ArticleFrontiers in plant science2026
Enhancing genomic prediction of wheat resistance to fusarium head blight through integration of passive resistance traits.
Article in Frontiers in plant science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
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3 authors.
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Abstract
Fusarium head blight (FHB) is one of the most devastating wheat diseases worldwide. Wheat resistance to FHB is controlled by multiple small-effect genes involved in active and passive resistance mechanisms. Complex resistance can be successfully managed using genomic selection. However, the efficiency of wheat genomic selection for FHB is restricted by prediction accuracy. In this study, we investigated the contribution of passive resistance traits in genomic prediction to FHB. A panel of 335 spring wheat genotypes were phenotyped for FHB severity and eight key traits associated with passive resistance in 2022, 2023, and 2025. Genome-wide association analysis (GWAS) was performed to identify MTAs significantly associated with FHB resistance and passive resistance traits. Genomic prediction was performed using Reproducing Kernel Hilbert Space regression (RKHS), Ridge Regression Best Linear Unbiased Prediction (rrBLUP), and Random Forest regression (RF) models. The contribution of passive resistance was evaluated by performing genomic prediction on separate sets of SNPs that were significantly associated with FHB resistance and morpho-phenological traits in GWAS. Genomic prediction based on set 1 from cross-validated GWAS outperformed the prediction of the entire set of markers (18,417 SNPs) in all models. Comparing the genomic prediction performance on the entire set of markers with that based on set 1 from cross-validated GWAS and fixed covariates (days to heading and anther extrusion), an absolute increase in accuracy of 0.10 (0.60 vs. 0.70), and 0.10 (0.60 vs. 0.70) was observed using the RKHS, and rrBLUP models, respectively. In RF model, DH and AE were included as additional predictors and the increase was 0.11 (0.53 vs. 0.64). The results indicate that the genomic prediction of wheat resistance to FHB can be improved integrating passive resistance traits into prediction models.
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