Pernille Merete Sarup

This study uses temporal allele frequency changes to detect adaptive variants in winter wheat breeding programs, finding that selection signals identify non-neutral variants associated with grain yield and protein content.

The paper integrates genomic and metabolomic data to improve breeding value prediction for malting quality traits in barley, showing higher accuracy with the metabolomics-genomic model.

The paper uses Drosophila metabolomics to show metabolites predict organismal phenotypes better than genomic data in some traits.

This study evaluates genomic prediction for malting quality traits in barley breeding using different cross-validation methods and training population sizes, finding medium to high heritability and accuracy improvements with larger training populations.