Genetic Resources and Crop Evolution

, Volume 66, Issue 2, pp 335–348 | Cite as

Genetic diversity and population structure of synthetic hexaploid-derived wheat (Triticum aestivum L.) accessions

  • Emily Gordon
  • Mina Kaviani
  • Sateesh Kagale
  • Thomas Payne
  • Alireza NavabiEmail author
Research Article


A comprehensive understanding of the population structure and genetic diversity of potential germplasm is necessary for making breeding decisions and to fully interpret marker-trait associations. The purpose of this study was to examine the genetic diversity and population structure of a panel of 194 synthetic hexaploid-derived wheat (SHW; Triticum aestivum L.) accessions using 6904 polymorphic single nucleotide polymorphism (SNP) markers. Ancestry-based dissimilarity indices and marker-based genetic distances were positively correlated (r = 0.67). The variation in the primary synthetic parent in the pedigrees accounted for 4.52%, while the degree of the synthetic contribution accounted for only 1.06% of variation in the genetic distance. In addition, variation in the Aegilops tauschii Coss. (syn. Aegilops squarrosa auct. non L.) accession and T. turgidum accession used in the initial cross accounted for 3.48% and 2.75% of the variation in genetic distance, respectively. Using a model-based population structure approach, seven sub-populations were identified in the panel. Results of the model-based population structure analysis was for the most part in agreement with the distance-based clustering using unweighted pair group method with arithmetic mean (UPGMA) of the genetic distance or ancestry data and the principle component analysis of relatedness. We conclude that using a model-based approach provides a more statistically robust estimation of population structure. Results of this study, while highlighting the potential contribution of introgressed genome in the panel, provide the foundation for employing this panel in genome-wide association studies.


Synthetic hexaploid derived wheat Genetic diversity Population structure Triticum and Aegilops tauschii 



Technical assistance of Yasmina Bekkaoui for performing SNP array hybridization, bioinformatics support of Dr. Matthew Hayden at La Trobe University in Melbourne, Australia in SNP genotype calling, and the financial support of the project by the National Scientific and Engineering Council of Canada are duly acknowledged.

Author contributions

EG and AN design the experiment, TP provided the germplasm and pedigree data, MK and EG conducted the lab work, SK genotyped the population with SNP markers, EG analyzed the data and prepared the manuscript, EG, MK, SK, TP, and AN reviewed and edited the manuscript prior to submission.

Compliance with ethical standards

Conflict of interest

All authors declare that there is no conflict of interest.

Supplementary material

10722_2018_711_MOESM1_ESM.pdf (414 kb)
Supplementary material 1 (PDF 414 kb)
10722_2018_711_MOESM2_ESM.pdf (202 kb)
Supplementary material 2 (PDF 201 kb)


  1. Bhatta M, Morgounov A, Belamkar V, Poland J, Baenziger PS (2018) Unlocking the novel genetic diversity and population structure of synthetic hexaploid wheat. BMC Genom 19:591. CrossRefGoogle Scholar
  2. Bordes J, Ravel C, Jaubertie JP, Duperrier B, Gardet O, Heumez E, Pissavy AL, Charmet G, Le Gouis J, Balfourier F (2013) Genomic regions associated with the nitrogen limitation response revealed in a global wheat core collection. Theor Appl Genet 126:805–822. CrossRefPubMedGoogle Scholar
  3. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635. CrossRefGoogle Scholar
  4. Carena MJ (2009) Cereals. Springer US, New YorkCrossRefGoogle Scholar
  5. Cheng R, Parker CC, Abney M, Palmer AA (2013) Practical considerations regarding the use of genotype and pedigree data to model relatedness in the context of genome-wide association studies. G3 3:1861–1867. CrossRefPubMedGoogle Scholar
  6. Cormier F, Le Gouis J, Dubreuil P, Lafarge S, Praud S (2014) A genome-wide identification of chromosomal regions determining nitrogen use efficiency components in wheat (Triticum aestivum L.). Theor Appl Genet 127:2679–2693. CrossRefPubMedGoogle Scholar
  7. Crossa J, Campos G, Perez P, Gianola D, Burgueno J, Araus JL, Makumbi D, Singh RP, Dreisigacker S, Yan J, Arief V, Banziger M, Braun H-J (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186:713–724. CrossRefPubMedPubMedCentralGoogle Scholar
  8. Das MK, Bai GH, Munjeeb-Kazi A, Mujeeb-Kazi A (2007) Genetic diversity in conventional and synthetic wheats with drought and salinity tolerance based on AFLP. Can J Plant Sci 87:691–702. CrossRefGoogle Scholar
  9. Del Blanco IA, Rajaram S, Kronstad WE (2001) Agronomic potential of synthetic hexaploid wheat-derived populations. Crop Sci 41:670–676. CrossRefGoogle Scholar
  10. Dreisigacker S, Zhang P, Warburton ML, Van Ginkel M, Hoisington D, Bohn M, Melchinger AE (2004) SSR and pedigree analyses of genetic diversity among CIMMYT wheat lines targeted to different megaenvironments. Crop Sci 44:381–388. CrossRefGoogle Scholar
  11. Dubcovsky J, Dvorak J (2007) Genome plasticity a key factor in the success of polyploid wheat under domestication. Science 316:1862–1866. CrossRefPubMedPubMedCentralGoogle Scholar
  12. Dvorak J, Luo MC, Yang ZL, Zhang HB (1998) The structure of the Aegilops tauschii genepool and the evolution of hexaploid wheat. Theor Appl Genet 97:657–670. CrossRefGoogle Scholar
  13. Edae EA, Byrne PF, Haley SD, Lopes MS, Reynolds MP (2014) Genome-wide association mapping of yield and yield components of spring wheat under contrasting moisture regimes. Theor Appl Genet 127:791–807. CrossRefPubMedGoogle Scholar
  14. Emebiri LC, Oliver JR, Mrva K, Mares D (2010) Association mapping of late maturity α-amylase (LMA) activity in a collection of synthetic hexaploid wheat. Mol Breed 26:39–49. CrossRefGoogle Scholar
  15. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620. CrossRefGoogle Scholar
  16. Ewens WJ, Spielman RS (1995) The transmission/disequilibrium test: history, subdivision, and admixture. Am J Hum Genet 57:455–464CrossRefPubMedPubMedCentralGoogle Scholar
  17. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Addison Wesley Longman, HarlowGoogle Scholar
  18. Galili T (2015) Dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering. Bioinformatics 31(22):3718–3720CrossRefPubMedPubMedCentralGoogle Scholar
  19. Gao L, Turner MK, Chao S, Kolmer J, Anderson JA (2016) Genome wide association study of seedling and adult plant leaf rust resistance in elite spring wheat breeding lines. PLoS ONE 11:1–25. CrossRefGoogle Scholar
  20. Gu Z, Gu L, Eils R, Schlesner M, Brors B (2014) Circlize implements and enhances circular visualization in R. Bioinformatics 30:2811–2812. CrossRefPubMedPubMedCentralGoogle Scholar
  21. Jafarzadeh J, Bonnett D, Jannink JL, Akdemir D, Dreisigacker S, Sorrells ME (2016) Breeding value of primary synthetic wheat genotypes for grain yield. PLoS ONE 11:1–24. CrossRefGoogle Scholar
  22. Lage J, Warburton ML, Crossa J, Skovmand B, Andersen SB (2003) Assessment of genetic diversity in synthetic hexaploid wheats and their Triticum dicoccum and Aegilops tauschii parents using AFLPs and agronomic traits. Euphytica 134:305–317. CrossRefGoogle Scholar
  23. Li J, Wan S, Yang Y (2014) Synthetic hexaploid wheat enhances variation and adaptive evolution of bread wheat in breeding processes. J Syst Evol 52:735–742. CrossRefGoogle Scholar
  24. Lin M, Zhang D, Liu S, Zhang G, Yu J, Fritz AK, Bai G (2016) Genome-wide association analysis on pre-harvest sprouting resistance and grain color in U.S. winter wheat. BMC Genom 17:794. CrossRefGoogle Scholar
  25. Lu H, Redus MA, Coburn JR, Rutger JN, McCouch SR, Tai TH (2005) Population structure and breeding patterns of 145 U.S. rice cultivars based on SSR marker analysis. Crop Sci 45:66–76. CrossRefGoogle Scholar
  26. Mammadov J, Aggarwal R, Buyyarapu R, Kumpatla S (2012) SNP markers and their impact on plant breeding. Int J Plant Genom 2012:1–11. CrossRefGoogle Scholar
  27. Marcussen T, Sandve SR, Heier L, Spannagl M, Pfeifer M, Jakobsen KS, Wulff BBH, Steuernagel B, Mayer KFX, Olsen O-A, Rogers J, el Dole J, Pozniak C, Eversole K, Feuillet C, Gill B, Friebe B, Lukaszewski AJ, Sourdille P et al (2014) Ancient hybridizations among the ancestral genomes of bread wheat. Science 345:1250092. CrossRefPubMedGoogle Scholar
  28. McFadden ES (1944) The artificial synthesis of Triticum spelta. Rec Genet Soc Am 13:26–27Google Scholar
  29. Mulki MA, Jighly A, Ye G, Emebiri LC, Moody D, Ansari O, Ogbonnaya FC (2013) Association mapping for soilborne pathogen resistance in synthetic hexaploid wheat. Mol Breed 31:299–311. CrossRefGoogle Scholar
  30. Müllner D (2013) fastcluster: fast hierarchical, agglomerative clustering routines for R and Python. J Stat Softw 53:1–18CrossRefGoogle Scholar
  31. Nei M (1977) F-statistics and analysis of gene diversity in subdivided populations. Ann Hum Genet 41:225–233. CrossRefPubMedGoogle Scholar
  32. Nei M (1978) Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89:583–590. CrossRefPubMedPubMedCentralGoogle Scholar
  33. Neumann K, Kobiljski B, Denčić S, Varshney RK, Börner A (2011) Genome-wide association mapping: a case study in bread wheat (Triticum aestivum L.). Mol Breed 27:37–58. CrossRefGoogle Scholar
  34. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959. CrossRefPubMedPubMedCentralGoogle Scholar
  35. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ, Sham PC (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575. CrossRefPubMedPubMedCentralGoogle Scholar
  36. Qadir A, Ilyas M, Akhtar W, Aziz E, Rasheed A, Mahmood T (2015) Study of genetic diversity in synthetic hexaploid wheats using random amplified polymorphic DNA. J Anim Plant Sci 25:1660–1666Google Scholar
  37. R core Team (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  38. Schut JW, Qi X, Stam P (1997) Association between relationship measures based on AFLP markers, pedigree data and morphological traits in barley. Theor Appl Genet 95:1161–1168. CrossRefGoogle Scholar
  39. Sharma I, Tyagi BS, Singh G, Venkatesh K, Gupta OP (2015) Enhancing wheat production—a global perspective. Indian J Agric Sci 85:3–13Google Scholar
  40. Soleimani VD, Baum BR, Johnson DA (2002) AFLP and pedigree-based genetic diversity estimates in modern cultivars of durum wheat [Triticum turgidum L. ssp. durum (Desf.) Husn.]. Theor Appl Genet 104:350–357. CrossRefPubMedGoogle Scholar
  41. Sukumaran S, Dreisigacker S, Lopes M, Chavez P, Reynolds MP (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
  42. Trethowan RM, Mujeeb-Kazi A (2008) Novel germplasm resources for improving environmental stress tolerance of hexaploid wheat. Crop Sci 48:1255–1265. CrossRefGoogle Scholar
  43. Van Becelaere G, Lubbers EL, Paterson AH, Chee PW (2005) Pedigree- vs. DNA marker-based genetic similarity estimates in cotton. Crop Sci 45:2281. CrossRefGoogle Scholar
  44. van Inghelandt D, Melchinger AE, Lebreton C, Stich B (2010) Population structure and genetic diversity in a commercial maize breeding program assessed with SSR and SNP markers. Theor Appl Genet 120:1289–1299. CrossRefPubMedPubMedCentralGoogle Scholar
  45. Wang DG (1998) Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome. Science 280:1077–1082. CrossRefPubMedGoogle Scholar
  46. Wang S, Wong D, Forrest K, Allen A, Chao S, Huang BE, Maccaferri M, Salvi S, Milner SG, Cattivelli L, Mastrangelo AM, Whan A, Stephen S, Barker G, Wieseke R, Plieske J, Lillemo M, Mather D, Appels R 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
  47. Warburton ML, Crossa J, Franco J, Kazi M, Trethowan R, Rajaram S, Pfeiffer W, Zhang P, Dreisigacker S, Van Ginkel M (2006) Bringing wild relatives back into the family: recovering genetic diversity in CIMMYT improved wheat germplasm. Euphytica 149:289–301. CrossRefGoogle Scholar
  48. Yu J, Pressoir G, Briggs WH, Vroh Bi I, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen DM, Holland JB, Kresovich S, Buckler ES (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38:203–208. CrossRefPubMedGoogle Scholar
  49. Yu M, Chen G, Zhang L, Liu Y, Liu D, Wang J, Pu Z, Zhang L, Lan X, Wei Y, Liu C, Zheng Y (2014) QTL mapping for important agronomic traits in synthetic hexaploid wheat derived from Aegiliops tauschii ssp. tauschii. J Integr Agric 13:1835–1844. CrossRefGoogle Scholar
  50. Zegeye H, Rasheed A, Makdis F, Badebo A, Ogbonnaya FC (2014) Genome-wide association mapping for seedling and adult plant resistance to stripe rust in synthetic hexaploid wheat. PLoS ONE 9:e105593. CrossRefPubMedPubMedCentralGoogle Scholar
  51. Zhang P, Dreisigacker S, Melchinger AE, Reif JC, Mujeeb Kazi A, Van Ginkel M, Hoisington D, Warburton ML (2005) Quantifying novel sequence variation and selective advantage in synthetic hexaploid wheats and their backcross-derived lines using SSR markers. Mol Breed 15:1–10. CrossRefGoogle Scholar
  52. Zhang Z, Ersoz E, Lai C, Todhunter RJ, Tiwari HK, Gore MA, Bradbury PJ, Yu J, Arnett DK, Ordovas JM, Buckler ES (2010) Mixed linear model approach adapted for genome-wide association studies. Nat Genet 42:355–360. CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Department of Plant AgricultureUniversity of GuelphGuelphCanada
  2. 2.National Research Council CanadaSaskatoonCanada
  3. 3.Genetic Resources ProgramInternational Maize and Wheat Improvement Center (CIMMYT)TexcocoMexico

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