Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Genebank genomics highlights the diversity of a global barley collection

Nature Genetics volume 51, pages 319–326 (2019)Cite this article

Subjects

Abstract

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Genetic structure of barley ex situ accessions.
Fig. 2: Genebank genomics.
Fig. 3: Genome-wide association scans for morphological characters.
Fig. 4: Isolation of the ROUGH AWN1.
Fig. 5: Association mapping of agronomic characters.

Similar content being viewed by others

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 https://doi.org/10.5447/IPK/2018/9. 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 https://doi.org/10.5447/IPK/2018/10 (flowering time). Passport, phenotypic and sequence data can be browsed in the BRIDGE web portal (http://bridge.ipk-gatersleben.de).

References

  1. Novembre, J. et al. Genes mirror geography within Europe. Nature 456, 98–101 (2008).

    Article  CAS  Google Scholar 

  2. Russell, J. et al. Exome sequencing of geographically diverse barley landraces and wild relatives gives insights into environmental adaptation. Nat. Genet. 48, 1024–1030 (2016).

    Article  CAS  Google Scholar 

  3. Brachi, B., Morris, G. P. & Borevitz, J. O. Genome-wide association studies in plants: the missing heritability is in the field. Genome. Biol. 12, 232 (2011).

    Article  Google Scholar 

  4. Lopes, M. S. et al. Exploiting genetic diversity from landraces in wheat breeding for adaptation to climate change. J. Exp. Bot. 66, 3477–3486 (2015).

    Article  CAS  Google Scholar 

  5. Oppermann, M., Weise, S., Dittmann, C. & Knüpffer, H. GBIS: the information system of the German Genebank. Database 2015, bav021 (2015).

    Article  Google Scholar 

  6. Pourkheirandish, M. et al. Evolution of the grain dispersal system in barley. Cell 162, 527–539 (2015).

    Article  CAS  Google Scholar 

  7. Poland, J. A., Brown, P. J., Sorrells, M. E. & Jannink, J.-L. Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS ONE 7, e32253 (2012).

    Article  CAS  Google Scholar 

  8. Wendler, N. et al. Unlocking the secondary gene-pool of barley with next-generation sequencing. Plant Biotechnol. J. 12, 1122–1131 (2014).

    Article  CAS  Google Scholar 

  9. Mascher, M. et al. A chromosome conformation capture ordered sequence of the barley genome. Nature 544, 427–433 (2017).

    Article  CAS  Google Scholar 

  10. Jakob, S. S. et al. Evolutionary history of wild barley (Hordeum vulgare subsp. spontaneum) analyzed using multilocus sequence data and paleodistribution modeling. Genome Biol. Evol. 6, 685–702 (2014).

    Article  CAS  Google Scholar 

  11. Chen, F. H. et al. Agriculture facilitated permanent human occupation of the Tibetan Plateau after 3600 bp. Science 347, 248–250 (2015).

    Article  CAS  Google Scholar 

  12. Badr, A. et al. On the origin and domestication history of barley (Hordeum vulgare). Mol. Biol. Evol. 17, 499–510 (2000).

    Article  CAS  Google Scholar 

  13. Blattner, F. R. & Méndez, A. G. B. RAPD data do not support a second centre of barley domestication in Morocco. Genet. Resour. Crop Evol. 48, 13–19 (2001).

    Article  Google Scholar 

  14. Pourkheirandish, M. et al. Elucidation of the origin of ‘agriocrithon’ based on domestication genes questions the hypothesis that Tibet is one of the centers of barley domestication. Plant J. 94, 525–534 (2018).

    Article  CAS  Google Scholar 

  15. Galinsky, K. J. et al. Fast principal-component analysis reveals convergent evolution of ADH1B in Europe and East Asia. Am. J. Hum. Genet. 98, 456–472 (2016).

    Article  CAS  Google Scholar 

  16. Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).

    Article  CAS  Google Scholar 

  17. Knüpffer, H. & van Hintum, T. J. L. in Core Collections of Plant Genetic Resources (eds Hodgkin, T., Brown, A. H. D., van Hintum, T. J. L. & Morales, E. A. V.) 171–178 (John Wiley and Sons, Chichester, UK, 1995).

  18. van Hintum, T. J. L. & Knüpffer, H. Duplication within and between germplasm collections. I. Genet. Resour. Crop Evol. 42, 127–133 (1995).

  19. van Hintum, T. J. L. & Visser, D. L. Duplication within and between germplasm collections. II. Genet. Resour. Crop Evol. 42, 135–145 (1995).

  20. Parzies, H., Spoor, W. & Ennos, R. Genetic diversity of barley landrace accessions (Hordeum vulgare ssp. vulgare) conserved for different lengths of time in ex situ gene banks. Heredity 84, 476 (2000).

    Article  CAS  Google Scholar 

  21. Harlan, J. R. Ethiopia: a center of diversity. Econ. Bot. 23, 309–314 (1969).

    Article  Google Scholar 

  22. Castañeda-Álvarez, N. P. et al. Global conservation priorities for crop wild relatives. Nat. Plants 2, 16022 (2016).

    Article  Google Scholar 

  23. Swarts, K. et al. Novel methods to optimize genotypic imputation for low-coverage, next-generation sequence data in crop plants. Plant Genome 7, https://doi.org/10.3835/plantgenome2014.05.0023 (2014).

    Article  CAS  Google Scholar 

  24. Mansfeld, R. Das morphologische System der Saatgerste, Hordeum vulgare L. sl. Der Züchter 20, 8–24 (1950).

    Article  Google Scholar 

  25. Komatsuda, T. et al. Six-rowed barley originated from a mutation in a homeodomain-leucine zipper I-class homeobox gene. Proc. Natl Acad. Sci. USA 104, 1424–1429 (2007).

    Article  CAS  Google Scholar 

  26. Ramsay, L. et al. INTERMEDIUM-C, a modifier of lateral spikelet fertility in barley, is an ortholog of the maize domestication gene TEOSINTE BRANCHED 1. Nat. Genet. 43, 169–172 (2011).

    Article  CAS  Google Scholar 

  27. Youssef, H. M. et al. Natural diversity of inflorescence architecture traces cryptic domestication genes in barley (Hordeum vulgare L.). Genet. Resour. Crop Evol. 64, 843–853 (2017).

    Article  CAS  Google Scholar 

  28. Lundqvist, U. Hexastichon and intermedium mutants in barley. Hereditas 92, 229–236 (1980).

    Google Scholar 

  29. Taketa, S. et al. Barley grain with adhering hulls is controlled by an ERF family transcription factor gene regulating a lipid biosynthesis pathway. Proc. Natl Acad. Sci. USA 105, 4062–4067 (2008).

    Article  CAS  Google Scholar 

  30. Elbaum, R., Zaltzman, L., Burgert, I. & Fratzl, P. The role of wheat awns in the seed dispersal unit. Science 316, 884–886 (2007).

    Article  CAS  Google Scholar 

  31. Liller, C. B. et al. Fine mapping of a major QTL for awn length in barley using a multiparent mapping population. Theor. Appl. Genet. 130, 269–281 (2017).

    Article  Google Scholar 

  32. Franckowiak, J. D. BGS 312; Smooth awn 1. Barley Genet. Newsl. 26, 261 (1997).

    Google Scholar 

  33. Hua, L. et al. LABA1, a domestication gene associated with long, barbed awns in wild rice. Plant Cell 27, 1875–1888 (2015).

    Article  CAS  Google Scholar 

  34. Gottwald, S., Bauer, P., Komatsuda, T., Lundqvist, U. & Stein, N. TILLING in the two-rowed barley cultivar ‘Barke’ reveals preferred sites of functional diversity in the gene HvHox1. BMC Res. Notes 2, 258 (2009).

    Article  Google Scholar 

  35. Åberg, E. & Wiebe, G. A. Classification of Barley Varieties Grown in the United States and Canada in 1945 (US Department of Agriculture, Washington, DC, USA, 1946).

  36. Longin, C. F. H. & Reif, J. C. Redesigning the exploitation of wheat genetic resources. Trends Plant Sci. 19, 631–636 (2014).

    Article  CAS  Google Scholar 

  37. González, M. Y. et al. Unlocking historical phenotypic data from an ex situ collection to enhance the informed utilization of genetic resources of barley (Hordeum sp.). Theoret. Appl. Genet. 131, 2009–2019 (2018).

    Article  Google Scholar 

  38. Turner, A., Beales, J., Faure, S., Dunford, R. P. & Laurie, D. A. The pseudo-response regulator Ppd-H1 provides adaptation to photoperiod in barley. Science 310, 1031–1034 (2005).

    Article  CAS  Google Scholar 

  39. Digel, B. et al. Photoperiod1 (Ppd-H1) controls leaf size. Plant Physiol. 172, 405–415 (2016).

    Article  CAS  Google Scholar 

  40. Fu, D. et al. Large deletions within the first intron in VRN-1 are associated with spring growth habit in barley and wheat. Mol. Genet. Genom. 273, 54–65 (2005).

    Article  CAS  Google Scholar 

  41. Yan, L. et al. The wheat and barley vernalization gene VRN3 is an orthologue of FT. Proc. Natl Acad. Sci. USA 103, 19581–19586 (2006).

    Article  CAS  Google Scholar 

  42. Tondelli, A. et al. Allelic variation at Fr-H1/Vrn-H1 and Fr-H2 loci is the main determinant of frost tolerance in spring barley. Environ. Exp. Bot. 106, 148–155 (2014).

    Article  CAS  Google Scholar 

  43. Richards, J. K., Friesen, T. L. & Brueggeman, R. S. Association mapping utilizing diverse barley lines reveals net form net blotch seedling resistance/susceptibility loci. Theoret. Appl. Genet. 130, 915–927 (2017).

    Article  CAS  Google Scholar 

  44. Arora, S. et al. Resistance gene discovery and cloning by sequence capture and association genetics. Preprint at bioRxiv https://doi.org/10.1101/248146 (2018).

  45. Jørgensen, I. H. Discovery, characterization and exploitation of Mlo powdery mildew resistance in barley. Euphytica 63, 141–152 (1992).

    Article  Google Scholar 

  46. Stein, N. et al. The eukaryotic translation initiation factor 4E confers multiallelic recessive Bymovirus resistance in Hordeum vulgare (L.). Plant J. 42, 912–922 (2005).

    Article  CAS  Google Scholar 

  47. Stracke, S. et al. Effects of introgression and recombination on haplotype structure and linkage disequilibrium surrounding a locus encoding Bymovirus resistance in barley. Genetics 175, 805–817 (2007).

    Article  CAS  Google Scholar 

  48. Friedt, W. & Foroughi-Wehr, B. Genetics of resistance to barley yellow mosaic virus. In: Barley Genetics V (Yasuda, S. & Konishi, T., eds.) 659–664 (Sanjo Press, Okayama, Japan, 1987).

  49. Humbroich, K. et al. Mapping of resistance against Barley mild mosaic virus‐Teik (BaMMV)—an rym5 resistance breaking strain of BaMMV—in the Taiwanese barley (Hordeum vulgare) cultivar ‘Taihoku A’. Plant Breeding 129, 346–348 (2010).

    Article  CAS  Google Scholar 

  50. Romay, M. C. et al. Comprehensive genotyping of the USA national maize inbred seed bank. Genome Biol. 14, R55 (2013).

    Article  Google Scholar 

  51. Wang, W. et al. Genomic variation in 3,010 diverse accessions of Asian cultivated rice. Nature 557, 43–49 (2018).

    Article  CAS  Google Scholar 

  52. Yano, K. et al. Genome-wide association study using whole-genome sequencing rapidly identifies new genes influencing agronomic traits in rice. Nat. Genet. 48, 927–934 (2016).

    Article  CAS  Google Scholar 

  53. Jiang, Y., Schmidt, R. H., Zhao, Y. & Reif, J. C. A quantitative genetic framework highlights the role of epistatic effects for grain-yield heterosis in bread wheat. Nat. Genet. 49, 1741–1746 (2017).

    Article  CAS  Google Scholar 

  54. Navarro, J. A. R. et al. A study of allelic diversity underlying flowering-time adaptation in maize landraces. Nat. Genet. 49, 476 (2017).

    Article  Google Scholar 

  55. Zimmermann, G., Bäumlein, H., Mock, H.-P., Himmelbach, A. & Schweizer, P. The multigene family encoding germin-like proteins of barley. Regulation and function in basal host resistance. Plant Physiol. 142, 181–192 (2006).

    Article  CAS  Google Scholar 

  56. Wendler, N. et al. Unlocking the secondary gene-pool of barley with next-generation sequencing. Plant Biotechnol. J. 12, 1122–1131 (2014).

    Article  CAS  Google Scholar 

  57. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).

    Article  Google Scholar 

  58. Mascher, M. et al. A chromosome conformation capture ordered sequence of the barley genome. Nature 544, 427–433 (2017).

    Article  CAS  Google Scholar 

  59. Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).

  60. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

  61. Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).

    Article  CAS  Google Scholar 

  62. Tange, O. GNU Parallel—the command-line power tool. ;login: 36, 42–47 (2011).

    Google Scholar 

  63. Zheng, X. et al. SeqArray—a storage-efficient high-performance data format for WGS variant calls. Bioinformatics 33, 2251–2257 (2017).

    Article  Google Scholar 

  64. Arend, D. et al. e!DAL—a framework to store, share and publish research data. BMC Bioinformatics 15, 214 (2014).

    Article  Google Scholar 

  65. Arend, D. et al. PGP repository: a plant phenomics and genomics data publication infrastructure. Database 2016, baw033 (2016).

    Article  Google Scholar 

  66. R Development Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, 2017).

    Google Scholar 

  67. Galinsky, K. J. et al. Fast principal-component analysis reveals convergent evolution of ADH1B in Europe and East Asia. Am. J. Hum. Genet. 98, 456–472 (2016).

    Article  CAS  Google Scholar 

  68. Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).

    Article  CAS  Google Scholar 

  69. Bhatia, G., Patterson, N., Sankararaman, S. & Price, A. L. Estimating and interpreting FST: the impact of rare variants. Genome Res. 23, 1514–1521 (2013).

    Article  CAS  Google Scholar 

  70. Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal 1695, 1–9 (2006).

    Google Scholar 

  71. De Beukelaer, H., Davenport, G. F. & Fack, V. Core Hunter 3: flexible core subset selection. BMC Bioinformatics 19, 203 (2018).

    Article  Google Scholar 

  72. Dray, S. & Dufour, A.-B. The ade4 package: implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007).

    Article  Google Scholar 

  73. Mansfeld, R. Das morphologische System der Saatgerste, Hordeum vulgare L. sl. Der Züchter 20, 8–24 (1950).

    Article  Google Scholar 

  74. Clark, M. F. & Adams, A. Characteristics of the microplate method of enzyme-linked immunosorbent assay for the detection of plant viruses. J. Gen. Virol. 34, 475–483 (1977).

    Article  CAS  Google Scholar 

  75. Lipka, A. E. et al. GAPIT: genome association and prediction integrated tool. Bioinformatics 28, 2397–2399 (2012).

    Article  CAS  Google Scholar 

  76. Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 7, https://doi.org/10.18637/jss.v067.i01 (2015).

  77. Yu, J. et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38, 203–208 (2006).

    Article  CAS  Google Scholar 

  78. Stich, B. et al. Comparison of mixed-model approaches for association mapping. Genetics 178, 1745–1754 (2008).

    Article  Google Scholar 

  79. VanRaden, P. Genomic measures of relationship and inbreeding. Interbull Bull. 37, 33–36 (2007).

    Google Scholar 

  80. Smith, A., Cullis, B. & Gilmour, A. Applications: the analysis of crop variety evaluation data in Australia. Aust. N. Z. J. Stat. 43, 129–145 (2001).

    Article  Google Scholar 

  81. Dunn, O. J. Multiple comparisons among means. J. Am. Stat. Assoc. 56, 52–64 (1961).

    Google Scholar 

  82. Mascher, M. et al. Barley whole exome capture: a tool for genomic research in the genus Hordeum and beyond. Plant J. 76, 494–505 (2013).

    Article  CAS  Google Scholar 

  83. Mascher, M. et al. Mapping-by-sequencing accelerates forward genetics in barley. Genome Biol. 15, R78 (2014).

    Article  Google Scholar 

Download references

Acknowledgements

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

  1. Matthias Jost

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

  2. Rajiv Sharma

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

  3. Raj K. Pasam

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

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

Authors and Affiliations

  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

Authors
  1. Sara G. Milner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matthias Jost
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shin Taketa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elena Rey Mazón
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Axel Himmelbach
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Markus Oppermann
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Stephan Weise
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Helmut Knüpffer
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Martín Basterrechea
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Patrick König
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Danuta Schüler
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Rajiv Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Raj K. Pasam
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Twan Rutten
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Ganggang Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Dongdong Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Jing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Gerhard Herren
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Thomas Müller
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Simon G. Krattinger
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Beat Keller
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Yong Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Maria Y. González
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Yusheng Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Antje Habekuß
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Sandra Färber
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Frank Ordon
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Matthias Lange
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Andreas Börner
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Andreas Graner
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Jochen C. Reif
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Uwe Scholz
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Martin Mascher
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Nils Stein
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

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.

Corresponding authors

Correspondence to Martin Mascher or Nils Stein.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

(a–d), 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

Supplementary Text and Figures

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

Reporting Summary

Supplementary Table 1

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

Supplementary Table 4

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

Supplementary Table 6

Phenotypic data for virus resistance

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Milner, S.G., Jost, M., Taketa, S. et al. Genebank genomics highlights the diversity of a global barley collection. Nat Genet 51, 319–326 (2019). https://doi.org/10.1038/s41588-018-0266-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-018-0266-x

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research