The objectives were to investigate prediction of malting quality (MQ) phenotypes in different locations using information from metabolomic spectra, and compare the prediction ability using different models and different sizes of training population (TP).
A total of 2,667 plots of 564 malting spring barley lines from three years and two locations were included. Five MQ traits were measured in wort produced from each individual plot. Metabolomic features (MFs) used were 24,018 NMR intensities measured on each wort sample. Models involved in the statistical analyses were a metabolomic best linear unbiased prediction (MBLUP) model and a partial least squares regression (PLSR) model. Predictive ability within location and across locations were compared using cross-validation methods.
The proportion of variance in MQ traits that could be explained by effects of MFs was above 0.9 for all traits. The prediction accuracy increased with increasing TP size but when the TP size reached 1,000, the rate of increase was negligible. The number of components considered in the PLSR models can affect the performance of PLSR models and 20 components were optimal. The accuracy of individual plots and line means using leave-one-line-out cross-validation ranged from 0.722 to 0.865 and using leave-one-location-out cross-validation ranged from 0.517 to 0.817.
In conclusion, it is possible to carry out metabolomic prediction of MQ traits using MFs, the prediction accuracy is 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.