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Genebank genomics highlights the diversity of a global barley collection

Nature Genetics (2018) | Download Citation


Genebanks hold comprehensive collections of cultivars, landraces and crop wild relatives of all major food crops, but their detailed characterization has so far been limited to sparse core sets. The analysis of genome-wide genotyping-by-sequencing data for almost all barley accessions of the German ex situ genebank provides insights into the global population structure of domesticated barley and points out redundancies and coverage gaps in one of the world’s major genebanks. Our large sample size and dense marker data afford great power for genome-wide association scans. We detect known and novel loci underlying morphological traits differentiating barley genepools, find evidence for convergent selection for barbless awns in barley and rice and show that a major-effect resistance locus conferring resistance to bymovirus infection has been favored by traditional farmers. This study outlines future directions for genomics-assisted genebank management and the utilization of germplasm collections for linking natural variation to human selection during crop evolution.

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Data availability

Sequence data collected in this study have been deposited at the European Nucleotide Archive (accession numbers PRJEB23967, PRJEB24563, PRJEB24627, PRJEB26634, PRJEB26652 and PRJEB27184; Supplementary Table 1). SNP matrices and phenotypic data have been deposited at Passport data for all accessions are reported in Supplementary Table 1. Phenotypic data used for GWAS are reported in Supplementary Table 4 (morphological characters), Supplementary Table 6 (virus resistance), and at (flowering time). Passport, phenotypic and sequence data can be browsed in the BRIDGE web portal (

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We thank G. Matzig, J. Pohl, M. Ziems, C. Fricke, M. Kretschmann, S. König, I. Walde, G. Schütze, A. Fiebig, J. Bauernfeind, T. Münch and D. Grau for technical assistance and G. Proeseler for initiating the long-term virus testing. We are grateful to H. de Beukelaer for Corehunter support. We thank B. Schierscher-Viret from the Swiss national genebank for providing seeds and K. Lipfert for artwork. This work was supported by a grant from the Leibniz Association to N.S., U.S., H.K., A.B., A.G. and J.C.R. (Pakt für Forschung und Innovation: SAW-2015-IPK-1 ‘BRIDGE’); by the German Ministry of Education and Research (BMBF; grant 031A536 ‘de.NBI’ to U.S.); by the Young Elite Scientists Sponsorship Program (2015QNRC001) from the China Association for Science and Technology (CAST); by a grant from the China Scholarship Council to G.G.; by funding from the China Agriculture Research System (CARS-05) and the Agricultural Science and Technology Innovation Program to J.Z.; and by the Swiss Federal Office for Agriculture in the framework of the National Plan of Action for the conservation and sustainable utilization of plant genetic resources (NAP-PGREL). S.G.M. acknowledges support from the German Academic Exchange service (DAAD) through a Leibniz-DAAD fellowship. Y.J. and M.Y.G. were supported by BMBF grants 031B0184A and 031B0190A, respectively. S.F. was supported by BMBF grants to F.O. and A.Habekuß (ViReCrop, FKZ: 0315708B; COBRA, FKZ: 031A323B).

Author information

Author notes

    • Matthias Jost

    Present address: Agriculture and Food, The Commonwealth Scientific and Industrial Research Organisation, Canberra, Australia

    • Rajiv Sharma

    Present address: University of Dundee at the James Hutton Institute, Invergowrie, UK

    • Raj K. Pasam

    Present address: Department of Economic Development, Jobs, Transport and Resources, Centre for AgriBioscience, Agriculture Victoria Research, Bundoora, Victoria, Australia

  1. These authors contributed equally: Sara G. Milner, Matthias Jost.


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

    • Sara G. Milner
    • , Matthias Jost
    • , Elena Rey Mazón
    • , Axel Himmelbach
    • , Markus Oppermann
    • , Stephan Weise
    • , Helmut Knüpffer
    • , Martín Basterrechea
    • , Patrick König
    • , Danuta Schüler
    • , Rajiv Sharma
    • , Raj K. Pasam
    • , Twan Rutten
    • , Yong Jiang
    • , Maria Y. González
    • , Yusheng Zhao
    • , Matthias Lange
    • , Andreas Börner
    • , Andreas Graner
    • , Jochen C. Reif
    • , Uwe Scholz
    • , Martin Mascher
    •  & Nils Stein
  2. Institute of Plant Science and Resources, Okayama University, Kurashiki, Japan

    • Shin Taketa
  3. Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China

    • Ganggang Guo
    • , Dongdong Xu
    •  & Jing Zhang
  4. Department of Plant and Microbial Biology, University of Zurich, Zurich, Switzerland

    • Gerhard Herren
    • , Thomas Müller
    • , Simon G. Krattinger
    •  & Beat Keller
  5. Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    • Simon G. Krattinger
  6. Institute for Resistance Research and Stress Tolerance, Julius Kühn Institute (Federal Research Centre for Cultivated Plants), Quedlinburg, Germany

    • Antje Habekuß
    • , Sandra Färber
    •  & Frank Ordon
  7. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany

    • Martin Mascher
  8. Center for Integrated Breeding Research, Georg-August-Universität Göttingen, Göttingen, Germany

    • Nils Stein


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N.S., M.M., U.S., J.C.R. and A.G. designed research. S.G.M., M.J., M.Y.G., Y.J., Y.Z. and M.M. analyzed data. A.B. supervised germplasm retrieval. G.H. optimized DNA extraction methods. A.Himmelbach performed GBS experiments. S.W., M.O. and H.K. managed, digitized and validated passport and phenotypic data. D.S., M.L. and U.S. managed sequence and phenotypic data. G.H., T.M., S.G.K. and B.K. contributed Swiss genebank accessions. G.G., D.X. and J.Z. contributed Chinese genebank accessions. M.J. and E.R.M. led phenotyping efforts. T.R. carried out microscopy. R.S. and R.K.P. mapped awn roughness. S.T. contributed expert knowledge on awn roughness. M.B., P.K., M.L. and U.S. implemented the online portal. A.Habekuß, S.F. and F.O. contributed data on virus resistance. S.G.M., M.J., N.S. and M.M. wrote the paper. All authors have read and approved the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Martin Mascher or Nils Stein.

Integrated supplementary information

  1. Supplementary Figure 1 Read depth and SNP distribution.

    a, Cumulative size of regions covered by GBS reads as a function of thresholds on missing data. The coverage analysis was performed on a random subset of 500 samples. Colors refer to the minimum read depth per sample. A total of 3 Mb of sequence is covered by at least two reads (dotted lines). b,c, Number of SNPs in 10-Mb windows along the barley reference genome: all SNPs (b) and SNPs with a minor allele frequency (MAF) ≥5% (c); SNPs with MAF ≥1%. Only variants with less than 10% missing data (Table 1) were considered.

  2. Supplementary Figure 2 Principal-component analysis of 19,778 domesticated barleys.

    PCA with 76,102 markers. Samples are colored according to geographic origin, row type, annual growth habit and domestication status. Color codes are defined in the inset map of a and the legends of b–d. The proportion of variance explained by the principal components is indicated in the axis labels of a. The proportion of variance explained by the principal components is indicated in the axis labels. The map was created with the R package mapdata.

  3. Supplementary Figure 3 PCA with 15,872 markers with a minor allele frequency  5 %.

    Samples are colored according to geographic origin. The color code is defined in the inset map. The proportion of variance explained by the principal components is indicated in the axis labels. The map was created with the R package mapdata.

  4. Supplementary Figure 4 ADMIXTURE results.

    (a), ADMIXTURE ancestry coefficients for k ranging from 2 to 12 for 17,640 samples with known countries of origin. The colored blocks below the bar plots correspond to the regional grouping of Fig. 1b and Supplementary Figs. 2 and 3. (b), Correspondence between ADMIXTURE and PCA. The same data points as in Supplementary Fig. 2 are shown. Samples are colored according to their assignment to ADMIXTURE groups (k = 12) in a. Samples whose highest ancestry coefficient is less than 70% are colored gray.

  5. Supplementary Figure 5 Comparison of ADMIXTURE runs with different k values.

    (a), Cross-validation (CV) errors of ADMIXTURE runs. CV errors for six replicate runs per k value are plotted. Jitter was added to x-axis coordinates. (b), Proportion of samples assigned to populations (q ≥ 0.7) for different values of k.

  6. Supplementary Figure 6 Decay of linkage disequilibrium in geographically defined germplasm groups of domesticated barley.

    Only variants with a minor allele frequency ≥ 1% were considered. Rolling means were used for smoothing. The geographically defined sample groups are a subset of the 10,183 non-redundant domesticated accessions of the IPK genebank.

  7. Supplementary Figure 7 Threshold for the identification of potential duplicates.

    The distribution (kernel density estimate) of pairwise homozygous difference between pairs of samples (sample set: 17,613 domesticated samples of the IPK genebank) is shown. A set of 72,200 SNPs segregating in this sample set was used. The red line (0.05%) marks the threshold for calling sample pairs potential duplicates.

  8. Supplementary Figure 8 Analysis of multiple individuals for 32 domesticated barley accessions from the IPK genebank.

    (a), Number of homozygous differences for pairs of individuals from the same accession. Country codes are used according to ISO 3166-1 alpha-3. Each dot represents a pair of individuals. The red line at 1,981 marks the 95th percentile of inter-accession differences, i.e., 95% of comparison between samples from different accessions have at least 1,981 homozygous differences. A set of 27,296 SNPs with a minor allele frequency of 0.05% for the identity-by-state analysis. (b), Percentage of heterozygous genotype calls per individual. Each dot represents the GBS sample of one individual. Only variants with minor allele frequency ≥ 20% were considered for b. Note that heterozygosity in the majority of samples is very low, as expected for any inbreeding crop.

  9. Supplementary Figure 9 Principal-component analysis of a core set of 1,000 diverse accessions from IPK’s genebank.

    The same data points as in Supplementary Fig. 2 are shown. Samples are colored blue if they are part of the core set. All other samples are shown in gray.

  10. Supplementary Figure 10 GWAS results without imputation and genetic differentiation between hulled and naked types.

    (ad), GWAS results for morphological traits using GBS SNPs with less than 10% missing data without imputation: row type (a), awn roughness (b), lemma adherence (c). d, FST between naked and hulled types in 1-Mb bins. The red lines in a and c indicate the significance threshold after correction for multiple testing using the Bonferroni method. GWAS scans were done using a mixed linear model approach with a sample set of 1,000 biologically independent individuals. FST was calculated using the method of Bhatia et al.69.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–10 and Supplementary Tables 2, 3, 5 and 7

  2. Reporting Summary

  3. Supplementary Table 1

    Passport data and sequence accession codes for 22,626 GBS samples

  4. Supplementary Table 4

    Passport data and scores for morphological characters for the core set of 1,000 accessions

  5. Supplementary Table 6

    Phenotypic data for virus resistance

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