Improving grain yield prediction in Southern US oat germplasm using genomics information and environmental covariates.

Genetic gains of oat (Avena sativa L.) grain yield have been historically low compared to other major cereal crops. The use of machine learning models to capture complex interactions and leveraging data types other than genomic information in prediction models has great potential for improving complex traits in oat breeding programs. This study assessed the performance of deep learning model for genomic prediction compared to other statistical models, examined the optimal training set size for grain yield prediction, and investigated the potential of incorporating environmental covariates for enhancing oat grain yield prediction. A total of 463 oat lines were evaluated in five environments in Southern United States, and genotyping of the lines gave 12,657 single-nucleotide polymorphism markers. Our results showed that training set sizes 200-350 could be the optimal size for our panel, indicating the possibility of reducing phenotyping costs by reducing the size of the oat panel tested. The deep learning model was less superior to genomic best linear unbiased prediction and other models for grain yield, test weight, and heading days in the different environments. Incorporating interaction effects (G × E or G × W) into the multikernel prediction models across environments improved predictive abilities for grain yield by up to 0.21 compared to the baseline models. This reveals the potential of incorporating weather data to enhance predictive abilities in genomic prediction models. Our findings provide important information for improving genetic gains in oat breeding programs by integrating genomics and environmental information.