Advances in genomics have expedited the improvement of several agriculturally important crops but similar efforts in wheat (Triticum spp.) have been more challenging. This is largely owing to the size and complexity of the wheat genome1, and the lack of genome-assembly data for multiple wheat lines2,3. Here we generated ten chromosome pseudomolecule and five scaffold assemblies of hexaploid wheat to explore the genomic diversity among wheat lines from global breeding programs. Comparative analysis revealed extensive structural rearrangements, introgressions from wild relatives and differences in gene content resulting from complex breeding histories aimed at improving adaptation to diverse environments, grain yield and quality, and resistance to stresses4,5. We provide examples outlining the utility of these genomes, including a detailed multi-genome-derived nucleotide-binding leucine-rich repeat protein repertoire involved in disease resistance and the characterization of Sm16, a gene associated with insect resistance. These genome assemblies will provide a basis for functional gene discovery and breeding to deliver the next generation of modern wheat cultivars.
Bibliographical noteFunding Information:
Acknowledgements We are grateful for funding from the Canadian Triticum Applied Genomics research project (CTAG2) funded by Genome Canada, Genome Prairie, the Western Grains Research Foundation, Government of Saskatchewan, Saskatchewan Wheat Development Commission, Alberta Wheat Commission, Viterra and Manitoba Wheat and Barley Growers Association. Funding was also provided by the Biotechnology and Biological Sciences Research Council (BBSRC) via the projects Designing Future Wheat (BB/P016855/1), sLOLA (BB/ J003557/1) and MAGIC Pangenome (BB/P010741/1, BB/P010733/1 and BB/P010768/1), by AMED NBRP (JP17km0210142), the German Federal Ministry of Education and Research (FKZ 031B0190, WHEATSeq, 2819103915 and 2819104015), German Network for Bioinformatics and Infrastructure de.NBI (FKZ 031A536A, 031A536B), German Federal Ministry of Food and Agriculture (BMEL FKZ 2819103915 WHEATSEQ), Israel Science Foundation (Grant 1137/17), JST CREST (JPMJCR16O3), US National Science Foundation (1339389), Kansas Wheat Commission and Kansas State University, MEXT KAKENHI, The Birth of New Plant Species (JP16H06469, JP16H06464, JP16H06466 and JP16K21727), National Agriculture and Food Research Organization (NARO) Vice President Fund, Swiss Federal Office of Agriculture (NAP-PGREL), Agroscope, Delley Seeds and Plants, ETH Zurich Institute of Agricultural Sciences, Fenaco Co-operative, IP-SUISSE, swisssem, JOWA, SGPV-FSPC, Swiss National Science Foundation (31003A_182318 and CRSII5_183578), University of Zurich Research Priority Program Evolution in Action, King Abdullah University of Science and Technology, Grains Research and Development Corporation (GRDC), Australian Research Council (CE140100008) and Groupe Limagrain. We are grateful for the computational support of the Functional Genomics Center Zurich, the Molecular Plant Breeding Group—ETH Zurich, and the Global Institute of Food Security (GIFS), Saskatoon. We acknowledge the contribution of the Australian Wheat Pathogens Consortium (https://data.bioplatforms.com/organization/edit/bpa-wheat-cultivars) in the generation of data used in this publication. The Initiative is supported by funding from Bioplatforms Australia through the Australian Government National Collaborative Research Infrastructure Strategy (NCRIS). We thank S. Wu for DNA preparations for assembly and ChIP–seq library preparations; O. Francisco-Pabalan and J. Santos, T. Wisk and S. Wolfe for their provision of OWBM images; M. Knauft, I. Walde, S. König, T. Münch, J. Bauernfeind and D. Schüler for their contribution to Hi-C data generation and sequencing, DNA sequencing and IT administration and sequence data management; J. Vrána for karyotyping the wheat cultivars Arina and Forno; and R. Regier for project management, administration and support.
© 2020, The Author(s).
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