Abstract
KEY MESSAGE: Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.
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Claims 11
Claim · model-written summary
In a single environment, transcriptomic BLUP (T) exceeded GBLUP prediction accuracy for 5 traits.
- Taxon
- oat
Avena sativat0 - Treatment
- transcriptomic BLUP (T)e0
- Outcome
- prediction accuracye0
- Direction
- greatere0
- Value
- 5e0
- Comparator
- GBLUPe0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs.”
t0“oat”
Claim · model-written summary
In a single environment, metabolomic BLUP (M) exceeded GBLUP prediction accuracy for 10 traits.
- Taxon
- oatAvena sativat0
- Treatment
- metabolomic BLUP (M)e0
- Outcome
- prediction accuracye0
- Direction
- greatere0
- Value
- 10e0
- Comparator
- GBLUPe0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs.”
t0“oat”
Claim · model-written summary
In a single environment, G + T exceeded GBLUP prediction accuracy for 11 traits.
- Taxon
- oatAvena sativat0
- Treatment
- G + Te0
- Outcome
- prediction accuracye0
- Direction
- greatere0
- Value
- 11e0
- Comparator
- GBLUPe0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs.”
t0“oat”
Claim · model-written summary
In a single environment, G + M exceeded GBLUP prediction accuracy for 17 traits.
- Taxon
- oatAvena sativat0
- Treatment
- G + Me0
- Outcome
- prediction accuracye0
- Direction
- greatere0
- Value
- 17e0
- Comparator
- GBLUPe0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs.”
t0“oat”
Claim · model-written summary
In a single environment, G + T + M exceeded GBLUP prediction accuracy for 17 traits.
- Taxon
- oatAvena sativat0
- Treatment
- G + T + Me0
- Outcome
- prediction accuracye0
- Direction
- greatere0
- Value
- 17e0
- Comparator
- GBLUPe0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs.”
t0“oat”
Claim · model-written summary
Metabolites generally outperformed transcripts when combined with SNPs.
- Taxon
- oatAvena sativat0
- Outcome
- prediction accuracye0
- Direction
- bettere0
- Qualifier
- when combined with SNPse0
- Comparator
- transcriptse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs.”
t0“oat”
Claim · model-written summary
Multi-environment multi-trait omics models outperformed counterpart multi-trait GBLUP models.
- Taxon
- oatAvena sativat0
- Outcome
- multi-trait models with omics data outperformede0
- Direction
- outperformede0
- Comparator
- counterpart multi-trait GBLUP modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model.”
t0“oat”
Claim · model-written summary
Multi-environment multi-trait omics models outperformed single-environment omics models.
- Taxon
- oatAvena sativat0
- Outcome
- multi-trait models with omics data outperformede0
- Direction
- outperformede0
- Comparator
- single-environment omics modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model.”
t0“oat”
Claim · model-written summary
Unstructured genetic covariance modeling gave the highest prediction accuracy.
- Taxon
- oatAvena sativat0
- Outcome
- prediction accuracye0
- Direction
- higheste0
- Comparator
- unstructured covariance modele0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model.”
t0“oat”
Claim · model-written summary
Omics-based locus prioritization with a two-kernel model improved prediction in a distantly related oat population.
- Taxon
- oatAvena sativat0
- Outcome
- genomic predictione0
- Direction
- improvee0
- Qualifier
- distantly related populatione0
- Comparator
- one population with omics datae0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance.”
t0“oat”
Claim · model-written summary
Authors propose the two-kernel model to improve prediction accuracy for traits with known genetic architecture.
- Taxon
- oatAvena sativat0
- Outcome
- improve prediction accuracye0
- Qualifier
- prior knowledge of genetic architecturee0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.”
t0“oat”