Poster # 516

Julio Sellani ¹, Stephen Harrison ¹, Trey Paul Price ¹, Richard Boyles ², Noah DeWitt ¹
1. Louisiana State University, Baton Rouge, LA
2. Clemson University, Florence, SC

Sellani, Julio

jsellani@agcenter.lsu.edu

Fusarium Head Blight (FHB) remains a major barrier to wheat productivity in the Southern U.S., reducing yield and grain quality through contamination with deoxynivalenol (DON), a harmful mycotoxin. In the face of rising disease pressure and limited breeding resources, we explored how early-generation genomic prediction can be reimagined to improve efficiency, scalability, and resilience. A soft red winter wheat population of 450 genotypes (90 families × 5 siblings) was evaluated under artificial FHB inoculation at two Louisiana locations in 2023–2024. Traits assessed included FHB severity, Fusarium-damaged kernels (FDK), DON concentration, plant height, stripe rust, and heading date.Using GBLUP and a leave-one-family-out cross-validation strategy, we compared the predictive performance of individual genotyping, family means, and mid-parent marker imputation. Results revealed minimal accuracy loss when replacing individual genotyping with low-cost, family-based methods. GWAS identified key resistance loci including Fhb1, 1B (Jamestown), and 4A, confirming genetic targets for selection. Our results challenge the assumption that genotyping every plant is necessary at early stages, offering an adaptive, scalable approach for genomic selection in resource-limited programs. This work contributes to the design of faster, smarter, and more cost-effective breeding pipelines, enhancing FHB resistance while reducing fungicide dependence. In a climate of increasing disease risk and tightening budgets, this innovation supports more resilient agriculture for growers and breeding programs alike.

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