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

Malachy T. Campbell, Haixiao Hu, Trevor H. Yeats, Melanie Caffe-Treml, Lucía Gutiérrez, Kevin P. Smith, Mark E. Sorrells, Michael A. Gore, Jean Luc Jannink

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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.

Original languageEnglish (US)
Article numberiyaa043
Issue number3
StatePublished - Mar 31 2021

Bibliographical note

Funding Information:
Mention of a trademark or proprietary product does not constitute a guarantee or warranty of the product by the USDA and does not imply its approval to the exclusion of other products that may also be suitable. The USDA is an equal opportunity provider and employer. Metabolomic data were generated by H.H. and T.H.Y.; analyses were performed by M.T.C. under the guidance of M.A.G. and J.L.J.; K.P.S. and T.H.Y. generated data used for validation; M.T.C. wrote the manuscript with guidance from J.L.J. and M.A.G.; comments were provided by H.H., L.G., M.E.S., M.A.G., and J.L.J.; this study was supported by grants secured by K.P.S., L.G., M.C.T., M.E.S., M.A.G., and J.L.J.; all authors read and approved the manuscript. Funding for this research was provided by United States Department of Agriculture - National Institute of Food and Agriculture - Agriculture and Food Research Initiative (USDANIFA-AFRI) grant (2017-67007-26502). The USDA is an equal opportunity provider and employer.

Publisher Copyright:
© The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America.


  • GWAS
  • GenPred
  • factor analysis
  • genomic prediction
  • metabolomics
  • shared data resource


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