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.

Genomic regions influencing the hyperspectral phenome of deoxynivalenol infected wheat.

The quantitative nature of fusarium head blight (FHB) resistance requires further exploration of the wheat genome to identify regions conferring resistance. In this study, we explored the application of hyperspectral imaging of Fusarium-infected wheat kernels and identified regions of the wheat genome contributing significantly to the accumulation of Deoxynivalenol (DON) mycotoxin. Strong correlations were identified between hyperspectral reflectance values for 204 wavebands in the 397-673 nm range and DON mycotoxin.