Unraveling genomic regions and optimizing genomic predictions for grain yield and yield-related traits in oats (Avena sativa L.)

Authors: OLVEIRA, GUILHERME - South Dakota State University; BAZZER, SUMANDEEP - South Dakota State University; Fiedler, Jason; Nandety, Raja Sekhar; Jannink, Jean Luc; CAFFE, MELANIE - South Dakota State University

Submitted to: Euphytica
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/25/2025
Publication Date: 5/16/2025
Citation: Olveira, G., Bazzer, S., Fiedler, J.D., Nandety, R., Jannink, J., Caffe, M. 2025. Unraveling genomic regions and optimizing genomic predictions for grain yield and yield-related traits in oats (Avena sativa L.). Euphytica. 221.Article 89. https://doi.org/10.1007/s10681-025-03526-3.
DOI: https://doi.org/10.1007/s10681-025-03526-3

Interpretive Summary: Oat is an important cereal grain crop that provides healthy calories for human nutrition and is a fantastic part of a producer's crop rotation due to its positive impact on soil health. However, the development of higher-yielding varieties is challenging in oat compared to other small grains. To overcome this, we conducted genetic analysis on breeding lines from South Dakota State University that were evaluated over six years in field trials. We identified 38 durable gene regions that influenced important traits such as lodging, height, disease resistance and yield. Going a step further, we developed a statistical model to predict performance of these traits from all the genome data available and, interestingly, found that optimal models only contained these durable gene regions found in the previous analysis. These results are useful to breeders that are implementing molecular breeding techniques to increase genetic gain in their programs and accelerate the release of elite cultivars.

Technical Abstract: Selecting for high grain yield is an intrinsic target of most crop breeding programs. However, the genetic complexity of the trait and its strong interactions with environmental factors pose significant challenges, hindering quick and efficient genetic improvement. This scenario is especially evident in oats (Avena sativa L.), as genetic gain for grain yield has historically been low when compared to major crops. The objective of this study was to leverage data collected in an oat breeding program, identifying molecular markers and genomic regions associated with grain yield and related traits and exploring if this genetic information could enhance genomic prediction models for oat grain yield. Association mapping was conducted to investigate genetic associations for grain yield, plant height, heading date, lodging, snapback, and crown rust severity in six oat breeding populations evaluated in multiple environments in South Dakota over six different years. Thirty-eight genomic regions associated with the traits were considered most promising because of their significance in multiple populations and/or environments, suggesting their potential as valuable genetic sources. When specific markers from the significant genomic regions were considered as fixed effects in linear and additive genomic prediction models, prediction accuracies for grain yield did not improve. However, using only the markers identified through association mapping, instead of the whole marker set, for model development improved prediction accuracy from approximately 4 to 6%. These results provide valuable information for increasing genetic gains for grain yield in oats and other crops.