Genomic prediction of seasonal forage yield in perennial ryegrass

BackgroundGenomic selection has the potential to accelerate genetic gain in perennial ryegrass breeding, provided complex traits such as forage yield can be predicted with sufficient accuracy.MethodsIn this study, we compared modelling approaches and feature selection strategies to evaluate the accuracy of genomic prediction models for seasonal forage yield production.ResultsOverall, model selection had limited impact on predictive ability when using the full data set. For a baseline genomic best linear unbiased prediction model, the highest mean predictive accuracy was obtained for spring grazing (0.78), summer grazing (0.62) and second cut silage (0.56). In terms of feature selection strategies, using uncorrelated single-nucleotide polymorphisms (SNPs) had no impact on predictive ability, allowing for a potential decrease of the data set dimensions. With a genome-wide association study, we found a significant SNP marker for spring grazing, located in the genic region annotated as coding for an enzyme responsible for fucosylation of xyloglucans-major components of the plant cell wall. We also presented an approach to increase interpretability of genomic prediction models with the use of Gene Ontology enrichment analysis.ConclusionsApproaches for feature selection will be relevant in development of low-cost genotyping platforms in support of routine and cost-effective implementation of genomic selection. This study evaluated the accuracy of genomic prediction models for seasonal forage yield production in perennial ryegrass. Using uncorrelated single-nucleotide polymorphism (SNPs) improved model accuracy, while selecting SNPs based on their concordance across different experiments reduced it. These findings could help develop low-cost genotyping platforms for routine and cost-effective implementation of genomic selection. image