Translating insights from the seed metabolome into improved prediction for lipid-composition traits in oat (Avena sativa L.)

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

Submitted to: Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/10/2020
Publication Date: 2/3/2021
Citation: Campbell, M.T., Hu, H., Yeats, T.H., Caffe-Treml, M., Gutierrez, L., Smith, K.P., Sorrells, M.E., Gore, M.A., Jannink, J. 2021. Translating insights from the seed metabolome into improved prediction for lipid-composition traits in oat (Avena sativa L.). Genetics. 217(3):iyaa043. https://doi.org/10.1093/genetics/iyaa043.
DOI: https://doi.org/10.1093/genetics/iyaa043

Interpretive Summary: Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Little is known regarding the genetic controllers of variation for these compounds in oat seed. We characterized natural variation in the mature seed of 367 diverse lines using a method called "metabolomics", that quantifies over 1,000 distinct chemical compounds in the oat seed without necessarily identifying them. We used this information to improve prediction for seed quality traits. We decomposed the information into one hundred factors that represent correlated compounds, of which 21% were associated with lipid metabolism. We showed that these factors tend to be affected by many genes. Nonetheless, we found significant gene associations for 23% of the factors. These associations were used to improve prediction of seed lipid and protein traits in two independent datasets. Predictions for 8 of 12 traits were significantly improved compared to a standard model. This study provides insight into variation in the oat seed metabolome and resources for breeders to improve selection for health-promoting seed quality traits. More broadly, the factorization approach to distill high-dimensional metabolic data to a set of biologically meaningful factors improved breeding decisions.

Technical Abstract: Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Little is known regarding the genetic controllers of variation for these compounds in oat seed. We characterized natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leveraged this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for 8 of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional “omics” data to a set of biologically meaningful variables and translate inferences on these data into improved breeding decisions.