ghap.predictblup | R Documentation |
Prediction of BLUP values in test individuals based on reference individuals.
ghap.predictblup(refblup, vcp, covmat, errormat = NULL, errorname = "", include.ref = TRUE, diagonals = FALSE, tol = 1e-12)
refblup |
A named numeric vector of reference BLUP values. |
vcp |
A numeric value for the variance in BLUP values. |
covmat |
A square matrix containing correlations among individuals. Both test and reference indiviudals must be present in the matrix. |
errormat |
A square error matrix for reference individuals. This matrix can be obtained with argument extras = "LHSi" in the |
errorname |
The name used for the random effect in the |
include.ref |
A logical value indicating if reference individuals should be included in the output (default = TRUE). |
diagonals |
A logical value indicating if diagonals of the covariance matrix should be used in calculations of accuracy and standard errors (default = FALSE). The default is to set diagonals to 1. For genomic estimated breeding values, using TRUE will account for inbreeding in the computation of accuracies and standard errors. |
tol |
A numeric value specifying the scalar to add to the diagonal of the covariance matrix if it is not inversible (default = 1e-12). |
A data frame with predictions of BLUP values. If an error matrix is provided, standard errors and accuracies are also included.
Yuri Tani Utsunomiya <ytutsunomiya@gmail.com>
J.F. Taylor. Implementation and accuracy of genomic selection. Aquaculture 2014. 420, S8-S14.
# #### DO NOT RUN IF NOT NECESSARY ### # # # Copy plink data in the current working directory # exfiles <- ghap.makefile(dataset = "example", # format = "plink", # verbose = TRUE) # file.copy(from = exfiles, to = "./") # # # Copy metadata in the current working directory # exfiles <- ghap.makefile(dataset = "example", # format = "meta", # verbose = TRUE) # file.copy(from = exfiles, to = "./") # # # Load plink data # plink <- ghap.loadplink("example") # # # Load phenotype and pedigree data # df <- read.table(file = "example.phenotypes", header=T) # # ### RUN ### # # # Subset individuals from the pure1 population # pure1 <- plink$id[which(plink$pop == "Pure1")] # plink <- ghap.subset(object = plink, ids = pure1, variants = plink$marker) # # # Subset markers with MAF > 0.05 # freq <- ghap.freq(plink) # mkr <- names(freq)[which(freq > 0.05)] # plink <- ghap.subset(object = plink, ids = pure1, variants = mkr) # # # Compute genomic relationship matrix # # Induce sparsity to help with matrix inversion # K <- ghap.kinship(plink, sparsity = 0.01) # # # Fit mixed model # df$rep <- df$id # model <- ghap.lmm(formula = pheno ~ 1 + (1|id) + (1|rep), # data = df, # covmat = list(id = K, rep = NULL), # extras = "LHSi") # refblup <- model$random$id$Estimate # names(refblup) <- rownames(model$random$id) # # # Predict blup of reference and test individuals # blup <- ghap.predictblup(refblup, vcp = model$vcp$Estimate[1], # covmat = as.matrix(K), # errormat = model$extras$LHSi, # errorname = "id") # # # Compare predictions # plot(blup$Estimate, model$random$id$Estimate) # abline(0,1)
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