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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CoreSNP: an efficient pipeline for core marker profile selection from genome-wide SNP datasets in crops
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Large-scale genotyping and phenotyping of a worldwide winter wheat genebank for its use in pre-breeding
Scientific Data Open Access 26 December 2022
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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.
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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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.
The authors declare no competing interests.
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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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