Implementing multi-trait genomic selection to improve grain milling quality in oats (Avena sativa L.)

Authors: DHAKAL, A - University Of Illinois; POLAND, J - King Abdullah University Of Science And Technology; ADHIKARI, L - King Abdullah University Of Science And Technology; FARYNA, E - Kansas State University; Fiedler, Jason; RUTKOSKI, J,E - University Of Illinois; ARBELAEZ, J,D - University Of Illinois

Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/8/2024
Publication Date: N/A
Citation: N/A

Interpretive Summary: Oats provide unique nutritional benefits and contribute to sustainable agricultural systems. Developing high-value oat varieties with superior quality is crucial to satisfy the demand for oat-based food products. To overcome the limitation of conventional selection, modern breeding tools have been developed that take advantage of genotyping information to predict line performance in the field. In recent years, these tools have also been advanced to leverage trait-correlations to increase the prediction accuracy. In this study, we evaluated several variations of these tools in 558 breeding lines from the University of Illinois Oat Breeding Program over two years. We found that for only some traits, these new algorithms were better than the original for predicting performance. This information is critical for oat breeding programs to make decisions on deployment of these new tools as part of their pipeline to generate new elite varieties.

Technical Abstract: Oats (Avena sativa L.) provide unique nutritional benefits and contribute to sustainable agricultural systems. Breeding high-value oat varieties that meet milling industry standards is crucial for satisfying the demand for oat-based food products. Test weight, thins, and groat percentage are primary traits that define oat milling quality and the final price of food-grade oats. Conventional selection for milling quality is costly and burdensome. Multi-trait genomic selection (MTGS) combines information from genome-wide markers and secondary traits genetically correlated with primary traits to predict breeding values of primary traits on candidate breeding lines. MTGS can improve prediction accuracy and significantly accelerate the rate of genetic gain. In this study, we evaluated different MTGS models that use morphometric grain traits to improve prediction accuracy for primary grain quality traits within the constraints of a breeding program. We evaluated 558 breeding lines from the University of Illinois Oat Breeding Program across two years for primary milling traits, test weight, thins, and groat percentage, and secondary grain morphometric traits derived from kernel and groat images. Kernel morphometric traits were genetically correlated with test weight and thins percentage but were uncorrelated with groat percentage. For test weight and thins percentage, the MTGS model that included the kernel morphometric traits in both training and candidate sets outperformed single-trait models by 52% and 59%, respectively. In contrast, MTGS models for groat percentage were not significantly better than the single-trait model. We found that incorporating kernel morphometric traits can improve the genomic selection for test weight and thin percentage.