraitAD()

An alternative method for adding a trait with additive and dominance effects to an AlphaSimR simulation. The function attempts to create a trait matching user defined values for number of QTL, inbreeding depression, additive genetic variance and dominance genetic variance.

Usage

SimParam$altAddTraitAD(
  nQtlPerChr,
  mean = 0,
  varA = 1,
  varD = 0,
  inbrDepr = 0,
  limMeanDD = c(0, 1.5),
  limVarDD = c(0, 0.5),
  silent = FALSE,
  force = FALSE,
  name = NULL
)

Arguments

Details

This function will always add a trait to 'SimParam', unless an error occurs with picking QTLs. The resulting trait will always have the desired mean and additive genetic variance. However, it may not have the desired values for inbreeding depression and dominance variance. Thus, it is strongly recommended to check the output printed to the console to determine how close the trait's parameters came to these desired values.

The mean and additive genetic variance will always be achieved exactly. The function attempts to achieve the desired dominance variance and inbreeding depression while staying within the user supplied constraints for the acceptable range of dominance degree mean and variance. If the desired values are not being achieved, the acceptable range need to be increased and/or the number of QTL may need to be increased. There are not limits to setting the range for dominance degree mean and variance, but care should be taken to with regards to the biological feasibility of the limits that are supplied. The default limits were somewhat arbitrarily set, so I make not claim to how reasonable these limits are for routine use.

Inbreeding depression in this function is defined as the difference in mean genetic value between a population with the same allele frequency as the reference population (population used to initialize SimParam) in Hardy-Weinberg equilibrium compared to a population with the same allele frequency that is fully inbred. This is equivalent to the amount the mean of a population increases when going from an inbreeding coefficient of 1 (fully inbred) to a population with an inbreeding coefficient of 0 (Hardy-Weinberg equilibrium). Note that the sign of the value should (usually) be positive. This corresponds to a detrimental effect of inbreeding when higher values of the trait are considered biologically beneficial.

Summary information on this trait is printed to the console when silent=FALSE. The summary information reports the inbreeding depression and dominance variance for the population as well as the dominance degree mean and variance applied to the trait.

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
\dontshow{SP$nThreads = 1L}
SP$altAddTraitAD(nQtlPerChr=10, mean=0, varA=1, varD=0.05, inbrDepr=0.2)

Method addTraitAG()

Randomly assigns eligible QTLs for one or more additive GxE traits. If simulating more than one trait, all traits will be pleiotropic with correlated effects.

Usage

SimParam$addTraitAG(
  nQtlPerChr,
  mean = 0,
  var = 1,
  varGxE = 1e-06,
  varEnv = 0,
  corA = NULL,
  corGxE = NULL,
  gamma = FALSE,
  shape = 1,
  force = FALSE,
  name = NULL
)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
\dontshow{SP$nThreads = 1L}
SP$addTraitAG(10, varGxE=2)

Method addTraitADG()

Randomly assigns eligible QTLs for a trait with dominance and GxE.

Usage

SimParam$addTraitADG(
  nQtlPerChr,
  mean = 0,
  var = 1,
  varEnv = 0,
  varGxE = 1e-06,
  meanDD = 0,
  varDD = 0,
  corA = NULL,
  corDD = NULL,
  corGxE = NULL,
  useVarA = TRUE,
  gamma = FALSE,
  shape = 1,
  force = FALSE,
  name = NULL
)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
\dontshow{SP$nThreads = 1L}
SP$addTraitADG(10, meanDD=0.5, varGxE=2)

Method addTraitAE()

Randomly assigns eligible QTLs for one or more additive and epistasis traits. If simulating more than one trait, all traits will be pleiotropic with correlated additive effects.

Usage

SimParam$addTraitAE(
  nQtlPerChr,
  mean = 0,
  var = 1,
  relAA = 0,
  corA = NULL,
  corAA = NULL,
  useVarA = TRUE,
  gamma = FALSE,
  shape = 1,
  force = FALSE,
  name = NULL
)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
\dontshow{SP$nThreads = 1L}
SP$addTraitAE(10, relAA=0.1)

Method addTraitADE()

Randomly assigns eligible QTLs for one or more traits with dominance and epistasis. If simulating more than one trait, all traits will be pleiotropic with correlated effects.

Usage

SimParam$addTraitADE(
  nQtlPerChr,
  mean = 0,
  var = 1,
  meanDD = 0,
  varDD = 0,
  relAA = 0,
  corA = NULL,
  corDD = NULL,
  corAA = NULL,
  useVarA = TRUE,
  gamma = FALSE,
  shape = 1,
  force = FALSE,
  name = NULL
)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
\dontshow{SP$nThreads = 1L}
SP$addTraitADE(10)

Method addTraitAEG()

Randomly assigns eligible QTLs for one or more additive and epistasis GxE traits. If simulating more than one trait, all traits will be pleiotropic with correlated effects.

Usage

SimParam$addTraitAEG(
  nQtlPerChr,
  mean = 0,
  var = 1,
  relAA = 0,
  varGxE = 1e-06,
  varEnv = 0,
  corA = NULL,
  corAA = NULL,
  corGxE = NULL,
  useVarA = TRUE,
  gamma = FALSE,
  shape = 1,
  force = FALSE,
  name = NULL
)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
\dontshow{SP$nThreads = 1L}
SP$addTraitAEG(10, varGxE=2)

Method addTraitADEG()

Randomly assigns eligible QTLs for a trait with dominance, epistasis and GxE.

Usage

SimParam$addTraitADEG(
  nQtlPerChr,
  mean = 0,
  var = 1,
  varEnv = 0,
  varGxE = 1e-06,
  meanDD = 0,
  varDD = 0,
  relAA = 0,
  corA = NULL,
  corDD = NULL,
  corAA = NULL,
  corGxE = NULL,
  useVarA = TRUE,
  gamma = FALSE,
  shape = 1,
  force = FALSE,
  name = NULL
)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
\dontshow{SP$nThreads = 1L}
SP$addTraitADEG(10, meanDD=0.5, varGxE=2)

Method manAddTrait()

Manually add a new trait to the simulation. Trait must be formatted as a LociMap-class. If the trait is not already formatted, consider using importTrait.

Usage

SimParam$manAddTrait(lociMap, varE = NA_real_, force = FALSE)

Arguments

Method importTrait()

Manually add a new trait(s) to the simulation. Unlike the manAddTrait function, this function does not require formatting the trait as a LociMap-class. The formatting is performed automatically for the user, with more user friendly data.frames or matrices taken as inputs. This function only works for A and AD trait types.

Usage

SimParam$importTrait(
  markerNames,
  addEff,
  domEff = NULL,
  intercept = NULL,
  name = NULL,
  varE = NULL,
  force = FALSE
)

Arguments

Method switchTrait()

Switch a trait in the simulation.

Usage

SimParam$switchTrait(traitPos, lociMap, varE = NA_real_, force = FALSE)

Arguments

Method removeTrait()

Remove a trait from the simulation

Usage

SimParam$removeTrait(traits, force = FALSE)

Arguments

Method setVarE()

Defines a default values for error variances used in setPheno. These defaults will be used to automatically generate phenotypes when new populations are created. See the details section of setPheno for more information about each arguments and how they should be used.

Usage

SimParam$setVarE(h2 = NULL, H2 = NULL, varE = NULL, corE = NULL)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
\dontshow{SP$nThreads = 1L}
SP$addTraitA(10)
SP$setVarE(h2=0.5)

Method setCorE()

Defines a correlation structure for default error variances. You must call setVarE first to define the default error variances.

Usage

SimParam$setCorE(corE)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
\dontshow{SP$nThreads = 1L}
SP$addTraitA(10, mean=c(0,0), var=c(1,1), corA=diag(2))
SP$setVarE(varE=c(1,1))
E = 0.5*diag(2)+0.5 #Positively correlated error
SP$setCorE(E)

Method rescaleTraits()

Linearly scales all traits to achieve desired values of means and variances in the founder population.

Usage

SimParam$rescaleTraits(
  mean = 0,
  var = 1,
  varEnv = 0,
  varGxE = 1e-06,
  useVarA = TRUE
)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitA(10)

#Create population
pop = newPop(founderPop, simParam=SP)
meanG(pop)

#Change mean to 1
SP$rescaleTraits(mean=1)
\dontshow{SP$nThreads = 1L}
#Run resetPop for change to take effect
pop = resetPop(pop, simParam=SP)
meanG(pop)

Method setRecombRatio()

Set the relative recombination rates between males and females. This allows for sex-specific recombination rates, under the assumption of equivalent recombination landscapes.

Usage

SimParam$setRecombRatio(femaleRatio)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
\dontshow{SP$nThreads = 1L}
SP$setRecombRatio(2) #Twice as much recombination in females

Method switchGenMap()

Replaces existing genetic map.

Usage

SimParam$switchGenMap(genMap, centromere = NULL)

Arguments

Method switchFemaleMap()

Replaces existing female genetic map.

Usage

SimParam$switchFemaleMap(genMap, centromere = NULL)

Arguments

Method switchMaleMap()

Replaces existing male genetic map.

Usage

SimParam$switchMaleMap(genMap, centromere = NULL)

Arguments

Method addToRec()

For internal use only.

Usage

SimParam$addToRec(lastId, id, mother, father, isDH, hist, ploidy)

Arguments

Method ibdHaplo()

For internal use only.

Usage

SimParam$ibdHaplo(iid)

Arguments

Method updateLastId()

For internal use only.

Usage

SimParam$updateLastId(lastId)

Arguments

Method addToPed()

For internal use only.

Usage

SimParam$addToPed(lastId, id, mother, father, isDH)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

SimParam$clone(deep = FALSE)

Arguments

Note

By default the founder population is the population used to initalize the SimParam object. This population can be changed by replacing the population in the founderPop slot. You must run resetPop on any existing populations to obtain the new trait values.

Examples

## ------------------------------------------------
## Method `SimParam$new`
## ------------------------------------------------

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

## ------------------------------------------------
## Method `SimParam$setTrackPed`
## ------------------------------------------------

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$setTrackPed(TRUE)

## ------------------------------------------------
## Method `SimParam$setTrackRec`
## ------------------------------------------------

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$setTrackRec(TRUE)

## ------------------------------------------------
## Method `SimParam$resetPed`
## ------------------------------------------------

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)


#Create population
pop = newPop(founderPop, simParam=SP)
pop@id # 1:10

#Create another population after reseting pedigree
SP$resetPed()
pop2 = newPop(founderPop, simParam=SP)
pop2@id # 1:10

## ------------------------------------------------
## Method `SimParam$restrSegSites`
## ------------------------------------------------

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$restrSegSites(minQtlPerChr=5, minSnpPerChr=5)

## ------------------------------------------------
## Method `SimParam$setSexes`
## ------------------------------------------------

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$setSexes("yes_sys")

## ------------------------------------------------
## Method `SimParam$addSnpChip`
## ------------------------------------------------

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addSnpChip(10)

## ------------------------------------------------
## Method `SimParam$addSnpChipByName`
## ------------------------------------------------

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addSnpChipByName(c("1_1","1_3"))

## ------------------------------------------------
## Method `SimParam$addTraitA`
## ------------------------------------------------

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)

## ------------------------------------------------
## Method `SimParam$addTraitAD`
## ------------------------------------------------

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitAD(10, meanDD=0.5)

## ------------------------------------------------
## Method `SimParam$altAddTraitAD`
## ------------------------------------------------

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$altAddTraitAD(nQtlPerChr=10, mean=0, varA=1, varD=0.05, inbrDepr=0.2)

## ------------------------------------------------
## Method `SimParam$addTraitAG`
## ------------------------------------------------

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitAG(10, varGxE=2)

## ------------------------------------------------
## Method `SimParam$addTraitADG`
## ------------------------------------------------

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitADG(10, meanDD=0.5, varGxE=2)

## ------------------------------------------------
## Method `SimParam$addTraitAE`
## ------------------------------------------------

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitAE(10, relAA=0.1)

## ------------------------------------------------
## Method `SimParam$addTraitADE`
## ------------------------------------------------

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitADE(10)

## ------------------------------------------------
## Method `SimParam$addTraitAEG`
## ------------------------------------------------

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitAEG(10, varGxE=2)

## ------------------------------------------------
## Method `SimParam$addTraitADEG`
## ------------------------------------------------

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitADEG(10, meanDD=0.5, varGxE=2)

## ------------------------------------------------
## Method `SimParam$setVarE`
## ------------------------------------------------

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)
SP$setVarE(h2=0.5)

## ------------------------------------------------
## Method `SimParam$setCorE`
## ------------------------------------------------

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10, mean=c(0,0), var=c(1,1), corA=diag(2))
SP$setVarE(varE=c(1,1))
E = 0.5*diag(2)+0.5 #Positively correlated error
SP$setCorE(E)

## ------------------------------------------------
## Method `SimParam$rescaleTraits`
## ------------------------------------------------

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitA(10)

#Create population
pop = newPop(founderPop, simParam=SP)
meanG(pop)

#Change mean to 1
SP$rescaleTraits(mean=1)

#Run resetPop for change to take effect
pop = resetPop(pop, simParam=SP)
meanG(pop)

## ------------------------------------------------
## Method `SimParam$setRecombRatio`
## ------------------------------------------------

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$setRecombRatio(2) #Twice as much recombination in females

Additive trait

Description

Extends LociMap-class to model additive traits

Slots

addEff

additive effects

intercept

adjustment factor for gv

Sex specific additive trait

Description

Extends TraitA-class to model seperate additive effects for parent of origin. Used exclusively for genomic selection.

Slots

addEffMale

additive effects

Sex specific additive and dominance trait

Description

Extends TraitA2-class to add dominance

Slots

domEff

dominance effects

Additive and dominance trait

Description

Extends TraitA-class to add dominance

Slots

domEff

dominance effects

Additive, dominance, and epistatic trait

Description

Extends TraitAD-class to add epistasis

Slots

epiEff

epistatic effects

Additive, dominance, epistasis, and GxE trait

Description

Extends TraitADE-class to add GxE effects

Slots

gxeEff

GxE effects

gxeInt

GxE intercept

envVar

Environmental variance

Additive, dominance and GxE trait

Description

Extends TraitAD-class to add GxE effects

Slots

gxeEff

GxE effects

gxeInt

GxE intercept

envVar

Environmental variance

Additive and epistatic trait

Description

Extends TraitA-class to add epistasis

Slots

epiEff

epistatic effects

Additive, epistasis and GxE trait

Description

Extends TraitAE-class to add GxE effects

Slots

gxeEff

GxE effects

gxeInt

GxE intercept

envVar

Environmental variance

Additive and GxE trait

Description

Extends TraitA-class to add GxE effects

Slots

gxeEff

GxE effects

gxeInt

GxE intercept

envVar

Environmental variance

Additive-by-additive epistatic deviations

Description

Returns additive-by-additive epistatic deviations for all traits

Usage

aa(pop, simParam = NULL)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitAD(10, meanDD=0.5)
SP$setVarE(h2=0.5)


#Create population
pop = newPop(founderPop, simParam=SP)
aa(pop, simParam=SP)

Add segregating site to MapPop

Description

This function allows for adding a new segregating site with user supplied genotypes to a MapPop. The position of the site is set using a genetic map position.

Usage

addSegSite(mapPop, siteName, chr, mapPos, haplo)

Arguments

Value

an object of MapPop-class

Examples

# Creates a populations of 10 outbred individuals
# Their genome consists of 1 chromosome and 2 segregating sites
founderPop = quickHaplo(nInd=10,nChr=1,segSites=2)

# Add a locus a the 0.5 Morgan map position
haplo = matrix(sample(x=0:1, size=20, replace=TRUE), ncol=1)

founderPop2 = addSegSite(founderPop, siteName="x", chr=1, mapPos=0.5, haplo=haplo)

pullSegSiteHaplo(founderPop2)

Lose individuals at random

Description

Samples individuals at random to remove from the population. The user supplies a probability for the individuals to be removed from the population.

Usage

attrition(pop, p)

Arguments

Value

an object of Pop-class

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=100, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)


#Create population
pop = newPop(founderPop, simParam=SP)

#Lose an expected 5% of individuals
pop = attrition(pop, p=0.05)

Breeding value

Description

Returns breeding values for all traits

Usage

bv(pop, simParam = NULL)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitAD(10, meanDD=0.5)
SP$setVarE(h2=0.5)


#Create population
pop = newPop(founderPop, simParam=SP)
bv(pop, simParam=SP)

Combine MapPop chromosomes

Description

Merges the chromosomes of multiple MapPop-class or NamedMapPop-class objects. Each MapPop must have the same number of chromosomes

Usage

cChr(...)

Arguments

Value

Returns an object of MapPop-class

Examples

pop1 = quickHaplo(nInd=10, nChr=1, segSites=10)
pop2 = quickHaplo(nInd=10, nChr=1, segSites=10)

combinedPop = cChr(pop1, pop2)

Calculate GCA

Description

Calculate general combining ability of test crosses. Intended for output from hybridCross using the "testcross" option, but will work for any population.

Usage

calcGCA(pop, use = "pheno")

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10, inbred=TRUE)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)

#Create population
pop = newPop(founderPop, simParam=SP)

#Make crosses for full diallele
pop2 = hybridCross(pop, pop, simParam=SP)
GCA = calcGCA(pop2, use="gv")

Dominance deviations

Description

Returns dominance deviations for all traits

Usage

dd(pop, simParam = NULL)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitAD(10, meanDD=0.5)
SP$setVarE(h2=0.5)


#Create population
pop = newPop(founderPop, simParam=SP)
dd(pop, simParam=SP)

Double the ploidy of individuals

Description

Creates new individuals with twice the ploidy. This function was created to model the formation of tetraploid potatoes from diploid potatoes. This function will work on any population.

Usage

doubleGenome(pop, keepParents = TRUE, simParam = NULL)

Arguments

Value

Returns an object of Pop-class

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=2, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)


#Create population
pop = newPop(founderPop, simParam=SP)

#Create individuals with doubled ploidy
pop2 = doubleGenome(pop, simParam=SP)

Estimated breeding value

Description

A wrapper for accessing the ebv slot

Usage

ebv(pop)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitAD(10, meanDD=0.5)
SP$setVarE(h2=0.5)

#Create population
pop = newPop(founderPop, simParam=SP)
pop@ebv = matrix(rnorm(pop@nInd), nrow=pop@nInd, ncol=1)
ebv(pop)

Edit genome

Description

Edits selected loci of selected individuals to a homozygous state for either the 1 or 0 allele. The gv slot is recalculated to reflect the any changes due to editing, but other slots remain the same.

Usage

editGenome(pop, ind, chr, segSites, allele, simParam = NULL)

Arguments

Value

Returns an object of Pop-class

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=2, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)

#Create population
pop = newPop(founderPop, simParam=SP)

#Change individual 1 to homozygous for the 1 allele
#at locus 1, chromosome 1
pop2 = editGenome(pop, ind=1, chr=1, segSites=1,
                  allele=1, simParam=SP)

Edit genome - the top QTL

Description

Edits the top QTL (with the largest additive effect) to a homozygous state for the allele increasing. Only nonfixed QTL are edited The gv slot is recalculated to reflect the any changes due to editing, but other slots remain the same.

Usage

editGenomeTopQtl(pop, ind, nQtl, trait = 1, increase = TRUE, simParam = NULL)

Arguments

Value

Returns an object of Pop-class

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=2, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)

#Create population
pop = newPop(founderPop, simParam=SP)

#Change up to 10 loci for individual 1
pop2 = editGenomeTopQtl(pop, ind=1, nQtl=10, simParam=SP)

Fast RR-BLUP

Description

Solves an RR-BLUP model for genomic predictions given known variance components. This implementation is meant as a fast and low memory alternative to RRBLUP or RRBLUP2. Unlike the those functions, the fastRRBLUP does not fit fixed effects (other than the intercept) or account for unequal replication.

Usage

fastRRBLUP(
  pop,
  traits = 1,
  use = "pheno",
  snpChip = 1,
  useQtl = FALSE,
  maxIter = 1000,
  Vu = NULL,
  Ve = NULL,
  simParam = NULL,
  ...
)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=20)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)
SP$setVarE(h2=0.5)
SP$addSnpChip(10)

#Create population
pop = newPop(founderPop, simParam=SP)

#Run GS model and set EBV
ans = fastRRBLUP(pop, simParam=SP)
pop = setEBV(pop, ans, simParam=SP)

#Evaluate accuracy
cor(gv(pop), ebv(pop))

Sumarize genetic parameters

Description

Calculates genetic and genic additive and dominance variances for an object of Pop-class

Usage

genParam(pop, simParam = NULL)

Arguments

Value

varA

an nTrait by nTrait matrix of additive genetic variances

varD

an nTrait by nTrait matrix of dominance genetic variances

varAA

an nTrait by nTrait matrix of additive-by-additive genetic variances

varG

an nTrait by nTrait matrix of total genetic variances

genicVarA

an nTrait vector of additive genic variances

genicVarD

an nTrait vector of dominance genic variances

genicVarAA

an nTrait vector of additive-by-additive genic variances

genicVarG

an nTrait vector of total genic variances

covA_HW

an nTrait vector of additive covariances due to non-random mating

covD_HW

an nTrait vector of dominance covariances due to non-random mating

covAA_HW

an nTrait vector of additive-by-additive covariances due to non-random mating

covG_HW

an nTrait vector of total genic covariances due to non-random mating

covA_L

an nTrait vector of additive covariances due to linkage disequilibrium

covD_L

an nTrait vector of dominance covariances due to linkage disequilibrium

covAA_L

an nTrait vector of additive-by-additive covariances due to linkage disequilibrium

covAD_L

an nTrait vector of additive by dominance covariances due to linkage disequilibrium

covAAA_L

an nTrait vector of additive by additive-by-additive covariances due to linkage disequilibrium

covDAA_L

an nTrait vector of dominance by additive-by-additive covariances due to linkage disequilibrium

covG_L

an nTrait vector of total genic covariances due to linkage disequilibrium

mu

an nTrait vector of trait means

mu_HW

an nTrait vector of expected trait means under random mating

gv

a matrix of genetic values with dimensions nInd by nTraits

bv

a matrix of breeding values with dimensions nInd by nTraits

dd

a matrix of dominance deviations with dimensions nInd by nTraits

aa

a matrix of additive-by-additive epistatic deviations with dimensions nInd by nTraits

gv_mu

an nTrait vector of intercepts with dimensions nInd by nTraits

gv_a

a matrix of additive genetic values with dimensions nInd by nTraits

gv_d

a matrix of dominance genetic values with dimensions nInd by nTraits

gv_aa

a matrix of additive-by-additive genetic values with dimensions nInd by nTraits

alpha

a list of average allele subsitution effects with length nTraits

alpha_HW

a list of average allele subsitution effects at Hardy-Weinberg equilibrium with length nTraits

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitAD(10, meanDD=0.5)
SP$setVarE(h2=0.5)


#Create population
pop = newPop(founderPop, simParam=SP)
ans = genParam(pop, simParam=SP)

Additive genic variance

Description

Returns additive genic variance for all traits

Usage

genicVarA(pop, simParam = NULL)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitAD(10, meanDD=0.5)
SP$setVarE(h2=0.5)


#Create population
pop = newPop(founderPop, simParam=SP)
genicVarA(pop, simParam=SP)

Additive-by-additive genic variance

Description

Returns additive-by-additive epistatic genic variance for all traits

Usage

genicVarAA(pop, simParam = NULL)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitAD(10, meanDD=0.5)
SP$setVarE(h2=0.5)


#Create population
pop = newPop(founderPop, simParam=SP)
genicVarAA(pop, simParam=SP)

Dominance genic variance

Description

Returns dominance genic variance for all traits

Usage

genicVarD(pop, simParam = NULL)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitAD(10, meanDD=0.5)
SP$setVarE(h2=0.5)


#Create population
pop = newPop(founderPop, simParam=SP)
genicVarD(pop, simParam=SP)

Total genic variance

Description

Returns total genic variance for all traits

Usage

genicVarG(pop, simParam = NULL)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitAD(10, meanDD=0.5)
SP$setVarE(h2=0.5)


#Create population
pop = newPop(founderPop, simParam=SP)
genicVarG(pop, simParam=SP)

Get genetic map

Description

Retrieves the genetic map for all loci.

Usage

getGenMap(object = NULL, sex = "A")

Arguments

Value

Returns a data.frame with:

id

Unique identifier for locus

chr

Chromosome containing the locus

pos

Genetic map position

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
getGenMap(founderPop)

Number of available threads

Description

Gets the number of available threads by calling the OpenMP function omp_get_max_threads()

Usage

getNumThreads()

Value

integer

Examples

getNumThreads()

Get pedigree

Description

Returns the population's pedigree as stored in the id, mother and father slots. NULL is returned if the input population lacks the required.

Usage

getPed(pop)

Arguments

Examples

# Create a founder population
founderPop = quickHaplo(2,1,2)

# Set simulation parameters
SP = SimParam$new(founderPop)

# Create a population
pop = newPop(founderPop, simParam=SP)

# Get the pedigree
getPed(pop)

# Returns NULL when a population lacks a pedigree
getPed(founderPop)

Get QTL genetic map

Description

Retrieves the genetic map for the QTL of a given trait.

Usage

getQtlMap(trait = 1, sex = "A", simParam = NULL)

Arguments

Value

Returns a data.frame with:

id

Unique identifier for the QTL

chr

Chromosome containing the QTL

site

Segregating site on the chromosome

pos

Genetic map position

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(5)

#Pull SNP map
getQtlMap(trait=1, simParam=SP)

Get SNP genetic map

Description

Retrieves the genetic map for a given SNP chip.

Usage

getSnpMap(snpChip = 1, sex = "A", simParam = NULL)

Arguments

Value

Returns a data.frame with:

id

Unique identifier for the SNP

chr

Chromosome containing the SNP

site

Segregating site on the chromosome

pos

Genetic map position

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addSnpChip(5)

#Pull SNP map
getSnpMap(snpChip=1, simParam=SP)

Genetic value

Description

A wrapper for accessing the gv slot

Usage

gv(pop)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitAD(10, meanDD=0.5)
SP$setVarE(h2=0.5)


#Create population
pop = newPop(founderPop, simParam=SP)
gv(pop)

Hybrid crossing

Description

A convenient function for hybrid plant breeding simulations. Allows for easy specification of a test cross scheme and/or creation of an object of HybridPop-class. Note that the HybridPop-class should only be used if the parents were created using the makeDH function or newPop using inbred founders. The id for new individuals is [mother_id]_[father_id]

Usage

hybridCross(
  females,
  males,
  crossPlan = "testcross",
  returnHybridPop = FALSE,
  simParam = NULL
)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=2, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)


#Create population
pop = newPop(founderPop, simParam=SP)

#Make crosses for full diallele
pop2 = hybridCross(pop, pop, simParam=SP)

Import genetic map

Description

Formats a genetic map stored in a data.frame to AlphaSimR's internal format. Map positions must be in Morgans.

Usage

importGenMap(genMap)

Arguments

Value

a list of named vectors

Examples

genMap = data.frame(markerName=letters[1:5],
                    chromosome=c(1,1,1,2,2),
                    position=c(0,0.5,1,0.15,0.4))

asrMap = importGenMap(genMap=genMap)

str(asrMap)

Import haplotypes

Description

Formats haplotype in a matrix format to an AlphaSimR population that can be used to initialize a simulation. This function serves as wrapper for newMapPop that utilizes a more user friendly input format.

Usage

importHaplo(haplo, genMap, ploidy = 2L, ped = NULL)

Arguments

Details

The optional pedigree can be a data.frame, matrix or a vector. If the object is a data.frame or matrix, the first three columns must include information in the following order: id, mother, and father. All values are coerced using as.character. If the object is a vector, it is assumed to only include the id. In this case, the mother and father will be set to "0" for all individuals.

Value

a MapPop-class if ped is NULL, otherwise a NamedMapPop-class

Examples

haplo = rbind(c(1,1,0,1,0),
              c(1,1,0,1,0),
              c(0,1,1,0,0),
              c(0,1,1,0,0))
colnames(haplo) = letters[1:5]

genMap = data.frame(markerName=letters[1:5],
                    chromosome=c(1,1,1,2,2),
                    position=c(0,0.5,1,0.15,0.4))

ped = data.frame(id=c("a","b"),
                 mother=c(0,0),
                 father=c(0,0))

founderPop = importHaplo(haplo=haplo, 
                         genMap=genMap,
                         ploidy=2L,
                         ped=ped)

Import inbred, diploid genotypes

Description

Formats the genotypes from inbred, diploid lines to an AlphaSimR population that can be used to initialize a simulation. An attempt is made to automatically detect 0,1,2 or -1,0,1 genotype coding. Heterozygotes or probabilistic genotypes are allowed, but will be coerced to the nearest homozygote. Pedigree information is optional and when provided will be passed to the population for easier identification in the simulation.

Usage

importInbredGeno(geno, genMap, ped = NULL)

Arguments

Details

The optional pedigree can be a data.frame, matrix or a vector. If the object is a data.frame or matrix, the first three columns must include information in the following order: id, mother, and father. All values are coerced using as.character. If the object is a vector, it is assumed to only include the id. In this case, the mother and father will be set to "0" for all individuals.

Value

a MapPop-class if ped is NULL, otherwise a NamedMapPop-class

Examples

geno = rbind(c(2,2,0,2,0),
             c(0,2,2,0,0))
colnames(geno) = letters[1:5]

genMap = data.frame(markerName=letters[1:5],
                    chromosome=c(1,1,1,2,2),
                    position=c(0,0.5,1,0.15,0.4))

ped = data.frame(id=c("a","b"),
                 mother=c(0,0),
                 father=c(0,0))

founderPop = importInbredGeno(geno=geno,
                              genMap=genMap,
                              ped=ped)

Test if individuals of a population are female or male

Description

Test if individuals of a population are female or male

Usage

isFemale(x)

isMale(x)

Arguments

Value

logical

Functions

• isMale(): Test if individuals of a population are female or male

Examples

founderGenomes <- quickHaplo(nInd = 3, nChr = 1, segSites = 100)
SP <- SimParam$new(founderGenomes)
SP$setSexes(sexes = "yes_sys")
pop <- newPop(founderGenomes)

isFemale(pop)
isMale(pop)

pop[isFemale(pop)]
pop[isFemale(pop)]@sex

Test if object is of a Population class

Description

Utilify function to test if object is of a Population class

Usage

isPop(x)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=2, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitA(10)

#Create population
pop = newPop(founderPop, simParam=SP)
isPop(pop)
isPop(SP)

Make designed crosses

Description

Makes crosses within a population using a user supplied crossing plan.

Usage

makeCross(pop, crossPlan, nProgeny = 1, simParam = NULL)

Arguments

Value

Returns an object of Pop-class

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)


#Create population
pop = newPop(founderPop, simParam=SP)

#Cross individual 1 with individual 10
crossPlan = matrix(c(1,10), nrow=1, ncol=2)
pop2 = makeCross(pop, crossPlan, simParam=SP)

Make designed crosses

Description

Makes crosses between two populations using a user supplied crossing plan.

Usage

makeCross2(females, males, crossPlan, nProgeny = 1, simParam = NULL)

Arguments

Value

Returns an object of Pop-class

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)


#Create population
pop = newPop(founderPop, simParam=SP)

#Cross individual 1 with individual 10
crossPlan = matrix(c(1,10), nrow=1, ncol=2)
pop2 = makeCross2(pop, pop, crossPlan, simParam=SP)

Generates DH lines

Description

Creates DH lines from each individual in a population. Only works with diploid individuals. For polyploids, use reduceGenome and doubleGenome.

Usage

makeDH(pop, nDH = 1, useFemale = TRUE, keepParents = TRUE, simParam = NULL)

Arguments

Value

Returns an object of Pop-class

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=2, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)


#Create population
pop = newPop(founderPop, simParam=SP)

#Create 1 DH for each individual
pop2 = makeDH(pop, simParam=SP)

Finds positions of loci by marker name

Description

Used to generate lociPerChr and lociLoc objects for a set of markers. These objects can be passed other functions for pulling genotypes or haplotypes.

Usage

mapLoci(markers, genMap)

Arguments

Value

A list containing lociPerChr and lociLoc that can be

Mean estimated breeding values

Description

Returns the mean estimated breeding values for all traits

Usage

meanEBV(pop)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitA(10)
trtH2 = 0.5
SP$setVarE(h2=trtH2)


#Create population
pop = newPop(founderPop, simParam=SP)
pop@ebv = trtH2 * (pop@pheno - meanP(pop)) #ind performance based EBV
meanEBV(pop)

Mean genetic values

Description

Returns the mean genetic values for all traits

Usage

meanG(pop)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitA(10)
SP$setVarE(h2=0.5)


#Create population
pop = newPop(founderPop, simParam=SP)
meanG(pop)

Mean phenotypic values

Description

Returns the mean phenotypic values for all traits

Usage

meanP(pop)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitA(10)
SP$setVarE(h2=0.5)


#Create population
pop = newPop(founderPop, simParam=SP)
meanP(pop)

Combine genomes of individuals

Description

This function is designed to model the pairing of gametes. The male and female individuals are treated as gametes, so the ploidy of newly created individuals will be the sum of it parents.

Usage

mergeGenome(females, males, crossPlan, simParam = NULL)

Arguments

Value

Returns an object of Pop-class

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)


#Create population
pop = newPop(founderPop, simParam=SP)

#Cross individual 1 with individual 10
crossPlan = matrix(c(1,10), nrow=1, ncol=2)
pop2 = mergeGenome(pop, pop, crossPlan, simParam=SP)

Merge list of populations

Description

Rapidly merges a list of populations into a single population

Usage

mergePops(popList)

Arguments

Value

Returns a Pop-class

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)


#Create a list of populations and merge list
pop = newPop(founderPop, simParam=SP)
pop@misc$tmp = rnorm(n=10)
pop@misc$tmp2 = rnorm(n=10)

popList = list(pop, pop)
pop2 = mergePops(popList)

Add Random Mutations

Description

Adds random mutations to individuals in a population. Note that any existing phenotypes or EBVs are kept. Thus, the user will need to run setPheno and/or setEBV to generate new phenotypes or EBVs that reflect changes introduced by the new mutations.

Usage

mutate(pop, mutRate = 2.5e-08, returnPos = FALSE, simParam = NULL)

Arguments

Value

an object of Pop-class if returnPos=FALSE or a list containing a Pop-class and a data.frame containing the postions of mutations if returnPos=TRUE

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=2, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)

#Create population
pop = newPop(founderPop, simParam=SP)

#Introduce mutations
pop = mutate(pop, simParam=SP)

Number of individuals

Description

A wrapper for accessing the nInd slot

Usage

nInd(pop)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitAD(10, meanDD=0.5)
SP$setVarE(h2=0.5)

#Create population
pop = newPop(founderPop, simParam=SP)
nInd(pop)

Creates an empty population

Description

Creates an empty Pop-class object with user defined ploidy and other parameters taken from simParam.

Usage

newEmptyPop(ploidy = 2L, simParam = NULL)

Arguments

Value

Returns an object of Pop-class with zero individuals

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=2, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitA(10)

#Create empty population
pop = newEmptyPop(simParam=SP)
isPop(pop)

New MapPop

Description

Creates a new MapPop-class from user supplied genetic maps and haplotypes.

Usage

newMapPop(genMap, haplotypes, inbred = FALSE, ploidy = 2L)

Arguments

Details

Each item of genMap must be a vector of ordered genetic lengths in Morgans. The first value must be zero. The length of the vector determines the number of segregating sites on the chromosome.

Each item of haplotypes must be coercible to a matrix. The columns of this matrix correspond to segregating sites. The number of rows must match the number of individuals times the ploidy if using inbred=FALSE. If using inbred=TRUE, the number of rows must equal the number of individuals. The haplotypes can be stored as numeric, integer or raw. The underlying C++ function will use raw.

Value

an object of MapPop-class

Examples

# Create genetic map for two chromosomes, each 1 Morgan long
# Each chromosome contains 11 equally spaced segregating sites
genMap = list(seq(0,1,length.out=11),
               seq(0,1,length.out=11))

# Create haplotypes for 10 outbred individuals
chr1 = sample(x=0:1,size=20*11,replace=TRUE)
chr1 = matrix(chr1,nrow=20,ncol=11)
chr2 = sample(x=0:1,size=20*11,replace=TRUE)
chr2 = matrix(chr2,nrow=20,ncol=11)
haplotypes = list(chr1,chr2)

founderPop = newMapPop(genMap=genMap, haplotypes=haplotypes)

Create new Multi Population

Description

Creates a new MultiPop-class from one or more Pop-class and/or MultiPop-class objects.

Usage

newMultiPop(...)

Arguments

Value

Returns an object of MultiPop-class

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=2, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitA(10)

#Create population
pop = newPop(founderPop, simParam=SP)
megaPop = newMultiPop(pop=pop)
isMultiPop(megaPop)

Create new population

Description

Creates an initial Pop-class from an object of MapPop-class or NamedMapPop-class. The function is intended for use with output from functions such as runMacs, newMapPop, or quickHaplo.

Usage

newPop(rawPop, simParam = NULL, ...)

Arguments

Details

Note that newPop takes genomes from the rawPop and uses them without recombination! Hence, if you call newPop(rawPop = founderGenomes) twice, you will get two sets of individuals with different id but the same genomes. To get genetically different sets of individuals you can subset the rawPop input, say first half for one set and the second half for the other set.

Value

Returns an object of Pop-class

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=2, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitA(10)

#Create population
pop = newPop(founderPop, simParam=SP)
isPop(pop)

#Misc
pop@misc$tmp1 = rnorm(n=2)
pop@misc$tmp2 = rnorm(n=2)

#MiscPop
pop@miscPop$tmp1 = sum(pop@misc$tmp1)
pop@miscPop$tmp2 = sum(pop@misc$tmp2)

Pedigree cross

Description

Creates a Pop-class from a generic pedigree and a set of founder individuals.

The way in which the user supplied pedigree is used depends on the value of matchID. If matchID is TRUE, the IDs in the user supplied pedigree are matched against founderNames. If matchID is FALSE, founder individuals in the user supplied pedigree are randomly sampled from founderPop.

Usage

pedigreeCross(
  founderPop,
  id,
  mother,
  father,
  matchID = FALSE,
  maxCycle = 100,
  DH = NULL,
  nSelf = NULL,
  useFemale = TRUE,
  simParam = NULL
)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=2, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)


#Create population
pop = newPop(founderPop, simParam=SP)

#Pedigree for a biparental cross with 7 generations of selfing
id = 1:10
mother = c(0,0,1,3:9)
father = c(0,0,2,3:9)
pop2 = pedigreeCross(pop, id, mother, father, simParam=SP)

Phenotype

Description

A wrapper for accessing the pheno slot

Usage

pheno(pop)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitAD(10, meanDD=0.5)
SP$setVarE(h2=0.5)


#Create population
pop = newPop(founderPop, simParam=SP)
pheno(pop)

Population variance

Description

Calculates the population variance matrix as opposed to the sample variance matrix calculated by var. i.e. divides by n instead of n-1

Usage

popVar(X)

Arguments

Value

an m by m variance-covariance matrix

Pull IBD haplotypes

Description

Retrieves IBD haplotype data

Usage

pullIbdHaplo(pop, chr = NULL, snpChip = NULL, simParam = NULL)

Arguments

Value

Returns a matrix of IBD haplotypes.

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=15)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)
SP$addSnpChip(5)
SP$setTrackRec(TRUE)

#Create population
pop = newPop(founderPop, simParam=SP)
pullIbdHaplo(pop, simParam=SP)

Pull marker genotypes

Description

Retrieves genotype data for user specified loci

Usage

pullMarkerGeno(pop, markers, asRaw = FALSE, simParam = NULL)

Arguments

Value

Returns a matrix of genotypes.

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=15)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)
SP$addSnpChip(5)

#Create population
pop = newPop(founderPop, simParam=SP)

#Pull genotype data for first two markers on chromosome one.
#Marker name is consistent with default naming in AlphaSimR.
pullMarkerGeno(pop, markers=c("1_1","1_2"), simParam=SP)

Pull marker haplotypes

Description

Retrieves haplotype data for user specified loci

Usage

pullMarkerHaplo(pop, markers, haplo = "all", asRaw = FALSE, simParam = NULL)

Arguments

Value

Returns a matrix of genotypes.

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=15)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)
SP$addSnpChip(5)
SP$setTrackRec(TRUE)

#Create population
pop = newPop(founderPop, simParam=SP)

#Pull haplotype data for first two markers on chromosome one.
#Marker name is consistent with default naming in AlphaSimR.
pullMarkerHaplo(pop, markers=c("1_1","1_2"), simParam=SP)

Pull QTL genotypes

Description

Retrieves QTL genotype data

Usage

pullQtlGeno(pop, trait = 1, chr = NULL, asRaw = FALSE, simParam = NULL)

Arguments

Value

Returns a matrix of QTL genotypes.

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=15)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)
SP$addSnpChip(5)

#Create population
pop = newPop(founderPop, simParam=SP)
pullQtlGeno(pop, simParam=SP)

Pull QTL haplotypes

Description

Retrieves QTL haplotype data

Usage

pullQtlHaplo(
  pop,
  trait = 1,
  haplo = "all",
  chr = NULL,
  asRaw = FALSE,
  simParam = NULL
)

Arguments

Value

Returns a matrix of QTL haplotypes.

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=15)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)
SP$addSnpChip(5)

#Create population
pop = newPop(founderPop, simParam=SP)
pullQtlHaplo(pop, simParam=SP)

Pull segregating site genotypes

Description

Retrieves genotype data for all segregating sites

Usage

pullSegSiteGeno(pop, chr = NULL, asRaw = FALSE, simParam = NULL)

Arguments

Value

Returns a matrix of genotypes

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=15)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitA(10)
SP$addSnpChip(5)

#Create population
pop = newPop(founderPop, simParam=SP)
pullSegSiteGeno(pop, simParam=SP)

Pull seg site haplotypes

Description

Retrieves haplotype data for all segregating sites

Usage

pullSegSiteHaplo(
  pop,
  haplo = "all",
  chr = NULL,
  asRaw = FALSE,
  simParam = NULL
)

Arguments

Value

Returns a matrix of haplotypes

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=15)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)
SP$addSnpChip(5)

#Create population
pop = newPop(founderPop, simParam=SP)
pullSegSiteHaplo(pop, simParam=SP)

Pull SNP genotypes

Description

Retrieves SNP genotype data

Usage

pullSnpGeno(pop, snpChip = 1, chr = NULL, asRaw = FALSE, simParam = NULL)

Arguments

Value

Returns a matrix of SNP genotypes.

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=15)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)
SP$addSnpChip(5)

#Create population
pop = newPop(founderPop, simParam=SP)
pullSnpGeno(pop, simParam=SP)

Pull SNP haplotypes

Description

Retrieves SNP haplotype data

Usage

pullSnpHaplo(
  pop,
  snpChip = 1,
  haplo = "all",
  chr = NULL,
  asRaw = FALSE,
  simParam = NULL
)

Arguments

Value

Returns a matrix of SNP haplotypes.

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=15)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)
SP$addSnpChip(5)

#Create population
pop = newPop(founderPop, simParam=SP)
pullSnpHaplo(pop, simParam=SP)

Quick founder haplotype simulation

Description

Rapidly simulates founder haplotypes by randomly sampling 0s and 1s. This is equivalent to having all loci with allele frequency 0.5 and being in linkage equilibrium.

Usage

quickHaplo(nInd, nChr, segSites, genLen = 1, ploidy = 2L, inbred = FALSE)

Arguments

Value

an object of MapPop-class

Examples

# Creates a populations of 10 outbred individuals
# Their genome consists of 1 chromosome and 100 segregating sites
founderPop = quickHaplo(nInd=10,nChr=1,segSites=100)

Make random crosses

Description

A wrapper for makeCross that randomly selects parental combinations for all possible combinantions.

Usage

randCross(
  pop,
  nCrosses,
  nProgeny = 1,
  balance = TRUE,
  parents = NULL,
  ignoreSexes = FALSE,
  simParam = NULL
)

Arguments

Value

Returns an object of Pop-class

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)


#Create population
pop = newPop(founderPop, simParam=SP)

#Make 10 crosses
pop2 = randCross(pop, 10, simParam=SP)

Make random crosses

Description

A wrapper for makeCross2 that randomly selects parental combinations for all possible combinantions between two populations.

Usage

randCross2(
  females,
  males,
  nCrosses,
  nProgeny = 1,
  balance = TRUE,
  femaleParents = NULL,
  maleParents = NULL,
  ignoreSexes = FALSE,
  simParam = NULL
)

Arguments

Value

Returns an object of Pop-class

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)


#Create population
pop = newPop(founderPop, simParam=SP)

#Make 10 crosses
pop2 = randCross2(pop, pop, 10, simParam=SP)

Create individuals with reduced ploidy

Description

Creates new individuals from gametes. This function was created to model the creation of diploid potatoes from tetraploid potatoes. It can be used on any population with an even ploidy level. The newly created individuals will have half the ploidy level of the originals. The reduction can occur with or without genetic recombination.

Usage

reduceGenome(
  pop,
  nProgeny = 1,
  useFemale = TRUE,
  keepParents = TRUE,
  simRecomb = TRUE,
  simParam = NULL
)

Arguments

Value

Returns an object of Pop-class

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=2, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)


#Create population
pop = newPop(founderPop, simParam=SP)

#Create individuals with reduced ploidy
pop2 = reduceGenome(pop, simParam=SP)

Reset population

Description

Recalculates a population's genetic values and resets phenotypes and EBVs.

Usage

resetPop(pop, simParam = NULL)

Arguments

Value

an object of Pop-class

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=2, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitA(10)

#Create population
pop = newPop(founderPop, simParam=SP)

#Rescale to set mean to 1
SP$rescaleTraits(mean=1)
pop = resetPop(pop, simParam=SP)

Create founder haplotypes using MaCS

Description

Uses the MaCS software to produce founder haplotypes (Chen et al. 2009).

Usage

runMacs(
  nInd,
  nChr = 1,
  segSites = NULL,
  inbred = FALSE,
  species = "GENERIC",
  split = NULL,
  ploidy = 2L,
  manualCommand = NULL,
  manualGenLen = NULL,
  nThreads = NULL
)

Arguments

Details

There are currently three species histories available: GENERIC, CATTLE, WHEAT, and MAIZE.

The GENERIC history is meant to be a reasonable all-purpose choice. It runs quickly and models a population with an effective populations size that has gone through several historic bottlenecks. This species history is used as the default arguments in the runMacs2 function, so the user should examine this function for the details of how the species is modeled.

The CATTLE history is based off of real genome sequence data (MacLeod et al. 2013).

The WHEAT (Gaynor et al. 2017) and MAIZE (Hickey et al. 2014) histories have been included due to their use in previous simulations. However, it should be noted that neither faithfully simulates its respective species. This is apparent by the low number of segregating sites simulated by each history relative to their real-world analogs. Adjusting these histories to better represent their real-world analogs would result in a drastic increase to runtime.

Value

an object of MapPop-class

References

Chen GK, Marjoram P, Wall JD (2009). “Fast and Flexible Simulation of DNA Sequence Data.” Genome Research, 19, 136-142. https://genome.cshlp.org/content/19/1/136.

Gaynor RC, Gorjanc G, Bentley AR, Ober ES, Howell P, Jackson R, Mackay IJ, Hickey JM (2017). “A Two-Part Strategy for Using Genomic Selection to Develop Inbred Lines.” Crop Science, 57(5), 2372–2386. ISSN 0011-183X, doi:10.2135/cropsci2016.09.0742, https://acsess.onlinelibrary.wiley.com/doi/full/10.2135/cropsci2016.09.0742.

Hickey JMDS, Crossa J, Hearne S, Babu R, Prasanna BM, Grondona M, Zambelli A, Windhausen VS, Mathews K, Gorjanc G (2014). “Evaluation of Genomic Selection Training Population Designs and Genotyping Strategies in Plant Breeding Programs Using Simulation.” Crop Science, 54(4), 1476-1488. doi:10.2135/cropsci2013.03.0195.

MacLeod IM, Larkin DM, Lewin HAHBJ, Goddard ME (2013). “Inferring Demography from Runs of Homozygosity in Whole-Genome Sequence, with Correction for Sequence Errors.” Molecular Biology and Evolution, 30(9), 2209–2223. doi:10.1093/molbev/mst125.

Examples

# Creates a populations of 10 outbred individuals
# Their genome consists of 1 chromosome and 100 segregating sites
## Not run: 
founderPop = runMacs(nInd=10,nChr=1,segSites=100)

## End(Not run)

Alternative wrapper for MaCS

Description

A wrapper function for runMacs. This wrapper is designed to provide a more intuitive interface for writing custom commands in MaCS (Chen et al. 2009). It effectively automates the creation of an appropriate line for the manualCommand argument in runMacs using user supplied variables, but only allows for a subset of the functionality offered by this argument. The default arguments of this function were chosen to match species="GENERIC" in runMacs.

Usage

runMacs2(
  nInd,
  nChr = 1,
  segSites = NULL,
  Ne = 100,
  bp = 1e+08,
  genLen = 1,
  mutRate = 2.5e-08,
  histNe = c(500, 1500, 6000, 12000, 1e+05),
  histGen = c(100, 1000, 10000, 1e+05, 1e+06),
  inbred = FALSE,
  split = NULL,
  ploidy = 2L,
  returnCommand = FALSE,
  nThreads = NULL
)

Arguments

Value

an object of MapPop-class or if returnCommand is true a string giving the MaCS command passed to the manualCommand argument of runMacs.

References

Chen GK, Marjoram P, Wall JD (2009). “Fast and Flexible Simulation of DNA Sequence Data.” Genome Research, 19, 136-142. https://genome.cshlp.org/content/19/1/136.

Examples

# Creates a populations of 10 outbred individuals
# Their genome consists of 1 chromosome and 100 segregating sites
# The command is equivalent to using species="GENERIC" in runMacs
## Not run: 
founderPop = runMacs2(nInd=10,nChr=1,segSites=100)

# runMacs() Implementation of the cattle demography following
#  Macleod et al. (2013) https://doi.org/10.1093/molbev/mst125
cattleChrSum = 2.8e9 # https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_002263795.3/
(cattleChrBp = cattleChrSum / 30)
recRate = 9.26e-09
(cattleGenLen = recRate * cattleChrBp)
mutRate = 1.20e-08
runMacs2(nInd = 10, nChr = 1, Ne = 90, bp = cattleChrBp,
         genLen = cattleGenLen, mutRate = 1.20e-08,
         histNe  = c(120, 250, 350, 1000, 1500, 2000, 2500, 3500, 7000, 10000, 17000, 62000),
         histGen = c(  3,   6,  12,   18,   24,  154,  454,  654, 1754,  2354,  3354, 33154),
         returnCommand = TRUE)

## End(Not run)

Sample haplotypes from a MapPop

Description

Creates a new MapPop-class from an existing MapPop-class by randomly sampling haplotypes.

Usage

sampleHaplo(mapPop, nInd, inbred = FALSE, ploidy = NULL, replace = TRUE)

Arguments

Value

an object of MapPop-class

Examples

founderPop = quickHaplo(nInd=2,nChr=1,segSites=11,inbred=TRUE)
founderPop = sampleHaplo(mapPop=founderPop,nInd=20)

Selection index

Description

Calculates values of a selection index given trait values and weights. This function is intended to be used in combination with selection functions working on populations such as selectInd.

Usage

selIndex(Y, b, scale = FALSE)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

#Model two genetically correlated traits
G = 1.5*diag(2)-0.5 #Genetic correlation matrix
SP$addTraitA(10, mean=c(0,0), var=c(1,1), corA=G)
SP$setVarE(h2=c(0.5,0.5))

#Create population
pop = newPop(founderPop, simParam=SP)

#Calculate Smith-Hazel weights
econWt = c(1, 1)
b = smithHazel(econWt, varG(pop), varP(pop))

#Selection 2 best individuals using Smith-Hazel index
#selIndex is used as a trait
pop2 = selectInd(pop, nInd=2, trait=selIndex,
                 simParam=SP, b=b)

Selection intensity

Description

Calculates the standardized selection intensity

Usage

selInt(p)

Arguments

Examples

selInt(0.1)

Select and randomly cross

Description

This is a wrapper that combines the functionalities of randCross and selectInd. The purpose of this wrapper is to combine both selection and crossing in one function call that minimized the amount of intermediate populations created. This reduces RAM usage and simplifies code writing. Note that this wrapper does not provide the full functionality of either function.

Usage

selectCross(
  pop,
  nInd = NULL,
  nFemale = NULL,
  nMale = NULL,
  nCrosses,
  nProgeny = 1,
  trait = 1,
  use = "pheno",
  selectTop = TRUE,
  simParam = NULL,
  ...,
  balance = TRUE
)

Arguments

Value

Returns an object of Pop-class

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)
SP$setVarE(h2=0.5)

#Create population
pop = newPop(founderPop, simParam=SP)

#Select 4 individuals and make 8 crosses
pop2 = selectCross(pop, nInd=4, nCrosses=8, simParam=SP)

Select families

Description

Selects a subset of full-sib families from a population.

Usage

selectFam(
  pop,
  nFam,
  trait = 1,
  use = "pheno",
  sex = "B",
  famType = "B",
  selectTop = TRUE,
  returnPop = TRUE,
  candidates = NULL,
  simParam = NULL,
  ...
)

Arguments

Value

Returns an object of Pop-class, HybridPop-class or MultiPop-class

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)
SP$setVarE(h2=0.5)

#Create population
pop = newPop(founderPop, simParam=SP)

#Create 3 biparental families with 10 progeny
pop2 = randCross(pop, nCrosses=3, nProgeny=10, simParam=SP)

#Select best 2 families
pop3 = selectFam(pop2, 2, simParam=SP)

Select individuals

Description

Selects a subset of nInd individuals from a population.

Usage

selectInd(
  pop,
  nInd,
  trait = 1,
  use = "pheno",
  sex = "B",
  selectTop = TRUE,
  returnPop = TRUE,
  candidates = NULL,
  simParam = NULL,
  ...
)

Arguments

Value

Returns an object of Pop-class, HybridPop-class or MultiPop-class

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)
SP$setVarE(h2=0.5)

#Create population
pop = newPop(founderPop, simParam=SP)

#Select top 5 (directional selection)
pop2 = selectInd(pop, 5, simParam=SP)
hist(pop@pheno); abline(v=pop@pheno, lwd=2)
abline(v=pop2@pheno, col="red", lwd=2)

#Select 5 most deviating from an optima (disruptive selection)
squaredDeviation = function(x, optima=0) (x - optima)^2
pop3 = selectInd(pop, 5, trait=squaredDeviation, selectTop=TRUE, simParam=SP)
hist(pop@pheno); abline(v=pop@pheno, lwd=2)
abline(v=pop3@pheno, col="red", lwd=2)

#Select 5 least deviating from an optima (stabilising selection)
pop4 = selectInd(pop, 5, trait=squaredDeviation, selectTop=FALSE, simParam=SP)
hist(pop@pheno); abline(v=pop@pheno, lwd=2)
abline(v=pop4@pheno, col="red", lwd=2)

#Select 5 individuals based on miscelaneous information with use function
pop@misc = list(smth=rnorm(10), smth2=rnorm(10))
useFunc = function(pop, trait=NULL) pop@misc$smth + pop@misc$smth2
pop5 = selectInd(pop, 5, use=useFunc, simParam=SP)
pop5@id

#... equivalent result with the use & trait function
useFunc2 = function(pop, trait=NULL) cbind(pop@misc$smth, pop@misc$smth2)
trtFunc = function(x) rowSums(x)
pop6 = selectInd(pop, 5, trait=trtFunc, use=useFunc2, simParam=SP)
pop6@id

Select open pollinating plants

Description

This function models selection in an open pollinating plant population. It allows for varying the percentage of selfing. The function also provides an option for modeling selection as occuring before or after pollination.

Usage

selectOP(
  pop,
  nInd,
  nSeeds,
  probSelf = 0,
  pollenControl = FALSE,
  trait = 1,
  use = "pheno",
  selectTop = TRUE,
  candidates = NULL,
  simParam = NULL,
  ...
)

Arguments

Value

Returns an object of Pop-class or MultiPop-class

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)
SP$setVarE(h2=0.5)

#Create population
pop = newPop(founderPop, simParam=SP)

#Create new population by selecting the best 3 plant
#Assuming 50% selfing in plants and 10 seeds per plant
pop2 = selectOP(pop, nInd=3, nSeeds=10, probSelf=0.5, simParam=SP)

Select individuals within families

Description

Selects a subset of nInd individuals from each full-sib family within a population. Will return all individuals from a full-sib family if it has less than or equal to nInd individuals.

Usage

selectWithinFam(
  pop,
  nInd,
  trait = 1,
  use = "pheno",
  sex = "B",
  famType = "B",
  selectTop = TRUE,
  returnPop = TRUE,
  candidates = NULL,
  simParam = NULL,
  ...
)

Arguments

Value

Returns an object of Pop-class, HybridPop-class or MultiPop-class

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)
SP$setVarE(h2=0.5)

#Create population
pop = newPop(founderPop, simParam=SP)

#Create 3 biparental families with 10 progeny
pop2 = randCross(pop, nCrosses=3, nProgeny=10, simParam=SP)

#Select best individual per family
pop3 = selectWithinFam(pop2, 1, simParam=SP)

Self individuals

Description

Creates selfed progeny from each individual in a population. Only works when sexes is "no".

Usage

self(pop, nProgeny = 1, parents = NULL, keepParents = TRUE, simParam = NULL)

Arguments

Value

Returns an object of Pop-class

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=2, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)


#Create population
pop = newPop(founderPop, simParam=SP)

#Self pollinate each individual
pop2 = self(pop, simParam=SP)

Set estimated breeding values (EBV)

Description

Adds genomic estimated values to a populations's EBV slot using output from a genomic selection functions. The genomic estimated values can be either estimated breeding values, estimated genetic values, or estimated general combining values.

Usage

setEBV(
  pop,
  solution,
  value = "gv",
  targetPop = NULL,
  append = FALSE,
  simParam = NULL
)

Arguments

Value

Returns an object of Pop-class

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=20)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)
SP$setVarE(h2=0.5)
SP$addSnpChip(10)

#Create population
pop = newPop(founderPop, simParam=SP)

#Run GS model and set EBV
ans = RRBLUP(pop, simParam=SP)
pop = setEBV(pop, ans, simParam=SP)

#Evaluate accuracy
cor(gv(pop), ebv(pop))

Set marker haplotypes

Description

Manually sets the haplotypes in a population for all individuals at one or more loci.

Usage

setMarkerHaplo(pop, haplo, simParam = NULL)

Arguments

Details

The format of the haplotype matrix should match the format of the output from pullMarkerHaplo with the option haplo="all". Thus, it is recommended that this function is first used to extract the haplotypes and that any desired changes be made to the output of pullMarkerHaplo before passing the matrix to setMarkerHaplo. Any changes made to QTL may potentially result in changes to an individuals genetic value. These changes will be reflected in the gv and/or gxe slot. All other slots will remain unchanged, so the ebv and pheno slots will not reflect the new genotypes.

Value

an object of the same class as the "pop" input

Examples

# Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=15)

# Extract haplotypes for marker "1_1"
H = pullMarkerHaplo(founderPop, markers="1_1")

# Set the first haplotype to 1
H[1,1] = 1L

# Set marker haplotypes
founderPop = setMarkerHaplo(founderPop, haplo=H)

Set phenotypes

Description

Sets phenotypes for all traits by adding random error from a multivariate normal distribution.

Usage

setPheno(
  pop,
  h2 = NULL,
  H2 = NULL,
  varE = NULL,
  corE = NULL,
  reps = 1,
  fixEff = 1L,
  p = NULL,
  onlyPheno = FALSE,
  traits = NULL,
  simParam = NULL
)

Arguments

Details

There are three arguments for setting the error variance of a phenotype: h2, H2, and varE. The user should only use one of these arguments. If the user supplies values for more than one, only one will be used according to order in which they are listed above.

The h2 argument allows the user to specify the error variance according to narrow-sense heritability. This calculation uses the additive genetic variance and total genetic variance in the founder population. Thus, the heritability relates to the founder population and not the current population.

The H2 argument allows the user to specify the error variance according to broad-sense heritability. This calculation uses the total genetic variance in the founder population. Thus, the heritability relates to the founder population and not the current population.

The varE argument allows the user to specify the error variance directly. The user may supply a vector describing the error variance for each trait or supply a matrix that specify the covariance of the errors.

The corE argument allows the user to specify correlations for the error covariance matrix. These correlations are be supplied in addition to the h2, H2, or varE arguments. These correlations will be used to construct a covariance matrix from a vector of variances. If the user supplied a covariance matrix to varE, these correlations will supercede values provided in that matrix.

The reps parameter is for convenient representation of replicated data. It is intended to represent replicated yield trials in plant breeding programs. In this case, varE is set to the plot error and reps is set to the number of plots per entry. The resulting phenotype represents the entry-means.

Value

Returns an object of Pop-class or HybridPop-class if onlyPheno=FALSE, if onlyPheno=TRUE a matrix is returned

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)

#Create population
pop = newPop(founderPop, simParam=SP)

#Add phenotype with error variance of 1
pop = setPheno(pop, varE=1)

Set GCA as phenotype

Description

Calculates general combining ability from a set of testers and returns these values as phenotypes for a population.

Usage

setPhenoGCA(
  pop,
  testers,
  use = "pheno",
  h2 = NULL,
  H2 = NULL,
  varE = NULL,
  corE = NULL,
  reps = 1,
  fixEff = 1L,
  p = NULL,
  inbred = FALSE,
  onlyPheno = FALSE,
  simParam = NULL
)

Arguments

Value

Returns an object of Pop-class or a matrix if onlyPheno=TRUE

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10, inbred=TRUE)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)

#Create population
pop = newPop(founderPop, simParam=SP)

#Set phenotype to average per
pop2 = setPhenoGCA(pop, pop, use="gv", inbred=TRUE, simParam=SP)

Set progeny test as phenotype

Description

Models a progeny test of individuals in 'pop'. Returns 'pop' with a phenotype representing the average performance of their progeny. The phenotype is generated by mating individuals in 'pop' to randomly chosen individuals in testPop a number of times equal to 'nMatePerInd'.

Usage

setPhenoProgTest(
  pop,
  testPop,
  nMatePerInd = 1L,
  use = "pheno",
  h2 = NULL,
  H2 = NULL,
  varE = NULL,
  corE = NULL,
  reps = 1,
  fixEff = 1L,
  p = NULL,
  onlyPheno = FALSE,
  simParam = NULL
)

Arguments

Details

The reps parameter is for convenient representation of replicated data. It was intended for representation of replicated yield trials in plant breeding programs. In this case, varE is set to the plot error and reps is set to the number plots per entry. The resulting phenotype would reflect the mean of all replications.

Value

Returns an object of Pop-class or a matrix if onlyPheno=TRUE

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10, inbred=TRUE)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)

#Create two populations of 5 individuals
pop1 = newPop(founderPop[1:5], simParam=SP)
pop2 = newPop(founderPop[6:10], simParam=SP)

#Set phenotype according to a progeny test
pop3 = setPhenoProgTest(pop1, pop2, use="gv", simParam=SP)

Calculate Smith-Hazel weights

Description

Calculates weights for Smith-Hazel index given economice weights and phenotypic and genotypic variance-covariance matrices.

Usage

smithHazel(econWt, varG, varP)

Arguments

Value

a vector of weight for calculating index values

Examples

G = 1.5*diag(2)-0.5
E = diag(2)
P = G+E
wt = c(1,1)
smithHazel(wt, G, P)

Solve Multikernel Model

Description

Solves a univariate mixed model with multiple random effects.

Usage

solveMKM(y, X, Zlist, Klist, maxIter = 40L, tol = 1e-04)

Arguments

Solve Multivariate Model

Description

Solves a multivariate mixed model of form Y=X\beta+Zu+e

Usage

solveMVM(Y, X, Z, K, tol = 1e-06, maxIter = 1000L)

Arguments

Solve RR-BLUP

Description

Solves a univariate mixed model of form y=X\beta+Mu+e

Usage

solveRRBLUP(y, X, M)

Arguments

Solve Multikernel RR-BLUP

Description

Solves a univariate mixed model with multiple random effects.

Usage

solveRRBLUPMK(y, X, Mlist, maxIter = 40L)

Arguments

Solve Multivariate RR-BLUP

Description

Solves a multivariate mixed model of form Y=X\beta+Mu+e

Usage

solveRRBLUPMV(Y, X, M, maxIter = 1000L, tol = 1e-06)

Arguments

Solve RR-BLUP with EM

Description

Solves a univariate mixed model of form y=X\beta+Mu+e using the Expectation-Maximization algorithm.

Usage

solveRRBLUP_EM(Y, X, M, Vu, Ve, tol, maxIter, useEM)

Arguments

Solve RR-BLUP with EM and 2 random effects

Description

Solves a univariate mixed model of form y=X\beta+M_1u_1+M_2u_2+e using the Expectation-Maximization algorithm.

Usage

solveRRBLUP_EM2(Y, X, M1, M2, Vu1, Vu2, Ve, tol, maxIter, useEM)

Arguments

Solve RR-BLUP with EM and 3 random effects

Description

Solves a univariate mixed model of form y=X\beta+M_1u_1+M_2u_2+M_3u_3+e using the Expectation-Maximization algorithm.

Usage

solveRRBLUP_EM3(Y, X, M1, M2, M3, Vu1, Vu2, Vu3, Ve, tol, maxIter, useEM)

Arguments

Solve Univariate Model

Description

Solves a univariate mixed model of form y=X\beta+Zu+e

Usage

solveUVM(y, X, Z, K)

Arguments

Linear transformation matrix

Description

Creates an m by m linear transformation matrix that can be applied to n by m uncorrelated deviates sampled from a standard normal distribution to produce correlated deviates with an arbitrary correlation of R. If R is not positive semi-definite, the function returns smoothing and returns a warning (see details).

Usage

transMat(R)

Arguments

Details

An eigendecomposition is applied to the correlation matrix and used to test if it is positive semi-definite. If the matrix is not positive semi-definite, it is not a valid correlation matrix. In this case, smoothing is applied to the matrix (as described in the 'cor.smooth' of the 'psych' library) to obtain a valid correlation matrix. The resulting deviates will thus not exactly match the desired correlation, but will hopefully be close if the input matrix wasn't too far removed from a valid correlation matrix.

Examples

# Create an 2x2 correlation matrix
R = 0.5*diag(2) + 0.5

# Sample 1000 uncorrelated deviates from a
# bivariate standard normal distribution
X = matrix(rnorm(2*1000), ncol=2)

# Compute the transformation matrix
T = transMat(R)

# Apply the transformation to the deviates
Y = X%*%T

# Measure the sample correlation
cor(Y)

Usefulness criterion

Description

Calculates the usefulness criterion

Usage

usefulness(
  pop,
  trait = 1,
  use = "gv",
  p = 0.1,
  selectTop = TRUE,
  simParam = NULL,
  ...
)

Arguments

Value

Returns a numeric value

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=2, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)

SP$addTraitA(10)

#Create population
pop = newPop(founderPop, simParam=SP)

#Determine usefulness of population
usefulness(pop, simParam=SP)

#Should be equivalent to GV of best individual
max(gv(pop))

Additive variance

Description

Returns additive variance for all traits

Usage

varA(pop, simParam = NULL)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitAD(10, meanDD=0.5)
SP$setVarE(h2=0.5)


#Create population
pop = newPop(founderPop, simParam=SP)
varA(pop, simParam=SP)

Additive-by-additive epistatic variance

Description

Returns additive-by-additive epistatic variance for all traits

Usage

varAA(pop, simParam = NULL)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitAD(10, meanDD=0.5)
SP$setVarE(h2=0.5)


#Create population
pop = newPop(founderPop, simParam=SP)
varAA(pop, simParam=SP)

Dominance variance

Description

Returns dominance variance for all traits

Usage

varD(pop, simParam = NULL)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitAD(10, meanDD=0.5)
SP$setVarE(h2=0.5)


#Create population
pop = newPop(founderPop, simParam=SP)
varD(pop, simParam=SP)

Variance of estimated breeding values

Description

Returns variance of estimated breeding values for all traits

Usage

varEBV(pop)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitA(10)
trtH2 = 0.5
SP$setVarE(h2=trtH2)


#Create population
pop = newPop(founderPop, simParam=SP)
pop@ebv = trtH2 * (pop@pheno - meanP(pop)) #ind performance based EBV
varA(pop)
varEBV(pop)

Total genetic variance

Description

Returns total genetic variance for all traits

Usage

varG(pop)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitA(10)
SP$setVarE(h2=0.5)


#Create population
pop = newPop(founderPop, simParam=SP)
varG(pop)

Phenotypic variance

Description

Returns phenotypic variance for all traits

Usage

varP(pop)

Arguments

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitA(10)
SP$setVarE(h2=0.5)


#Create population
pop = newPop(founderPop, simParam=SP)
varP(pop)

Writes a Pop-class as PLINK files

Description

Writes a Pop-class to PLINK PED and MAP files. The arguments for this function were chosen for consistency with RRBLUP2. The base pair coordinate will the locus position as stored in AlphaSimR and not an actual base pair position. This is because AlphaSimR doesn't track base pair positions, only relative positions for the loci used in the simulation.

Usage

writePlink(
  pop,
  baseName,
  traits = 1,
  use = "pheno",
  snpChip = 1,
  useQtl = FALSE,
  simParam = NULL,
  ...
)

Arguments

Examples

## Not run: 
#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=15)

#Set simulation parameters
SP = SimParam$new(founderPop)
\dontshow{SP$nThreads = 1L}
SP$setSexes(sex="yes_rand")
SP$addTraitA(nQtlPerChr=10)
SP$addSnpChip(nSnpPerChr=5)
SP$setVarE(h2=0.5)

#Create population
pop = newPop(rawPop = founderPop)

# Write out PLINK files
writePlink(pop, baseName="test")

## End(Not run)

Write data records

Description

Saves a population's phenotypic and marker data to a directory.

Usage

writeRecords(
  pop,
  dir,
  snpChip = 1,
  useQtl = FALSE,
  includeHaplo = FALSE,
  append = TRUE,
  simParam = NULL
)

Arguments