Utilization of a publicly available diversity panel in genomic prediction of Fusarium head blight resistance traits in wheat

Authors: WINN, ZACHARY - Colorado State University; LYERLY, JEANETTE - North Carolina State University; Brown-Guedira, Gina; MURPHY, J. PAUL - North Carolina State University; MASON, R. ESTEN - Colorado State University

Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/27/2023
Publication Date: 9/1/2023
Citation: Winn, Z.J., Lyerly, J.H., Brown Guedira, G.L., Murphy, J., Mason, R. 2023. Utilization of a publicly available diversity panel in genomic prediction of Fusarium head blight resistance traits in wheat. The Plant Genome. 16(3):e20353. https://doi.org/10.1002/tpg2.20353.
DOI: https://doi.org/10.1002/tpg2.20353

Interpretive Summary: Fusarium head blight (FHB) is an economically and environmentally concerning disease of wheat since the pathogen produces a mycotoxin that is detrimental to human health. Efforts by plant breeders to develop resistant cultivars are complicated by the environmental effects on disease level and complex inheritance for resistance. We designed a study for utilizing a genomic prediction approach for selecting the best lines. We combined data from field evaluation of advanced lines developed by public breeding programs for which data on thousands of DNA variants was generated. Our analyses indicate that the accuracy of the genomic selection approach can in improved by grouping environments based on disease levels of check varieties. This work may be used as a model for utilizing public resources for genomic prediction of FHB resistance traits across public wheat breeding programs.

Technical 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 coupled with genomic selection has been suggested when breeding for FHB resistance. A historical dataset comprised of entries in the Southern Uniform Winter Wheat Scab Nursery (SUWWSN) from 2011 to 2021 was partitioned and used in genomic prediction. Two traits were curated from 2011 to 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 as FDK and three for DON. Cross-validation on SUWWSN data from 2011 to 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 = 0.58 and r = 0.53, respectively. Forward validation for DON indicated a predictive accuracy of r = 0.57 and r = 0.45, respectively. Forward validation using environments in cluster one for FDK indicated a predictive accuracy of r = 0.65 and r = 0.60, respectively. Forward validation using environments in cluster one for DON indicated a predictive accuracy of r = 0.67 and r = 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 for utilizing public resources for genomic prediction of FHB resistance traits across public wheat breeding programs.