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Genomics-informed prebreeding unlocks the diversity in genebanks for wheat improvement

Nature Genetics volume 54, pages 1544–1552 (2022)Cite this article

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Abstract

The great efforts spent in the maintenance of past diversity in genebanks are rationalized by the potential role of plant genetic resources (PGR) in future crop improvement—a concept whose practical implementation has fallen short of expectations. Here, we implement a genomics-informed prebreeding strategy for wheat improvement that does not discriminate against nonadapted germplasm. We collect and analyze dense genetic profiles for a large winter wheat collection and evaluate grain yield and resistance to yellow rust (YR) in bespoke core sets. Breeders already profit from wild introgressions but PGR still offer useful, yet unused, diversity. Potential donors of resistance sources not yet deployed in breeding were detected, while the prebreeding contribution of PGR to yield was estimated through ‘Elite × PGR’ F1 crosses. Genomic prediction within and across genebanks identified the best parents to be used in crosses with elite cultivars whose advanced progenies can outyield current wheat varieties in multiple field trials.

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Fig. 1: Genetic diversity within and between genebanks.
Fig. 2: Tracing the history of introgression breeding.
Fig. 3: Deep mining PGR of the IPK genebank for new sources of resistance against YR not yet used in winter wheat breeding.
Fig. 4: Uncovering the yield breeding value (BV) of PGR for prebreeding through Elite × PGR hybrids and genomic prediction.

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Data availability

Raw sequence data collected in this study have been deposited at the European Nucleotide Archive (https://www.ebi.ac.uk/ena/) under the following Project IDs: PRJEB41976 (GBS), PRJEB48988 (WGS) and PRJEB48738 (WGS). Individual sequenced samples can be found through the ‘SAMEA’ BioSample IDs (Supplementary Tables 3, 15 and 18) on EMBL-EBI BioSamples (https://www.ebi.ac.uk/biosamples/). VCF files for GBS and WGS data are available from EBI EVA (https://www.ebi.ac.uk/eva/) under project PRJEB52759. Integrated phenotypic and genomic data used for GWAS in T3Cs and genomic prediction were deposited into e!DAL-PGP111 and can be accessed here112,113. Among the Supplementary Tables, the following data are included: passports of the studied plant material and presence in datasets/analyses (Supplementary Table 1), list of SSD-PGR from the TRI catalog and their DOIs (Supplementary Table 39), mislabeled tetraploid wheats (Supplementary Table 4), duplicated accessions within the IPK collection (Supplementary Table 5) and between INRAE and IPK collections (Supplementary Table 7), accessions private to INRAE (Supplementary Table 8) and IPK genebanks (Supplementary Table 9), YR score estimates from large-scale screenings based on natural infections (Supplementary Table 12), YR score estimates based on natural and artificial field inoculations in balanced experiments (Supplementary Table 15), detected selective sweep regions (Supplementary Table 19), genotypes of the historic panel and their alien introgressions (Supplementary Table 21), markers significantly associated with YR score estimates (Supplementary Table 22), donors of potentially new sources of YR resistance for prebreeding (Supplementary Table 28) and those optimal for gene validation (Supplementary Table 27), sequences of k-mers associated with YR scores but absent in reference genomes (Supplementary Table 25) and their donors (Supplementary Table 26), yield breeding values of PGR estimated using the Elite × PGR bridging context (Supplementary Table 29), estimated yield performances of prebreeding lines in validation experiments and their DOIs (Supplementary Table 34), genomic predictions of yield breeding values for the IPK (Supplementary Table 35) and INRAE (Supplementary Table 37) collections.

Code availability

The custom awk code for filtering of VCF files is available at e!DAL-PGP and can be accessed at ref. 114 (https://doi.org/10.5447/ipk/2022/15). Custom R codes associated to input files deposited into e!DAL-PGP are available in the respective YR_GWAS_ R_codes112 (https://doi.org/10.5447/ipk/2022/5) and RD_and_GP_R_codes113 (https://doi.org/10.5447/ipk/2022/6) folders.

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Acknowledgements

This research work was mainly funded by the German Federal Ministry of Education and Research under the frame of the Project GeneBank2.0 (grant no. FKZ031B0184B and FKZ031B0184A to J.C.R.). Additional financial support was provided by the German Federal Ministry of Food and Agriculture under the frame of the GenDiv-Project (grant no. 2814603813 to N.S.). We are very thankful to A. Börner for providing seeds of the ‘B’ collection. We would like to also thank C. Martin, J. Perovic, J. Schneider, S. Gentz, A. Kunze, M. Kühne, L. Gaczensky and M. Koch for their valuable technical support in field activities, as well as S. König, J. Pohl, I. Walde and M. Knauft for their technical assistance in producing GBS and WGS data. We additionally thank J. Bauernfeind, T. Münch and H. Miehe for administration of the IT infrastructure as well as A. Fiebig, D. Schüler and D. Arend for their support with data management and repositories.

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Author notes

  1. Sandip M. Kale

    Present address: Carlsberg Research Laboratory, Copenhagen, Denmark

  2. Fang Liu

    Present address: Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, China

  3. These authors contributed equally: Albert W. Schulthess, Sandip M. Kale, Fang Liu.

Authors and Affiliations

  1. Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany

    Albert W. Schulthess, Sandip M. Kale, Fang Liu, Yusheng Zhao, Norman Philipp, Maximilian Rembe, Yong Jiang, Axel Himmelbach, Jörg Fuchs, Markus Oppermann, Stephan Weise, Matthias Lange, Uwe Scholz, Nils Stein, Martin Mascher & Jochen C. Reif

  2. Julius Kühn Institute (Federal Research Centre for Cultivated Plants), Quedlinburg, Germany

    Ulrike Beukert & Albrecht Serfling

  3. Limagrain GmbH, Peine-Rosenthal, Germany

    Philipp H. G. Boeven & Johannes Schacht

  4. State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany

    C. Friedrich H. Longin

  5. KWS SAAT SE & Co. KGaA, Einbeck, Germany

    Sonja Kollers & Viktor Korzun

  6. KWS LOCHOW GmbH, Bergen, Germany

    Nina Pfeiffer

  7. Center for Integrated Breeding Research (CiBreed), Georg-August-University, Göttingen, Germany

    Nils Stein

  8. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany

    Martin Mascher

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  1. Albert W. Schulthess
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Contributions

J.C.R., M.M., N.S. and V.K. developed the concept. M.O. and S.W. provided passport information of the TRI catalog of genebank accessions as well as DOIs for their derived progenies. S.K. provided DNA for GBS and A.H. and N.S. produced sequencing raw reads. A.H. and N.S. obtained high-quality DNA samples and generated WGS raw reads. A.W.S., N. Philipp, U.B., A.S., N. Pfeiffer., P.H.G.B. and J.S. conducted YR resistance screenings. N. Philipp, P.H.G.B. and C.F.H.L. produced seeds and conducted yield trials for hybrids. N. Philipp, M.R. and J.C.R. produced, selected and yield-tested PGR-derived families. J.F. confirmed wheat ploidy level through fluorometry. A.W.S., Y.Z., A.S., N. Philipp and M.R. analyzed and curated phenotypic data. S.M.K. processed sequencing reads, integrated INRAE and IPK genomic data, generated SNP and k-mer matrices and performed diversity, selective sweep, introgression analyses as well as GBS-based GWAS. A.W.S. integrated genomic and phenotypic data, selected T3Cs and performed genomic prediction. F.L. performed GWAS for YR and selected donors with the support of J.C.R., A.W.S. and M.M. A.S. provided genomic data for NILs carrying Yr genes and performed colocation analysis. Y.J., Y.Z. and M.M. provided statistical support. M.L. and U.S. facilitated the data management, the sequence and variation data submission to public repositories. A.W.S., S.M.K., F.L., M.M. and J.C.R. wrote the manuscript with the input of all other co-authors.

Corresponding authors

Correspondence to Martin Mascher or Jochen C. Reif.

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Supplementary information

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Supplementary Notes 1 and 2 and Figs. 1–17.

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Schulthess, A.W., Kale, S.M., Liu, F. et al. Genomics-informed prebreeding unlocks the diversity in genebanks for wheat improvement. Nat Genet 54, 1544–1552 (2022). https://doi.org/10.1038/s41588-022-01189-7

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