Genomic and hyperspectral imaging-based prediction blending enables selection for reduced deoxynivalenol content in wheat grains.

Breeding for low deoxynivalenol (DON) mycotoxin content in wheat is challenging due to the complexity of the trait and phenotyping limitations. Since phenomic prediction relies on non-additive effects and genomic prediction on additive effects, their complementarity can improve selection accuracy. In this study DON-infected wheat kernels were imaged using a hyperspectral camera to generate reflectance values across the spectrum of visible and near infrared light that were used in phenomic predictions. Five Bayesian generalized linear regression models, and two machine learning models were trained using phenomic and genomic predictions from advanced soft winter wheat breeding lines evaluated in 2021 and 2022. Across all training sets and models, phenomic predictions using wavebands in the visible light spectrum (400-700 nm) had higher predictive ability than genomic predictions or phenomic predictions using the full waveband range (400-1000 nm). Forward prediction using 2021 trial, 2022 trial, and combined trials as the training set was performed using model blending on two sets of F4:5 selection candidates evaluated independently in 2022 and 2023. The phenotypic and genetic correlations, as well as indirect selection accuracies, of the model averages of phenomic predictions and combined phenomic and genomic predictions were higher than genomic predictions alone. Accuracies depended on the combination of training set and selection candidates. Unsupervised K-Means clustering using the blended predicted values partitioned selection candidates into two groups with high and low mean observed DON content. This study demonstrates the potential of hyperspectral imaging-based phenomic prediction to complement genomic prediction and highlights considerations for prediction-based selection of low DON in wheat.