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Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations

Creator: Haixiao Hu
Creator: Campbell, M.T.
Creator: Yeats, T.H.
Creator: Xuying Zheng
Creator: Runcie, D.E.
Creator: Covarrubias, E.
Creator: Broeckling, C.
Creator: Linxing Yao
Creator: Caffe-Treml, M.
Creator: Gutiérrez, L.
Creator: Smith, K.P.
Creator: Tanaka, J.
Creator: Hoekenga, O.A.
Creator: Sorrells, M.E.
Creator: Gore, M.A.
Creator: Jannink, J.L.
Year: 2021
Language: English
Publisher: Springer
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
Type: Article
Place of Publication: Berlin (Germany)
Pages: 4043-4054
Issue: 12
Volume: 134
DOI: 10.1007/s00122-021-03946-4
Description: 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. Abstract: 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.
Agrovoc: DATA
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ISSN: 0040-5752
Journal: Theoretical and Applied Genetics

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  • Genetic Resources
    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP

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