Metabolomic spectra for phenotypic prediction of malting quality in spring barley

We investigated prediction of malting quality (MQ) phenotypes in different locations using metabolomic spectra, and compared the prediction ability of different models, and training population (TP) sizes. Data of five MQ traits was measured on 2667 individual plots of 564 malting spring barley lines from three years and two locations. A total of 24,018 metabolomic features (MFs) were measured on each wort sample. Two statistical models were used, a metabolomic best linear unbiased prediction (MBLUP) and a partial least squares regression (PLSR). Predictive ability within location and across locations were compared using cross-validation methods. For all traits, more than 90% of the total variance in MQ traits could be explained by MFs. The prediction accuracy increased with increasing TP size and stabilized when the TP size reached 1000. The optimal number of components considered in the PLSR models was 20. The accuracy using leave-one-line-out cross-validation ranged from 0.722 to 0.865 and using leave-one-location-out cross-validation from 0.517 to 0.817. In conclusion, the prediction accuracy of metabolomic prediction of MQ traits using MFs was high and MBLUP is better than PLSR if the training population is larger than 100. The results have significant implications for practical barley breeding for malting quality.

Automated summary

In this study, metabolomic spectra were used to predict malting quality phenotypes in different locations, and the prediction ability of different models and training population sizes were compared. The results showed that more than 90% of the total variance in malting quality traits could be explained by metabolomic features. The prediction accuracy increased with increasing training population size and stabilized when the size reached 1000. The optimal number of components considered in the prediction models was 20. The accuracy using cross-validation ranged from 0.722 to 0.865 for leave-one-line-out and from 0.517 to 0.817 for leave-one-location-out. Therefore, metabolomic prediction of malting quality traits using metabolomic features has high accuracy, and MBLUP is better than PLSR when the training population size is larger than 100.