Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa)

Abstract

Selection for more nutritious crop plants is an important goal of plant breeding to improve food quality and contribute to human health outcomes. While there are efforts to integrate genomic prediction to accelerate breeding progress, an ongoing challenge is identifying strategies to improve accuracy when predicting within biparental populations in breeding programs. We tested multiple genomic prediction methods for 12 seed fatty acid content traits in oat (Avena sativa L.), as unsaturated fatty acids are a key nutritional trait in oat. Using two well‐characterized oat germplasm panels and other biparental families as training populations, we predicted family mean and individual values within families. Genomic prediction of family mean exceeded a mean accuracy of 0.40 and 0.80 using an unrelated and related germplasm panel, respectively, where the related germplasm panel outperformed prediction based on phenotypic means (0.54). Within family prediction accuracy was more variable: training on the related germplasm had higher accuracy than the unrelated panel (0.14–0.16 and 0.05–0.07, respectively), but variability between families was not easily predicted by parent relatedness, segregation of a locus detected by a genome‐wide association study in the panel, or other characteristics. When using other families as training populations, prediction accuracies were comparable to the related germplasm panel (0.11–0.23), and families that had half‐sib families in the training set had higher prediction accuracy than those that did not. Overall, this work provides an example of genomic prediction of family means and within biparental families for an important nutritional trait and suggests that using related germplasm panels as training populations can be effective.

The Plant Genome special section: Grain quality and nutritional genomics for breeding next‐generation crops

“…Tanaka et al (2022) worked on the same trait and provided sound data support for the hypothesis that prior major QTL contributing regions and candidate causal genes conferring biological knowledge to elevate vitamin E content improves prediction based on analysis deploying multi-trait prediction model in two panels of maize inbred lines. Next, research by Brzozowski et al (2023) on genomic selection was in oat targeting seed nutritional traits in biparental populations by testing multiple genomic prediction methods for 12 seed fatty acid content traits. The results indicated more variability for prediction accuracy within family as compared to the unrelated panel.…”