optiSel (version 0.9.1)

optiSel-package: optiSel

Description

optiSel

Details

Optimum Contribution Selection

The following functions can be used to compute optimum contributions:

opticont	Calculates optimum genetic contributions of selection candidates to the next generation,
opticont4mb	Calculates optimum genetic contributions of selection candidates to the next generation
	using multi-breed genotype data,
opticomp	Calculates breed composition of a multi-breed population with maximum diversity,

Function noffspring can then be used to compute the optimum numbers of offspring of selection candidates from their optimum contributions, and function matings can be used for mate allocation. The available constraint settings and objective functions for a particular data set can be determined with the following help functions:

help.opticont	Displays available objective functions and constraints for function opticont,
help.opticont4mb	Displays available objective functions and constraints for function opticont4mb.

For checking the results use

summary.opticont	Calculates genetic parameters of the offspring population, and
	checks for validity of optimum genetic contributions.

Kinships

For pairs of individuals the following kinships can be computed:

pedIBD	Calculates pedigree based probability of alleles to be IBD ("pedigree based kinship""),
segIBD	Calculates segment based probability of alleles to be IBD ("segment based kinship"),
pedIBDatN	Calculates pedigree based probability of alleles to be IBD at segments with Native origin,
segIBDatN	Calculates segment based probability of alleles to be IBD at segments with Native origin,
pedIBDorM	Calculates pedigree based probability of alleles to be IBD or Migrant alleles,
segIBDandN	Calculates segment based probability of alleles to be IBD and have Native origin,
segN	Calculates segment based probability of alleles to have Native origin,
makeA	Calculates the pedigree-based additive relationship matrix.

Results from these functions can be combined with function kinlist into a single R object, which can then be used as an argument to function opticont or opticont4mb. Function sim2dis can be used to convert a similarity matrix (e.g. a kinship matrix) into a dissimilarity matrix which is suitable for multidimensional scaling.

Breed Composition

The breed composition of crossbred individuals can be accessed with

pedBreedComp	Calculates pedigree based the Breed Composition, which is the genetic contribution
	of each individual from other breeds and from native founders. The native contribution
	is the proportion of the genome not originating from other breeds.
segBreedComp	Calculates segment based the Breed Composition. The native contribution is the
	proportion of the genome belonging to segments that have low frequency in
	other breeds.

The native contributions obtained by the above functions can be contrained or maximized with functions opticont or opticont4mb to remove introgressed genetic material, or alternatively, they can be considered a quantitative trait and included in a selection index.

Haplotype frequencies

Frequencies of haplotype segments in particular breeds can be computed and plotted with

haplofreq	Calculates the maximum frequency each segment has in a set of reference breeds,
	and the name of the breed in which the segment has maximum frequency.
	Identification of native segments.
freqlist	Combines results obtained with function haplofreq for different reference breeds
	into a single R object which is suitable for plotting.
plot.HaploFreq	Plots frequencies of haplotype segments in particular reference breeds.

Inbreeding Coefficients and Genetic Contributions

The inbreeding coefficients and genetic contributions from ancestors can be computed with:

pedInbreeding	Calculates pedigree based Inbreeding.
segInbreeding	Calculates segment based Inbreeding, i.e. inbreeding based on
	runs of homozygosity (ROH).
genecont	Calculates genetic contributions each individual has from all it's ancestors in
	the pedigree.

Preparing and plotting pedigree data

There are some functions for preparing and plotting pedigree data

prePed	prepares a Pedigree by sorting, adding founders and pruning the pedigree,
completeness	Calculates pedigree completeness in all ancestral generations,
summary.Pedig	Calculates number of equivalent complete generations, number of fully
	traced generations, number of maximum generations traced, index of
	pedigree completeness, inbreeding coefficients,
subPed	Creates a subset of a large Pedigree,
pedplot	Plots a pedigree,
sampleIndiv	Sampling Individuals from a pedigree.

Population Parameters

Finally, there are some functions for estimating population parameters:

conttac	Calculates genetic contributions of breeds to age cohorts,
kinwac	Calculates for every individual it's mean kinship with the age cohort to which
	it belongs,
summary.kinMatrices	Calculates for every age cohort several genetic parameters. These may
	include average kinships, genetic diversities, gene diversity at native loci,
	the native effective size, and the native genome equivalent.

Genotype File Format

All functions reading phased genotype data assume that the files are in the following format:

Each file has a header and no row names. Cells are separated by blank spaces. The number of rows is equal to the number of markers from the respective chromosome and the markers are in the same order as in the map. There can be some extra columns on the left hand side containing no genotype data. The remaining columns contain genotypes of individuals written as two alleles separated by a character, e.g. A/B, 0/1, A|B, A B, or 0 1. The same two symbols must be used for all markers. Column names are the IDs of the individuals. If the blank space is used as separator then the ID of each individual should be repeated in the header to get a regular delimited file. The columns to be skipped and the individual IDs must have no white spaces.

Use function read.indiv to extract the IDs of the individuals from a genotype file.

References

de Cara MAR, Villanueva B, Toro MA, Fernandez J (2013). Using genomic tools to maintain diversity and fitness in conservation programmes. Molecular Ecology. 22: 6091-6099

Wellmann, R., and Pfeiffer, I. (2009). Pedigree analysis for conservation of genetic diversity and purging. Genet. Res. 91: 209-219

Wellmann, R., and Bennewitz, J. (2011). Identification and characterization of hierarchical structures in dog breeding schemes, a novel method applied to the Norfolk Terrier. Journal of Animal Science. 89: 3846-3858

Wellmann, R., Hartwig, S., Bennewitz, J. (2012). Optimum contribution selection for conserved populations with historic migration; with application to rare cattle breeds. Genetics Selection Evolution. 44: 34

Wellmann, R., Bennewitz, J., Meuwissen, T.H.E. (2014) A unified approach to characterize and conserve adaptive and neutral genetic diversity in subdivided populations. Genet Res (Camb). 69: e16

Examples

# NOT RUN {
#See ?opticont for optimum contribution selection 
#See ?opticont4mb for multi-breed optimum contribution selection 
#These examples demonstrate computation of some population genetic parameters.

data(ExamplePed)
Pedig <- prePed(ExamplePed, thisBreed="Hinterwaelder", lastNative=1970)
head(Pedig)


############################################
# Evaluation of                            #
#    - kinships                            #
#    - genetic diversities                 #
#    - native effective size               #
#    - native genome equivalent            #
############################################

Kinships <- kinlist(pedIBD=pedIBD(Pedig), pedIBDatN=pedIBDatN(Pedig, thisBreed="Hinterwaelder"))
Param  <- summary(Kinships, Pedig, tlim=c(1970,2005), histNe=150, base=1800, df=4)

plot(Param$cohort, Param$Ne, type="l", ylim=c(0,150), 
     main="Native Effective Size", ylab="Ne", xlab="")

matplot(Param$cohort, Param[,c("pedIBD", "pedIBDatN")], 
        type="l",ylim=c(0,1),main="Kinships", xlab="Year", ylab="mean Kinship")
abline(0,0)
legend("topleft", legend = c("pedIBD", "pedIBDatN"), lty=1:2, col=1:2, cex=0.6)

info <- paste("Base Year =", attributes(Param)$base, "  historic Ne =", attributes(Param)$histNe)

plot(Param$cohort, Param$condGD, type="l", ylim=c(0,1), 
     main="Diversity at native alleles",ylab="condGD",xlab="")
mtext(info, cex=0.7)

plot(Param$cohort,Param$NGE,type="l",main="Native Genome Equivalents", 
     ylab="NGE",xlab="",ylim=c(0,7))
mtext(info, cex=0.7)


############################################
# Genetic contributions from other breeds  #
############################################

cont <- pedBreedComp(Pedig, thisBreed='Hinterwaelder')
contByYear <- conttac(cont, Pedig$Born, use=Pedig$Breed=="Hinterwaelder", mincont=0.04, long=FALSE)
round(contByYear,2)

barplot(contByYear, ylim=c(0,1), col=1:10, ylab="genetic contribution",
        legend=TRUE, args.legend=list(x="topleft",cex=0.6))


######################################################
# Frequencies of haplotype segments in other breeds  #
######################################################

data(map)
data(Cattle)
dir   <- system.file("extdata", package="optiSel")
files <- file.path(dir, paste("Chr", 1:2, ".phased", sep=""))

Freq <- freqlist(
  haplofreq(files, Cattle, map, thisBreed="Angler", refBreeds="Rotbunt",   minSNP=20),
  haplofreq(files, Cattle, map, thisBreed="Angler", refBreeds="Holstein",  minSNP=20),
  haplofreq(files, Cattle, map, thisBreed="Angler", refBreeds="Fleckvieh", minSNP=20)
  )

plot(Freq, ID=1, hap=2, refBreed="Rotbunt")


# }