Genome-wide association study and genomic prediction of oat crown rust resistance in the southern US elite oat (Avena sativa L.) germplasm

Authors: ACHARYA, JANAM - University Of Florida; BABAR, MD - University Of Florida; Esvelt Klos, Kathy; KHAN, NAEEM - University Of Florida; HARRISON, STEPHEN - Louisiana State University; IBRAHIM, AMIR - Texas A&M University; MURPHY, PAUL - North Carolina State University; Fiedler, Jason

Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/3/2026
Publication Date: N/A
Citation: N/A

Interpretive Summary: Oat (Avena sativa L.) is an important cereal crop grown for food, feed, and forage in the United States, including in the southern region. One of the major challenges in oat production is crown rust, a fungal disease that can cause significant yield losses. To better understand the genetic basis of resistance to crown rust, researchers evaluated 234 winter and facultative oat lines developed by southern U.S. breeding programs across five locations known for frequent disease outbreaks. Using DNA-based markers, the study identified specific regions in the oat genome that contribute to resistance against crown rust. Some overlapped with previously known resistance areas, while others were unique to this study. These regions also contained genes involved in plant immune responses. The study also tested whether computer models could accurately predict which oat lines would perform well against crown rust, even without field testing. Among the tested models, simpler statistical models like BayesA and RRBLUP performed better than more complex machine learning methods. This research provides valuable genetic information and prediction tools that can help breeders develop oat varieties with improved resistance to crown rust more efficiently and with greater confidence.

Technical Abstract: Crown rust (CR), caused by Puccinia coronata f. sp. avenae, is a major constraint to oat production in the southern United States. This study aimed to dissect the genetic architecture of CR resistance and evaluate the potential of genomic prediction in a panel of 234 diverse winter and facultative oat lines adapted to the southern US. Using multi-environment phenotyping across five crown rust-prone environments and genotyping with 8,234 high-quality SNP markers, we performed a genome-wide association study (GWAS) and genomic prediction analyses. GWAS identified 13 significant loci associated with crown rust resistance. Nine of the loci co-localized with known QTLs or harbored candidate resistance genes such as LRR, NBS-LRR, and serine/threonine kinases. A stable marker on chromosome 3D was consistently detected in multiple environments (Baton Rouge, LA in 2016, Castroville, TX in 2016 and Winnsboro, LA in 2017) and in the combined analysis, suggesting stability across environments. Additional loci were identified on chromosome 4C (Citra, FL and Quincy, FL in 2017) and on chromosome 2A (Winnsboro, LA in 2017 and in the combined analysis). These may be useful for marker-assisted selection. Genomic prediction models showed high predictive abilities, with parametric models, particularly BayesA and RRBLUP, performing well across multiple cross-validation schemes. Random forest models showed comparable performance, and gradient boosting had noticeably lower predictive ability. These results underscore the effectiveness of using GWAS and genomic prediction to enhance breeding for durable crown rust resistance in oat and provide a genomic framework for developing resilient cultivars suited to southern U.S. growing conditions.