Generalizable approaches for genomic prediction of metabolites in plants

Abstract

Plant metabolites are important for plant breeders to improve nutrition and agronomic performance, yet integrating selection for metabolomic traits is limited by phenotyping expense and limited genetic characterization, especially of uncommon metabolites. As such, developing biologically-based and generalizable genomic selection methods for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for more than 600 metabolites measured by GC-MS and LC-MS in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite GWAS (mGWAS) and selected loci to use in multi-kernel 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 tested if similar metabolites had consistent model ranks and found that, while different metrics of ‘similarity’ had different results, using annotation-free methods to group metabolites led to consistent within-group model rankings. Overall, testing biological rationales for developing kernels for genomic prediction across populations, contributes to developing frameworks for plant breeding for metabolite traits.

Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa)

“…As the biosynthetic process of fatty acids is well-conserved across plant species (Li-Beisson et al, 2013), these results are promising for employing similar methods in other grain and seed crops. This work complements the growing number of examples of genomic prediction of agronomic, quality and metabolite traits in oat (Brzozowski et al, 2022;Haikka, Knürr et al, 2020;Mellers et al, 2020;Rio et al, 2021), and provides a foundation for incorporating flavor (Günther-Jordanland et al, 2020;Lapveteläinen & Rannikko, 2000;Lapveteläinen et al, 2001) and aroma (Dach & Schieberle, 2021;Schuh & Schieberle, 2005) traits in a genomics-enabled oat breeding program. Broadly, developing effective genomic prediction methods for nutritional traits within families will contribute to efficient plant breeding for more nutritious staple crops.…”

Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain

“…The latter of which, however, resulted in lower predictive abilities relative to genome-wide markers for tocochromanol levels in fresh sweet corn kernels (Baseggio et al, 2019). In contrast to a single GRM approach, multikernel models (Speed & Balding, 2014) are a promising yet unexplored prediction approach to account for the large-effect loci associated with tocochromanol grain phenotypes, as separating genetic markers into functional (causal or potentially causal genes) and nonfunctional sets through the calculation of multiple GRMs has improved predictions for plant metabolic phenotypes (Turner-Hissong et al, 2020;Campbell et al, 2021aCampbell et al, , 2121bBrzozowski et al, 2022).…”

Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain

“…The latter of which, however, resulted in lower predictive abilities relative to genome-wide markers for tocochromanol levels in fresh sweet corn kernels (Baseggio et al, 2019). In contrast to a single GRM approach, multikernel models (Speed & Balding, 2014) are a promising yet unexplored prediction approach to account for the large-effect loci associated with tocochromanol grain phenotypes, as separating genetic markers into functional (causal or potentially causal genes) and nonfunctional sets through the calculation of multiple GRMs has improved predictions for plant metabolic phenotypes (Turner-Hissong et al, 2020; Campbell et al, 2021a; b; Brzozowski et al, 2022).…”