Theoretical and Applied Genetics

, Volume 131, Issue 2, pp 407–416 | Cite as

Unlocking the diversity of genebanks: whole-genome marker analysis of Swiss bread wheat and spelt

  • Thomas Müller
  • Beate Schierscher-Viret
  • Dario Fossati
  • Cécile Brabant
  • Arnold Schori
  • Beat Keller
  • Simon G. Krattinger
Original Article


Key message

High-throughput genotyping of Swiss bread wheat and spelt accessions revealed differences in their gene pools and identified bread wheat landraces that were not used in breeding.


Genebanks play a pivotal role in preserving the genetic diversity present among old landraces and wild progenitors of modern crops and they represent sources of agriculturally important genes that were lost during domestication and in modern breeding. However, undesirable genes that negatively affect crop performance are often co-introduced when landraces and wild crop progenitors are crossed with elite cultivars, which often limit the use of genebank material in modern breeding programs. A detailed genetic characterization is an important prerequisite to solve this problem and to make genebank material more accessible to breeding. Here, we genotyped 502 bread wheat and 293 spelt accessions held in the Swiss National Genebank using a 15K wheat SNP array. The material included both spring and winter wheats and consisted of old landraces and modern cultivars. Genome- and sub-genome-wide analyses revealed that spelt and bread wheat form two distinct gene pools. In addition, we identified bread wheat landraces that were genetically distinct from modern cultivars. Such accessions were possibly missed in the early Swiss wheat breeding program and are promising targets for the identification of novel genes. The genetic information obtained in this study is appropriate to perform genome-wide association studies, which will facilitate the identification and transfer of agriculturally important genes from the genebank into modern cultivars through marker-assisted selection.



We thank Simon Fluckiger and Helen Zbinden for technical assistance with DNA extraction and Prof. Eduard Akhunov for providing the genotypic data of the international wheat accessions. This work was supported by the Swiss Federal Office for Agriculture (BLW) in the framework of NAP-PGREL (national plan of action for the conservation and sustainable utilization of plant genetic resources). SGK is supported by an Ambizione fellowship of the Swiss National Science Foundation.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

The authors declare that this study complies with the current laws of the countries in which the experiments were performed.

Supplementary material

122_2017_3010_MOESM1_ESM.csv (215 kb)
Supplementary material 1 (CSV 215 kb) Online resource 1—Accession and passport data Additional information about the accessions used in this study
122_2017_3010_MOESM2_ESM.csv (23.4 mb)
Supplementary material 2 (CSV 23999 kb) Online resource 2—Genotypes Genotypes of accessions
122_2017_3010_MOESM3_ESM.xlsx (19 kb)
Supplementary material 3 (XLSX 19 kb) Online resource 3—Similar accessions Additional information about highly similar accessions
122_2017_3010_MOESM4_ESM.csv (24 kb)
Supplementary material 4 (CSV 23 kb) Online resource 4—Winter bread wheat landraces with no overlap with cultivars Passport data of winter bread wheat landraces of circle A in Fig. 2
122_2017_3010_MOESM5_ESM.csv (15 kb)
Supplementary material 5 (CSV 14 kb) Online resource 5—Spring bread wheat landraces with only little overlap with cultivars Passport data of spring bread wheat landraces of circle B in Fig. 2
122_2017_3010_MOESM6_ESM.pdf (44 kb)
Supplementary material 6 (PDF 43 kb) Online resource 6—Phylogenetic tree Phylogenetic tree of all accessions. Tree was constructed using R packages adegenet and poppr. Color codes: purple: spring spelt, dark green: winter spelt, light green: wheat/spelt cross, yellow: wheat winter landrace, red: wheat winter cultivar, light blue: wheat spring landrace, dark blue: wheat spring cultivar
122_2017_3010_MOESM7_ESM.pdf (13.1 mb)
Supplementary material 7 (PDF 13424 kb) Online resource 7—Supporting figures and tables Supporting figures S1–S17 and supporting tables S1–S4


  1. Akeret Ö (2005) Plant remains from a Bell Beaker site in Switzerland, and the beginnings of Triticum spelta (spelt) cultivation in Europe. Veg Hist Archaeobot 14:279–286. CrossRefGoogle Scholar
  2. Albrechtsen A, Nielsen FC, Nielsen R (2010) Ascertainment biases in SNP chips affect measures of population divergence. Mol Biol Evol 27:2534–2547. CrossRefPubMedPubMedCentralGoogle Scholar
  3. Arnold B, Corbett-Detig RB, Hartl D, Bomblies K (2013) RADseq underestimates diversity and introduces genealogical biases due to nonrandom haplotype sampling. Mol Ecol 22:3179–3190. CrossRefPubMedGoogle Scholar
  4. Bertin P, Grégorie D, Massart S, de Froidmont D (2001) Genetic diversity among European cultivated spelt revealed by microsatellites. Theor Appl Genet 102:148–156. CrossRefGoogle Scholar
  5. Blatter RHE, Jacomet S, Schlumbaum A (2004) About the origin of European spelt (Triticum spelta L.): allelic differentiation of the HMW glutenin B1-1 and A1-2 subunit genes. Theor Appl Genet 108:360–367. CrossRefPubMedGoogle Scholar
  6. Börner A, Landjeva S, Nagel M et al (2014) Plant genetic resources for food and agriculture (PGRFA) maintenance and research. Genet Plant Physiol 4:13–21Google Scholar
  7. Cavanagh CR, Chao S, Wang S et al (2013) Genome-wide comparative diversity uncovers multiple targets of selection for improvement in hexaploid wheat landraces and cultivars. Proc Natl Acad Sci 110:8057–8062. CrossRefPubMedPubMedCentralGoogle Scholar
  8. Chao S, Dubcovsky J, Dvorak J et al (2010) Population- and genome-specific patterns of linkage disequilibrium and SNP variation in spring and winter wheat (Triticum aestivum L.). BMC Genom 11:727. CrossRefGoogle Scholar
  9. Clavijo BJ, Venturini L, Schudoma C et al (2017) An improved assembly and annotation of the allohexaploid wheat genome identifies complete families of agronomic genes and provides genomic evidence for chromosomal translocations. Genome Res 27:885–896. CrossRefPubMedPubMedCentralGoogle Scholar
  10. Collard BCY, Mackill DJ (2008) Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Philos Trans R Soc B 363:557–572. CrossRefGoogle Scholar
  11. Cornish-Bowden A (1985) Nomenclature for incompletely specified bases in nucleic acid sequences: recommendations 1984. Nucleic Acids Res 13:3021–3030. CrossRefPubMedPubMedCentralGoogle Scholar
  12. Danecek P, Auton A, Abecasis G et al (2011) The variant call format and VCFtools. Bioinformatics 27:2156–2158. CrossRefPubMedPubMedCentralGoogle Scholar
  13. Dreisigacker S, Kishii M, Lage J et al (2008) Use of synthetic hexaploid wheat to increase diversity for CIMMYT bread wheat improvement. Aust J Agric Res 59:413. CrossRefGoogle Scholar
  14. Dvorak J, Deal KR, Luo MC et al (2012) The origin of spelt and free-threshing hexaploid wheat. J Hered 103:426–441. CrossRefPubMedGoogle Scholar
  15. Elshire RJ, Glaubitz JC, Sun Q et al (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One 6:e19379. CrossRefPubMedPubMedCentralGoogle Scholar
  16. FAO (2015) FAO statistical pocketbook. Food and Agriculture Organization of the United Nations, RomeGoogle Scholar
  17. Feuillet C, Langridge P, Waugh R (2008) Cereal breeding takes a walk on the wild side. Trends Genet 24:24–32. CrossRefPubMedGoogle Scholar
  18. Flint-Garcia SA, Thornsberry JM, Buckler ES (2003) Structure of linkage disequilibrium in plants. Annu Rev Plant Biol 54:357–374. CrossRefPubMedGoogle Scholar
  19. Fu YB, Somers DJ (2009) Genome-wide reduction of genetic diversity in wheat breeding. Crop Sci 49:161–168. CrossRefGoogle Scholar
  20. Gaudet DA, Kozub GC (1991) Screening winter wheat for resistance to cottony snow mold under controlled conditions. Can J Plant Sci 71:957–966. CrossRefGoogle Scholar
  21. Glaubitz JC, Casstevens TM, Lu F et al (2014) TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline. PLoS One. PubMedPubMedCentralGoogle Scholar
  22. Hamming RW (1950) Error detecting and error correcting codes. Bell Syst Tech J 29:147–160. CrossRefGoogle Scholar
  23. Hunter JD (2007) Matplotlib: a 2D graphics environment. Comput Sci Eng 9:90–95. CrossRefGoogle Scholar
  24. Jacquemin JM (2011) Wheat breeding in Belgium. In: Bonjean AP, Angus WJ, van Ginkel M (eds) The world wheat book: a history of wheat breeding, vol 2. Lavoisier, ParisGoogle Scholar
  25. Jombart T, Ahmed I (2011) adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics 27:21CrossRefGoogle Scholar
  26. Jombart T, Devillard S, Balloux F (2010) Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet 11:94. CrossRefPubMedPubMedCentralGoogle Scholar
  27. Kamvar ZN, Tabima JF, Grünwald NJ (2014) Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2:e281. CrossRefPubMedPubMedCentralGoogle Scholar
  28. Kang HM, Sul JH, Service SK et al (2010) Variance component model to account for sample structure in genome-wide association studies. Nat Genet 42:348–354. CrossRefPubMedPubMedCentralGoogle Scholar
  29. Kilian B, Graner A (2012) NGS technologies for analyzing germplasm diversity in genebanks. Brief Funct Genom 11:38–50CrossRefGoogle Scholar
  30. Kleijer G, Schori A, Schierscher B (2012) Die nationale Genbank von Agroscope ACW gestern, heute und morgen. Agrar Schweiz 3:408–413Google Scholar
  31. Knaus BJ, Grünwald NJ (2017) vcfr: a package to manipulate and visualize variant call format data in R. Mol Ecol Resour 17:44–53. CrossRefPubMedGoogle Scholar
  32. Li Y, Willer CJ, Ding J et al (2010) MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 34:816–834. CrossRefPubMedPubMedCentralGoogle Scholar
  33. Macer RCF (1966) The formal and monosomic genetic analysis of stripe rust (Puccinia striiformis) resistance in wheat. In: Mackey I (ed) Proceedings of the second international wheat genetics symposium, Lund, Sweden 1963. Hereditas Suppl 2. pp 127–142Google Scholar
  34. Martinet G (1907) Expériences sur la sélection des céréalesGoogle Scholar
  35. Mason AS, Zhang J, Tollenaere R et al (2015) High-throughput genotyping for species identification and diversity assessment in germplasm collections. Mol Ecol Resour 15:1091–1101. CrossRefPubMedGoogle Scholar
  36. McCouch SR, McNally KL, Wang W, Hamilton RS (2012) Genomics of gene banks: a case study in rice. Am J Bot 99:407–423. CrossRefPubMedGoogle Scholar
  37. McKinney W (2010) Data structures for statistical computing in Python. In: Proceedings of the 9th Python in science conference, pp 51–56Google Scholar
  38. Mengistu DK, Kidane YG, Catellani M et al (2016) High-density molecular characterization and association mapping in Ethiopian durum wheat landraces reveals high diversity and potential for wheat breeding. Plant Biotechnol J 14:1800–1812. CrossRefPubMedPubMedCentralGoogle Scholar
  39. Merchuk-Ovnat L, Barak V, Fahima T et al (2016) Ancestral QTL alleles from wild emmer wheat improve drought resistance and productivity in modern wheat cultivars. Front Plant Sci 7:452. CrossRefPubMedPubMedCentralGoogle Scholar
  40. Mohler V, Singh D, Singrün C, Park RF (2012) Characterization and mapping of Lr65 in spelt wheat “Altgold Rotkorn”. Plant Breed 131:252–257. CrossRefGoogle Scholar
  41. Mujeeb-Kazi A, Rosas V, Roldan S (1996) Conservation of the genetic variation of Triticum tauschii (Coss.) Schmalh. (Aegilops squarrosa auct. non L.) in synthetic hexaploid wheats (T. turgidum L. × T. tauschii; 2n = 6x = 42, AABBDD) and its potential utilization for wheat improvement. Genet Resour Crop Evol 43:129–134. CrossRefGoogle Scholar
  42. Paradis E, Claude J, Strimmer K (2004) APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20:289–290. CrossRefPubMedGoogle Scholar
  43. Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830Google Scholar
  44. Perez F, Granger BE (2007) IPython: a system for interactive scientific computing. Comput Sci Eng 9:21–29. CrossRefGoogle Scholar
  45. Poland JA, Brown PJ, Sorrells ME, Jannink J-L (2012) Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS One 7:e32253. CrossRefPubMedPubMedCentralGoogle Scholar
  46. Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575. CrossRefPubMedPubMedCentralGoogle Scholar
  47. Reif JC, Melchinger AE, Frisch M (2005a) Genetical and mathematical properties of similarity and dissimilarity coefficients applied in plant breeding and seed bank management. Crop Sci 45:1. CrossRefGoogle Scholar
  48. Reif JC, Zhang P, Dreisigacker S et al (2005b) Wheat genetic diversity trends during domestication and breeding. Theor Appl Genet 110:859–864. CrossRefPubMedGoogle Scholar
  49. Reynolds M, Dreccer F, Trethowan R (2006) Drought-adaptive traits derived from wheat wild relatives and landraces. J Exp Bot 58:177–186. CrossRefPubMedGoogle Scholar
  50. Rogers JS (1972) Measures of genetic similarity and genetic distance. Stud Genet 7:145–153Google Scholar
  51. Salamini F, Ozkan H, Brandolini A et al (2002) Genetics and geography of wild cereal domestication in the Near East. Nat Rev Genet 3:429–441. PubMedGoogle Scholar
  52. Schilperoord P (2006) Die Bedeutung des Getreidearchivs der Forschungsanstalt für Agrarökologie und Landwirtschaft Zürich-Reckenholz für die nationale Samenbank. Arbeitsbericht III NAP 02-231Google Scholar
  53. Schilperoord P (2013) Kulturpflanzen in der Schweiz—Dinkel. Verein für alpine Kulturpflanzen, AlvaneuGoogle Scholar
  54. Siedler H, Messmer MM, Schachermayr GM et al (1994) Genetic diversity in European wheat and spelt breeding material based on RFLP data. Theor Appl Genet 88:994–1003. CrossRefPubMedGoogle Scholar
  55. Steffenson BJ, Solanki S, Brueggeman RS (2016) Landraces from mountainous regions of Switzerland are sources of important genes for stem rust resistance in barley. Alp Bot 126:23–33. CrossRefGoogle Scholar
  56. Stein N, Herren G, Keller B (2001) A new DNA extraction method for high-throughput marker analysis in a large-genome species such as Triticum aestivum. Plant Breed 120:354–356. CrossRefGoogle Scholar
  57. Sukumaran S, Dreisigacker S, Lopes M et al (2015) Genome-wide association study for grain yield and related traits in an elite spring wheat population grown in temperate irrigated environments. Theor Appl Genet 128:353–363. CrossRefPubMedGoogle Scholar
  58. Sun Q, Wei Y, Ni Z et al (2002) Microsatellite marker for yellow rust resistance gene Yr5 in wheat introgressed from spelt wheat. Plant Breed 121:539–541. CrossRefGoogle Scholar
  59. Tanksley S, McCouch S (1997) Seed banks and molecular maps: unlocking genetic potential from the wild. Science (80-) 277:1063–1066. CrossRefGoogle Scholar
  60. Turner SD (2014) qqman: an R package for visualizing GWAS results using Q–Q and manhattan plots. bioRxiv. Google Scholar
  61. van der Walt S, Colbert SC, Varoquaux G (2011) The NumPy array: a structure for efficient numerical computation. Comput Sci Eng 13:22–30. CrossRefGoogle Scholar
  62. Vos PG, Paulo MJ, Voorrips RE et al (2017) Evaluation of LD decay and various LD-decay estimators in simulated and SNP-array data of tetraploid potato. Theor Appl Genet 130:123–135. CrossRefPubMedGoogle Scholar
  63. Wang S, Wong D, Forrest K et al (2014) Characterization of polyploid wheat genomic diversity using a high-density 90 000 single nucleotide polymorphism array. Plant Biotechnol J 12:787–796. CrossRefPubMedPubMedCentralGoogle Scholar
  64. Wang C, Kao W-H, Hsiao CK et al (2015) Using Hamming distance as information for SNP-sets clustering and testing in disease association studies. PLoS One 10:e0135918. CrossRefPubMedPubMedCentralGoogle Scholar
  65. Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution (New York) 38:1358. Google Scholar
  66. Winzeler H, Schmid J, Winzeler M, Rüegger A (1991) Neue Aspekte der Dinkelzüchtung (Triticum spelta L.) in der Schweiz. In: 2. Hohenheimer Dinkelkolloquium, Universität Hohenheim, pp 11–25Google Scholar
  67. Yan L, Loukoianov A, Tranquilli G et al (2003) Positional cloning of the wheat vernalization gene VRN1. Proc Natl Acad Sci USA 100:6263–6268. CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Plant and Microbial BiologyUniversity of ZurichZurichSwitzerland
  2. 2.Department of Plant Production SciencesAgroscope, ChanginsNyonSwitzerland
  3. 3.Biological and Environmental Sciences and Engineering Division (BESE)King Abdullah University of Science and Technology (KAUST)ThuwalSaudi Arabia

Personalised recommendations