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Genomic predictions to leverage phenotypic data across genebanks

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Title Genomic predictions to leverage phenotypic data across genebanks
 
Creator El-Hanafi, Samira
 
Contributor Jiang, Yong
Kehel, Zakaria
Schulthess, Albert W
Zhao, Yusheng
Mascher, Martin
Haupt, Max
Himmelbach, Axel
Stein, Nils
Amri, Ahmed
Reif, Jochen Christoph
 
Subject icarda
genomic prediction
ipk
prediction ability
 
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.
 
Date 2024-02-13T19:11:02Z
2024-02-13T19:11:02Z
 
Type Journal Article
 
Identifier https://www.frontiersin.org/articles/10.3389/fpls.2023.1227656/full#supplementary-material
https://mel.cgiar.org/reporting/downloadmelspace/hash/1d116d5aacb47f7cb7e287ad22d55256/v/7bb709e4a114c27dbf2a25924e71aea6
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.
https://hdl.handle.net/20.500.11766/69170
Open access
 
Language en
 
Relation S. El Hanafi et al. (2023-05-16): Genome-wide prediction of thousand kernel weight for 234 winter barley accessions from the ICARDA genebank using 1,910 IPK genebank accessions as training set.
https://commons.datacite.org/doi.org/10.5447/ipk/2023/8
 
Rights CC-BY-4.0
 
Format PDF
 
Publisher Frontiers Media
 
Source Frontiers in Plant Science;14,(2023)