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

Abstract

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 used 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 2 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 thins percentage.