Melanie Caffe

The paper develops genomic prediction methods for plant metabolites using metabolite GWAS and kernel models, improving accuracy for LC-MS metabolites but not GC-MS.

This study evaluates multi-omics prediction models for oat traits across environments and populations, showing improved accuracy over genomic prediction with SNP data.

This study uses metabolomics and genomic approaches to improve prediction of healthful compounds in oat seeds by identifying latent factors associated with lipid metabolism.