Genomic prediction of Fusarium head blight resistance in early stages using advanced breeding lines in hard winter wheat

Authors: ZHANG, JINFENG - South Dakota State University; GILL, HARSIMARDEEP - South Dakota State University; BRAR, NAVREET - South Dakota State University; HALDER, JYOTIRMOY - South Dakota State University; ALI, SHAUKAT - South Dakota State University; LIU, XIAOTIAN - South Dakota State University; Bernardo, Amy; St Amand, Paul; Bai, Guihua; TURNIPSEED, BRENT - South Dakota State University; SEHGAL, SUNISH - South Dakota State University

Submitted to: The Crop Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/22/2022
Publication Date: 3/25/2022
Citation: Zhang, J., Gill, H., Brar, N., Halder, J., Ali, S., Liu, X., Bernardo, A.E., St Amand, P.C., Bai, G., Turnipseed, B., Sehgal, S. 2022. Genomic prediction of Fusarium head blight resistance in early stages using advanced breeding lines in hard winter wheat. The Crop Journal. https://doi.org/10.1016/j.cj.2022.03.010.
DOI: https://doi.org/10.1016/j.cj.2022.03.010

Interpretive Summary: Fusarium head blight (FHB) of wheat causes significant losses in grain yield and quality worldwide. Genomic prediction models use a large number of genome-wide DNA markers to predict performance traits in candidate varieties without the need for extensive field testing. We used univariate and multivariate genomic prediction models to evaluate the prediction accuracy for different FHB traits using 476 advanced elite wheat breeding lines developed by South Dakota State University. We observed a moderate prediction accuracy, suggesting that genomic prediction models can facilitate the rejection of highly susceptible materials at an early stage in a breeding program. This can reduce the time and high costs of field testing for FHB resistance in wheat breeding programs.

Technical Abstract: Fusarium head blight (FHB), also known as scab, is a devastating fungal disease of wheat that causes significant losses in grain yield and quality. Quantitative inheritance and cumbersome phenotyping make FHB resistance a challenging trait for direct selection in wheat breeding. Genomic selection to predict FHB resistance traits has shown promise in several studies. Here, we used univariate and multivariate genomic prediction models to evaluate the prediction accuracy (PA) for different FHB traits using 476 elite and advanced breeding lines developed by South Dakota State University hard winter wheat breeding program. These breeding lines were assessed for FHB disease index (DIS), and percentage of Fusarium damaged kernels (FDK) in three FHB nurseries in 2018, 2019, and 2020 (TP18, TP19, and TP20) and were evaluated as training populations (TP) for genomic prediction (GP) of FHB traits. We observed a moderate PA using univariate models for DIS (0.39 and 0.35) and FDK (0.35 and 0.37) using TP19 and TP20, respectively, while slightly higher PA was observed (0.41 for DIS and 0.38 for FDK) when TP19 and TP20 (TP19 + 20) were combined to leverage the advantage of a large training population. Although GP with multivariate approach including plant height and days to heading as covariates did not significantly improve PA for DIS and FDK over univariate models, PA for DON increased by 20% using DIS, FDK, DTH as covariates using multi-trait model in 2020. Finally, we used TP19, TP20, and TP19 + 20 in forward prediction to calculate genomic-estimated breeding values (GEBVs) for DIS and FDK in preliminary breeding lines at an early stage of the breeding program. We observed moderate PA of up to 0.59 for DIS and 0.54 for FDK, demonstrating the promise in genomic prediction for FHB resistance in earlier stages using advanced lines. Our results suggest GP for expensive FHB traits like DON and FDK can facilitate the rejection of highly susceptible materials at an early stage in a breeding program.