example split split plot asreml heritabilities

The regression of phenotypes on breeding values was close to 1 for all methods, indicating little bias, except for GBLUP and adjusted phenotypes (regression = 0.78). No clear differences in accuracy were observed between GBLUP and BayesR. However, for some traits with smaller reference population size, there was no increase in accuracy or it was small. Results and conclusions: Genomic methods increased the accuracy of predicted breeding values to on average 0.2 across traits (range 0.07 to 0.31), compared to an average accuracy of 0.09 for pedigree-based BLUP. Accuracies and regression coefficients were calculated using either phenotypes or adjusted phenotypes as observed variables. Cross-validation of predictions across sire families was used to evaluate the accuracy of predictions based on the correlation of predicted and observed values and the regression of observed on predicted values was used to evaluate bias of methods. Methods: Prediction of breeding values by best linear unbiased prediction (BLUP) based on pedigree information was compared to prediction based on genomic BLUP (GBLUP) and a Bayesian prediction method (BayesR). Carcass and novel meat quality traits were predicted in a multi-breed sheep population that included Merino, Border Leicester, Polled Dorset and White Suffolk sheep and their crosses.

This is especially useful for meat quality traits in sheep.

Accuracy of pedigree and genomic predictions of carcass and novel meat quality traits in multi-breed sheep data assessed by cross-validationīackground: Genomic predictions can be applied early in life without impacting selection candidates.

Example split split plot asreml heritabilities