Browsing by Person "Zhao, Yusheng"

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Large-scale genotyping and phenotyping of a worldwide winter wheat genebank for its use in pre-breeding
(2022) Schulthess, Albert W.; Kale, Sandip M.; Zhao, Yusheng; Gogna, Abhishek; Rembe, Maximilian; Philipp, Norman; Liu, Fang; Beukert, Ulrike; Serfling, Albrecht; Himmelbach, Axel; Oppermann, Markus; Weise, Stephan; Boeven, Philipp H. G.; Schacht, Johannes; Longin, C. Friedrich H.; Kollers, Sonja; Pfeiffer, Nina; Korzun, Viktor; Fiebig, Anne; Schüler, Danuta; Lange, Matthias; Scholz, Uwe; Stein, Nils; Mascher, Martin; Reif, Jochen C.
Plant genetic resources (PGR) stored at genebanks are humanity’s crop diversity savings for the future. Information on PGR contrasted with modern cultivars is key to select PGR parents for pre-breeding. Genotyping-by-sequencing was performed for 7,745 winter wheat PGR samples from the German Federal ex situ genebank at IPK Gatersleben and for 325 modern cultivars. Whole-genome shotgun sequencing was carried out for 446 diverse PGR samples and 322 modern cultivars and lines. In 19 field trials, 7,683 PGR and 232 elite cultivars were characterized for resistance to yellow rust - one of the major threats to wheat worldwide. Yield breeding values of 707 PGR were estimated using hybrid crosses with 36 cultivars - an approach that reduces the lack of agronomic adaptation of PGR and provides better estimates of their contribution to yield breeding. Cross-validations support the interoperability between genomic and phenotypic data. The here presented data are a stepping stone to unlock the functional variation of PGR for European pre-breeding and are the basis for future breeding and research activities.
Order from entropy: big data from FAIR data cohorts in the digital age of plant breeding
(2025) Gogna, Abhishek; Arend, Daniel; Beier, Sebastian; Rezaei, Ehsan Eyshi; Würschum, Tobias; Zhao, Yusheng; Chu, Jianting; Reif, Jochen C.
Lack of interoperable datasets in plant breeding research creates an innovation bottleneck, requiring additional effort to integrate diverse datasets—if access is possible at all. Handling of plant breeding data and metadata must, therefore, change toward adopting practices that promote openness, collaboration, standardization, ethical data sharing, sustainability, and transparency of provenance and methodology. FAIR Digital Objects, which build on research data infrastructures and FAIR principles, offer a path to address this interoperability crisis, yet their adoption remains in its infancy. In the present work, we identify data sharing practices in the plant breeding domain as Data Cohorts and establish their connection to FAIR Digital Objects. We further link these cohorts to broader research infrastructures and propose a Data Trustee model for federated data sharing. With this we aim to push the boundaries of data management, often viewed as the last step in plant breeding research, to an ongoing process to enable future innovations in the field.