ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2021
Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- Multi-omics revolution to promote plant breeding efficiency.Frontiers in plant science · 2022Pooled it
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- Climate change and crop resilience: harnessing metabolomics for predicting stress tolerance.The New phytologist · 2026Review
- Sugarcane Breeding in the Genomic Era: Integrative Strategies and Emerging Technologies.Plants (Basel, Switzerland) · 2026Review
- Optimizing biomass partitioning in wheat using UAV-based hyperspectral phenomic and genomic prediction: kernel-based and machine learning approaches.Frontiers in plant science · 2026Article
- Assessment of genomic prediction capabilities of transcriptome data in a barley multi-parent RIL population.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Unlocking gene regulatory networks for crop resilience and sustainable agriculture.Nature biotechnology · 2025Review
- Integrating multi-omics and machine learning for disease resistance prediction in legumes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Metabolic marker-assisted genomic prediction improves hybrid breeding.Plant communications · 2025Article
- Integrating multi-layered biological priors to improve genomic prediction accuracy in beef cattle.Biology direct · 2024Article
- Hybrid Prediction in Horticulture Crop Breeding: Progress and Challenges.Plants (Basel, Switzerland) · 2024Review
- Tabular deep learning: a comparative study applied to multi-task genome-wide prediction.BMC bioinformatics · 2024Article
- Transcriptome-based prediction for polygenic traits in rice using different gene subsets.BMC genomics · 2024Article
- High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictions.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Enhancing winter wheat prediction with genomics, phenomics and environmental data.BMC genomics · 2024Article
- Feature engineering of environmental covariates improves plant genomic-enabled prediction.Frontiers in plant science · 2024Article
- 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
- An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture.Biology · 2023Review
Corrections and comments
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Authors and funding
16 authors at 8 institutions in 2 countries.
Funding
Abstract
key messageIntegration 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.