This function acts as a front-end for TASSEL's genomic prediction functions. This analysis method uses gBLUP (genomic BLUP) to predict phenotypes from genotypes. It proceeds by fitting a mixed model that uses kinship to capture covariance between taxa. The mixed model can calculate BLUPs for taxa that do not have phenotypes based on the phenotypes of lines with relationship information.

A phenotype dataset and a kinship matrix must be supplied as input to the method by selecting both then choosing Analysis/Genomic Selection. In addition to trait values, the phenotype dataset may also contain factors or covariates which will be used as fixed effects in the model. All taxa in the phenotype dataset can only appear once. No repeated values are allowed for a single taxon. When the analysis is run, the user is presented with the choice to run k-fold cross-validation. If cross- validation is selected, then the number of folds and the number of iterations can be entered. For each iteration and each fold within an iteration, the correlation between the observed and predicted values will be reported. If cross-validation is not selected, then the original observations, predicted values and PEVs (prediction error variance) will be reported for all taxa in the dataset.

When k-fold cross-validation is performed, only taxa with phenotypes and rows in the kinship matrix are used. That set of taxa are divided into k subsets of equal size. Each subset in turn is used as the validation set. Phenotypes of the individuals in the validation are set to 0 then predicted using the remaining individuals as the training set. The correlation (r) of the observed values and predicted values is calculated for the validation set and reported. The mean and standard deviation of the mean of the r's are calculated for each trait and reported in the comments section of the "Accuracy" data set that is output by the analysis. In general, the results are not very sensitive to the choice of k. The number of iterations affects the standard error of the mean for the accuracy estimates. The defaults of k = 5 and iterations = 20 will be adequate for most users.

genomicPrediction(tasPhenoObj, kinship, doCV = FALSE, kFolds, nIter)

Arguments

tasPhenoObj

An object of class TasselGenotypePenotype that contains a phenotype object.

kinship

A TASSEL kinship object of class TasselDistanceMatrix.

doCV

Do you want to perform k-fold cross-validation? Defaults to FALSE.

kFolds

Number of folds to be entered.

nIter

Number of iterations to be ran.

Value

Returns a DataFrame-based data frame