Generalizable approaches for genomic prediction of metabolites in plants

Authors: BRZOZOWSKI, LAUREN - Cornell University; CAMPBELL, MALACHY - Cornell University; HU, HAIXIAO - Cornell University; CAFFEE, MELANIE - South Dakota State University; GUTIERREZ, LUCIA - University Of Wisconsin; SMITH, KEVIN - University Of Minnesota; SORRELLS, MARK - Cornell University; GORE, MICHAEL - Cornell University; Jannink, Jean-Luc

Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/21/2022
Publication Date: 6/14/2022
Citation: Brzozowski, L.J., Campbell, M.T., Hu, H., Caffee, M., Gutierrez, L., Smith, K.P., Sorrells, M.E., Gore, M., Jannink, J. 2022. Generalizable approaches for genomic prediction of metabolites in plants. The Plant Genome. 15: Article e20205. https://doi.org/10.1002/tpg2.20205.
DOI: https://doi.org/10.1002/tpg2.20205

Interpretive Summary: Plant metabolites are important traits for plant breeders seeking to improve nutrition and agronomic performance yet integrating selection for plant metabolites can be expensive, especially for uncommon metabolites. As such, developing generalizable genomic selection methods based on biochemical pathway biology for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for >600 metabolites in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite genome-wide association study (mGWAS) and selected loci that encompassed metabolome-wide results or results from specific metabolite structures or biosynthetic pathways. Prediction models using information from some types of metabolites in the discovery panel improved prediction accuracy of metabolite traits in the validation panel consisting of more advanced breeding lines. No approach, however, improved prediction accuracy for all metabolites. We ranked model performance by metabolite and found that metabolites with similar polarity had consistent rankings of models. Overall, testing biological rationales for developing specific prediction models across populations contributes to developing frameworks for plant breeding for metabolite traits.

Technical Abstract: Plant metabolites are important traits for plant breeders seeking to improve nutrition and agronomic performance yet integrating selection for metabolomic traits can be limited by phenotyping expense and degree of genetic characterization, especially of uncommon metabolites. As such, developing generalizable genomic selection methods based on biochemical pathway biology for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for >600 metabolites measured by gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–mass spectrometry (LC-MS) in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite genome-wide association study (mGWAS) and selected loci to use in multikernel models that encompassed metabolome-wide mGWAS results or mGWAS from specific metabolite structures or biosynthetic pathways. Metabolite kernels developed from LC-MS metabolites in the discovery panel improved prediction accuracy of LC-MS metabolite traits in the validation panel consisting of more advanced breeding lines. No approach, however, improved prediction accuracy for GC-MS metabolites. We ranked model performance by metabolite and found that metabolites with similar polarity had consistent rankings of models. Overall, testing biological rationales for developing kernels for genomic prediction across populations contributes to developing frameworks for plant breeding for metabolite traits.