Genomic predictions to leverage phenotypic data across genebanks
enome-wide prediction is a powerful tool in breeding. Initial results suggest that genome-wide approaches are also promising for enhancing the use of the genebank material: predicting the performance of plant genetic resources can unlock their hidden potential and fill the information gap in geneban...
| Autores principales: | , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Frontiers Media
2024
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| Acceso en línea: | https://hdl.handle.net/10568/139331 |
| _version_ | 1855541161397059584 |
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| author | El-Hanafi, Samira Jiang, Yong Kehel, Zakaria Schulthess, Albert W Zhao, Yusheng Mascher, Martin Haupt, Max Himmelbach, Axel Stein, Nils Amri, Ahmed Reif, Jochen Christoph |
| author_browse | Amri, Ahmed El-Hanafi, Samira Haupt, Max Himmelbach, Axel Jiang, Yong Kehel, Zakaria Mascher, Martin Reif, Jochen Christoph Schulthess, Albert W Stein, Nils Zhao, Yusheng |
| author_facet | El-Hanafi, Samira Jiang, Yong Kehel, Zakaria Schulthess, Albert W Zhao, Yusheng Mascher, Martin Haupt, Max Himmelbach, Axel Stein, Nils Amri, Ahmed Reif, Jochen Christoph |
| author_sort | El-Hanafi, Samira |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | enome-wide prediction is a powerful tool in breeding. Initial results suggest that genome-wide approaches are also promising for enhancing the use of the genebank material: predicting the performance of plant genetic resources can unlock their hidden potential and fill the information gap in genebanks across the world and, hence, underpin prebreeding programs. As a proof of concept, we evaluated the power of across-genebank prediction for extensive germplasm collections relying on historical data on flowering/heading date, plant height, and thousand kernel weight of 9,344 barley (Hordeum vulgare L.) plant genetic resources from the German Federal Ex situ Genebank for Agricultural and Horticultural Crops (IPK) and of 1,089 accessions from the International Center for Agriculture Research in the Dry Areas (ICARDA) genebank. Based on prediction abilities for each trait, three scenarios for predictive characterization were compared: 1) a benchmark scenario, where test and training sets only contain ICARDA accessions, 2) across-genebank predictions using IPK as training and ICARDA as test set, and 3) integrated genebank predictions that include IPK with 30% of ICARDA accessions as a training set to predict the rest of ICARDA accessions. Within the population of ICARDA accessions, prediction abilities were low to moderate, which was presumably caused by a limited number of accessions used to train the model. Interestingly, ICARDA prediction abilities were boosted up to ninefold by using training sets composed of IPK plus 30% of ICARDA accessions. Pervasive genotype × environment interactions (GEIs) can become a potential obstacle to train robust genome-wide prediction models across genebanks. This suggests that the potential adverse effect of GEI on prediction ability was counterbalanced by the augmented training set with certain connectivity to the test set. Therefore, across-genebank predictions hold the promise to improve the curation of the world’s genebank collections and contribute significantly to the long-term development of traditional genebanks toward biodigital resource centers. |
| format | Journal Article |
| id | CGSpace139331 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Frontiers Media |
| publisherStr | Frontiers Media |
| record_format | dspace |
| spelling | CGSpace1393312026-01-15T02:19:29Z Genomic predictions to leverage phenotypic data across genebanks El-Hanafi, Samira Jiang, Yong Kehel, Zakaria Schulthess, Albert W Zhao, Yusheng Mascher, Martin Haupt, Max Himmelbach, Axel Stein, Nils Amri, Ahmed Reif, Jochen Christoph barley genebanks icarda genomic prediction ipk prediction ability enome-wide prediction is a powerful tool in breeding. Initial results suggest that genome-wide approaches are also promising for enhancing the use of the genebank material: predicting the performance of plant genetic resources can unlock their hidden potential and fill the information gap in genebanks across the world and, hence, underpin prebreeding programs. As a proof of concept, we evaluated the power of across-genebank prediction for extensive germplasm collections relying on historical data on flowering/heading date, plant height, and thousand kernel weight of 9,344 barley (Hordeum vulgare L.) plant genetic resources from the German Federal Ex situ Genebank for Agricultural and Horticultural Crops (IPK) and of 1,089 accessions from the International Center for Agriculture Research in the Dry Areas (ICARDA) genebank. Based on prediction abilities for each trait, three scenarios for predictive characterization were compared: 1) a benchmark scenario, where test and training sets only contain ICARDA accessions, 2) across-genebank predictions using IPK as training and ICARDA as test set, and 3) integrated genebank predictions that include IPK with 30% of ICARDA accessions as a training set to predict the rest of ICARDA accessions. Within the population of ICARDA accessions, prediction abilities were low to moderate, which was presumably caused by a limited number of accessions used to train the model. Interestingly, ICARDA prediction abilities were boosted up to ninefold by using training sets composed of IPK plus 30% of ICARDA accessions. Pervasive genotype × environment interactions (GEIs) can become a potential obstacle to train robust genome-wide prediction models across genebanks. This suggests that the potential adverse effect of GEI on prediction ability was counterbalanced by the augmented training set with certain connectivity to the test set. Therefore, across-genebank predictions hold the promise to improve the curation of the world’s genebank collections and contribute significantly to the long-term development of traditional genebanks toward biodigital resource centers. 2024-02-13T19:12:44Z 2024-02-13T19:12:44Z Journal Article https://hdl.handle.net/10568/139331 en https://commons.datacite.org/doi.org/10.5447/ipk/2023/8 Open Access application/pdf Frontiers Media Samira El-Hanafi, Yong Jiang, Zakaria Kehel, Albert W Schulthess, Yusheng Zhao, Martin Mascher, Max Haupt, Axel Himmelbach, Nils Stein, Ahmed Amri, Jochen Christoph Reif. (28/8/2023). Genomic predictions to leverage phenotypic data across genebanks. Frontiers in Plant Science, 14. |
| spellingShingle | barley genebanks icarda genomic prediction ipk prediction ability El-Hanafi, Samira Jiang, Yong Kehel, Zakaria Schulthess, Albert W Zhao, Yusheng Mascher, Martin Haupt, Max Himmelbach, Axel Stein, Nils Amri, Ahmed Reif, Jochen Christoph Genomic predictions to leverage phenotypic data across genebanks |
| title | Genomic predictions to leverage phenotypic data across genebanks |
| title_full | Genomic predictions to leverage phenotypic data across genebanks |
| title_fullStr | Genomic predictions to leverage phenotypic data across genebanks |
| title_full_unstemmed | Genomic predictions to leverage phenotypic data across genebanks |
| title_short | Genomic predictions to leverage phenotypic data across genebanks |
| title_sort | genomic predictions to leverage phenotypic data across genebanks |
| topic | barley genebanks icarda genomic prediction ipk prediction ability |
| url | https://hdl.handle.net/10568/139331 |
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