Integration of machine learning and genome-wide association study to explore the genomic prediction accuracy of agronomic trait in oats (Avena sativa L.).

Machine learning (ML) has garnered significant attention for its potential to enhance the accuracy of genomic predictions (GPs) in various economic crops with the use of complete genomic information. Genome-wide association studies (GWAS) are widely used to pinpoint trait-related causal variant loci in genomes. However, the simultaneous integration of both methods for crop genome prediction necessitates further research. In this study, we integrated ML and GWAS to assess the efficiency of GP for seven key agronomic traits in 195 oat (Avena sativa) cultivars from major oat-growing regions around the world. A total of 94 trait-associated single nucleotide polymorphisms were identified through the GWAS study. GP studies were conducted using the classical model genomic best linear unbiased prediction (GBLUP) and six ML models. GBLUP performed poorly in predicting all traits except flag leaf width, while none of the ML models consistently provided the best prediction accuracy across all traits. The prediction accuracy of the GWAS-derived markers was better than that of the use of genome-wide markers, and plant height had the highest prediction rate at 100 GWAS-derived markers, and the rest of the traits for which more markers were required. These results play an important role in advancing the use of GP in small oat breeding programs by optimizing the prediction rate of GP and reducing the number of markers, confirming that high prediction rates can be achieved with smaller datasets.