Genomic predictions to leverage phenotypic data across genebanks

Frontiers in Plant Science

Plant Science

Zakaria Kehel¹  Ahmed Amri¹  Samira El Hanafi²  Yong Jiang²  Axel Himmelbach²  Max Haupt²  Jochen C. Reif²  Yusheng Zhao²  Martin Mascher²  Albert W. Schulthess²  Nils Stein³ 

[1] International Center for Agricultural Research in Dry Areas (ICARDA), Rabat, Morocco;Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany;Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany;Center for Integrated Breeding Research (CiBreed), Georg-August-University, Göttingen, Germany;

关键词: barley;    genebank;    genomic prediction;    ICARDA;    IPK;    prediction ability;   

DOI  :  10.3389/fpls.2023.1227656

 received in 2023-05-23, accepted in 2023-08-07,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 El Hanafi, Jiang, Kehel, Schulthess, Zhao, Mascher, Haupt, Himmelbach, Stein, Amri and Reif