# tools:::Rd_package_title("optiSel") tools:::Rd_package_description("optiSel") package ## Details Optimum Contribution Selection After kinships, breeding values and/or native contributions of the selection candidates have been computed, function candes can be used to create an R-object containing all this information. The current average kinships and trait values are estimated by this function, and the available objective functions and constraints for optimum contribution selection are reported. The following function can then be used to compute optimum contributions: ||| |:--|:--| |opticont|Calculates optimum genetic contributions of selection candidates to the next generation,| ||and checks if all constraints are fulfilled.| Function noffspring can be used to compute the optimum numbers of offspring of selection candidates from their optimum contributions. Function matings can be used for mate allocation. Kinships For pairs of individuals the following kinships can be computed: ||| |:--|:--| |pedIBD|Calculates ped igree based probability of alleles to be IBD ("pedigree based kinship""),| |segIBD|Calculates seg ment based probability of alleles to be IBD ("segment based kinship"),| |pedIBDatN|Calculates ped igree based probability of alleles to be IBD at segments with N ative origin,| |segIBDatN|Calculates seg ment based probability of alleles to be IBD at segments with N ative origin,| |pedIBDorM|Calculates ped igree based probability of alleles to be IBD or M igrant alleles,| |segIBDandN|Calculates seg ment based probability of alleles to be IBD and have N ative origin,| |segN|Calculates seg ment based probability of alleles to have N ative origin,| |makeA|Calculates the pedigree-based additive relationship matrix.| Phenotypes and results from these functions can be combined with function candes into a single R object, which can then be used as an argument to function opticont . The segment based kinship can be used to calculate the optimum contributions of different breeds to a hypothetical multi-breed population with maximum genetic diversity by using function opticomp . 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 ped igree based the Breed Comp osition, 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 seg ment based the Breed Comp osition. 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 constrained or maximized with function opticont to remove introgressed genetic material, or alternatively, the segment-based native contribution 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 ped igree based Inbreeding .| |segInbreeding|Calculates seg ment based Inbreeding , i.e. inbreeding based on| ||runs of homozygosity (ROH).| |genecont|Calculates gene tic cont ributions 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|pre pares a Ped igree 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 sub set of a large Ped igree,| |pedplot|Plots a pedigree,| |sampleIndiv|Samples individuals from a pedigree.| Population Parameters Finally, there are some functions for estimating population parameters: ||| |:--|:--| |conttac|Calculates genetic cont ributions of breeds t o a ge c ohorts,| |summary.candes|Calculates for every age cohort several genetic parameters. These may| ||include average kinships, kinships at native loci,| ||the native effective size, and the native genome equivalent.| Genotype File Format All functions reading genotype data assume that the files are in the following format: Genotypes are phased and missing genotypes have been imputed. 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. ## Author(s) tools:::Rd_package_author("optiSel") Maintainer: tools:::Rd_package_maintainer("optiSel") ## 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 Wellmann, R. (2019). Optimum Contribution Selection and Mate Allocation for Breeding: The R Package optiSel. BMC Bioinformatics 20:25 ## Examples ```r #See ?opticont for 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 # ############################################ phen <- Pedig[Pedig$Breed=="Hinterwaelder",] pKin <- pedIBD(Pedig) pKinatN <- pedIBDatN(Pedig, thisBreed="Hinterwaelder") pop <- candes(phen=phen, pKin=pKin, pKinatN=pKinatN, quiet=TRUE, reduce.data=FALSE) Param <- summary(pop, tlim=c(1970,2005), histNe=150, base=1800, df=4) plot(Param$t, Param$Ne, type="l", ylim=c(0,150), main="Native Effective Size", ylab="Ne", xlab="") matplot(Param$t, Param[,c("pKin", "pKinatN")], type="l",ylim=c(0,1),main="Kinships", xlab="Year", ylab="mean Kinship") abline(0,0) legend("topleft", legend = c("pKin", "pKinatN"), lty=1:2, col=1:2, cex=0.6) info <- paste("Base Year =", attributes(Param)$base, " historic Ne =", attributes(Param)$histNe) plot(Param$t,Param$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") ```

optiSel-package