Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials

Implementing genomic-based prediction models in genomic selection requires an understanding of the measures for evaluating prediction accuracy from different models and methods using multi-trait data. In this study, we compared prediction accuracy using six large multi-trait wheat data sets (quality and grain yield). The data were used to predict 1 year (testing) from the previous year (training) to assess prediction accuracy using four different prediction models. The results indicated that the conventional Pearson's correlation between observed and predicted values underestimated the true correlation value, whereas the corrected Pearson's correlation calculated by fitting a bivariate model was higher than the division of the Pearson's correlation by the squared root of the heritability across traits, by 2.53-11.46%. Across the datasets, the corrected Pearson's correlation was higher than the uncorrected by 5.80-14.01%. Overall, we found that for grain yield the prediction performance was highest using a multi-trait compared to a single-trait model. The higher the absolute genetic correlation between traits the greater the benefits of multi-trait models for increasing the genomic-enabled prediction accuracy of traits.

Automated summary

Implementing genomic-based prediction models in genomic selection involves understanding how to evaluate prediction accuracy from different models and methods using multi-trait data. This study compared prediction accuracy using six large multi-trait wheat datasets and found that a corrected Pearson's correlation method was more accurate than the traditional method. For grain yield, using a multi-trait model yielded higher prediction performance compared to a single-trait model, with the benefits increasing as genetic correlations between traits strengthen.