ArticleGenetics2021
Translating insights from the seed metabolome into improved prediction for lipid-composition traits in oat (Avena sativa L.).
Article in Genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Integrated metabolomic and transcriptomic analysis reveal the flavor formation mechanism during oat grain development.Food chemistry. Molecular sciences · 2026Article
- Genome-wide association studies reveal genetic control of nutritional quality, milling traits, and agronomic characteristics in oat (Avena sativa L.).The plant genome · 2025Article
- Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain.The plant genome · 2023Article
- Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa).The plant genome · 2023Article
- A Bayesian model for genomic prediction using metabolic networks.Bioinformatics advances · 2023Article
- Generalizable approaches for genomic prediction of metabolites in plants.The plant genome · 2022Article
- Selection for seed size has uneven effects on specialized metabolite abundance in oat (Avena sativa L.).G3 (Bethesda, Md.) · 2022Article
- Recent applications of metabolomics in plant breeding.Breeding science · 2022Article
- Improving Genomic Prediction for Seed Quality Traits in Oat (Avena sativa L.) Using Trait-Specific Relationship Matrices.Frontiers in genetics · 2021Article
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Authors and funding
9 authors.
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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.
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