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Genomic predictions to leverage phenotypic data across genebanks

Genome-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 geneba...

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Autores principales: El Hanafi, Samira, Jiang, Yong, Kehel, Zakaria, Schulthess, Albert W., Zhao, Yusheng, Mascher, Martin, Haupt, Max, Himmelbach, Axel, Stein, Nils, Amri, Ahmed, Reif, Jochen C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10493331/
https://www.ncbi.nlm.nih.gov/pubmed/37701801
http://dx.doi.org/10.3389/fpls.2023.1227656
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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 C.
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 C.
author_sort El Hanafi, Samira
collection PubMed
description Genome-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.
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spelling pubmed-104933312023-09-12 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 C. Front Plant Sci Plant Science Genome-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. Frontiers Media S.A. 2023-08-28 /pmc/articles/PMC10493331/ /pubmed/37701801 http://dx.doi.org/10.3389/fpls.2023.1227656 Text en Copyright © 2023 El Hanafi, Jiang, Kehel, Schulthess, Zhao, Mascher, Haupt, Himmelbach, Stein, Amri and Reif https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
El Hanafi, Samira
Jiang, Yong
Kehel, Zakaria
Schulthess, Albert W.
Zhao, Yusheng
Mascher, Martin
Haupt, Max
Himmelbach, Axel
Stein, Nils
Amri, Ahmed
Reif, Jochen C.
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 Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10493331/
https://www.ncbi.nlm.nih.gov/pubmed/37701801
http://dx.doi.org/10.3389/fpls.2023.1227656
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