Abstract

Fusarium head blight (FHB) is an economically and environmentally concerning disease of wheat (Triticum aestivum L). A two-pronged approach of marker assisted selection (MAS) coupled with genomic selection (GS) has been suggested when breeding for FHB resistance. An historical dataset comprised of entries in the Southern Uniform Winter Wheat Scab Nursery (SUWWSN) from 2011-2021 was partitioned and used in genomic prediction. Two traits were curated from 2011-2021 in the SUWWSN: percent Fusarium damaged kernels (FDK) and Deoxynivalenol (DON) content. Heritability was estimated for each trait-by-environment combination. A consistent set of check lines was drawn from each year in the SUWWSN, and K-means clustering was performed across environments to assign environments into clusters. Two clusters were identified for FDK and three for DON. Cross-validation on SUWWSN data from 2011-2019 indicated no outperforming training population in comparison to the combined dataset. Forward validation for FDK on the SUWWSN 2020 and 2021 data indicated a predictive accuracy r {approx} 0.58 and r {approx} 0.53, respectively. Forward validation for DON indicated a predictive accuracy of r {approx} 0.57 and r {approx} 0.45, respectively. Forward validation using environments in cluster one for FDK indicated a predictive accuracy of r {approx} 0.65 and r {approx} 0.60, respectively. Forward validation using environments in cluster one for DON indicated a predictive accuracy of r {approx} 0.67 and r {approx} 0.60, respectively. These results indicated that selecting environments based on check performance may produce higher forward prediction accuracies. This work may be used as a model to create a public resource for genomic prediction of FHB resistance traits across public wheat breeding programs. CORE IDEASO_LIThe data from the Southern Uniform Winter Wheat Nursery may be used for genomic prediction. C_LIO_LICreating training populations based on like-check performance improves forward genomic predictive accuracies. C_LIO_LIFiltering out locations with low genomic, per-plot, narrow-sense heritability may improve predictive accuracies. C_LI