Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations
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.
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Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, GENOMICS, AGRONOMIC CHARACTERS, GENETIC MARKERS, DATA, |
Online Access: | https://hdl.handle.net/10883/21704 |
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dig-cimmyt-10883-217042022-10-03T19:58:33Z Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations Haixiao Hu Campbell, M.T. Yeats, T.H. Xuying Zheng Runcie, D.E. Covarrubias-Pazaran, G. Broeckling, C. Linxing Yao Caffe-Treml, M. Gutiérrez, L. Smith, K.P. Tanaka, J. Hoekenga, O.A. Sorrells, M.E. Gore, M.A. Jannink, J.L. AGRICULTURAL SCIENCES AND BIOTECHNOLOGY GENOMICS AGRONOMIC CHARACTERS GENETIC MARKERS DATA 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. 4043-4054 2021-10-27T00:10:21Z 2021-10-27T00:10:21Z 2021 Article Published Version https://hdl.handle.net/10883/21704 10.1007/s00122-021-03946-4 English https://github.com/hh622/Oat_Multi-omics_Prediction https://link.springer.com/article/10.1007%2Fs00122-021-03946-4#Sec21 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 CIMMYT-Knowledge-Center@cgiar.org 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 Open Access Berlin (Germany) Springer 12 134 0040-5752 Theoretical and Applied Genetics |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY GENOMICS AGRONOMIC CHARACTERS GENETIC MARKERS DATA AGRICULTURAL SCIENCES AND BIOTECHNOLOGY GENOMICS AGRONOMIC CHARACTERS GENETIC MARKERS DATA |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY GENOMICS AGRONOMIC CHARACTERS GENETIC MARKERS DATA AGRICULTURAL SCIENCES AND BIOTECHNOLOGY GENOMICS AGRONOMIC CHARACTERS GENETIC MARKERS DATA Haixiao Hu Campbell, M.T. Yeats, T.H. Xuying Zheng Runcie, D.E. Covarrubias-Pazaran, G. Broeckling, C. Linxing Yao Caffe-Treml, M. Gutiérrez, L. Smith, K.P. Tanaka, J. Hoekenga, O.A. Sorrells, M.E. Gore, M.A. Jannink, J.L. Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations |
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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. |
format |
Article |
topic_facet |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY GENOMICS AGRONOMIC CHARACTERS GENETIC MARKERS DATA |
author |
Haixiao Hu Campbell, M.T. Yeats, T.H. Xuying Zheng Runcie, D.E. Covarrubias-Pazaran, G. Broeckling, C. Linxing Yao Caffe-Treml, M. Gutiérrez, L. Smith, K.P. Tanaka, J. Hoekenga, O.A. Sorrells, M.E. Gore, M.A. Jannink, J.L. |
author_facet |
Haixiao Hu Campbell, M.T. Yeats, T.H. Xuying Zheng Runcie, D.E. Covarrubias-Pazaran, G. Broeckling, C. Linxing Yao Caffe-Treml, M. Gutiérrez, L. Smith, K.P. Tanaka, J. Hoekenga, O.A. Sorrells, M.E. Gore, M.A. Jannink, J.L. |
author_sort |
Haixiao Hu |
title |
Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations |
title_short |
Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations |
title_full |
Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations |
title_fullStr |
Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations |
title_full_unstemmed |
Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations |
title_sort |
multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations |
publisher |
Springer |
publishDate |
2021 |
url |
https://hdl.handle.net/10883/21704 |
work_keys_str_mv |
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