Theoretical and Applied Genetics

, Volume 131, Issue 9, pp 2009–2019 | Cite as

Unlocking historical phenotypic data from an ex situ collection to enhance the informed utilization of genetic resources of barley (Hordeum sp.)

  • Maria Y. González
  • Norman Philipp
  • Albert W. Schulthess
  • Stephan Weise
  • Yusheng Zhao
  • Andreas Börner
  • Markus Oppermann
  • Andreas Graner
  • Jochen C. ReifEmail author
Original Article


Key message Historical data generated during seed regeneration are valuable to populate a bio-digital resource center for barley (Hordeum sp.).


Precise estimates of trait performance of genetic resources are considered as an intellectually challenging, complex, costly and time-consuming step needed to exploit the phenotypic and genetic diversity maintained in genebanks for breeding and research. Using barley (Hordeum sp.) as a model, we examine strategies to tap into historical data available from regeneration trials. This is a first step toward extending the Federal ex situ Genebank into a bio-digital resource center facilitating an informed choice of barley accessions for research and breeding. Our study is based on historical data of seven decades collected for flowering time, plant height, and thousand grain weight during the regeneration of 12,872 spring and winter barley accessions. Linear mixed models were implemented in conjunction with routines for assessment of data quality. A resampling study highlights the potential risk of biased estimates in second-order statistics when grouping accessions for regeneration according to the year of collection or geographic origin. Based on rigorous quality assessment, we obtained high heritability estimates for the traits under consideration exceeding 0.8. Thus, the best linear unbiased estimations for the three traits are a valuable source to populate a bio-digital resource center for the IPK barley collection. The proposed strategy to leverage historical data from regeneration trials is not crop specific and can be used as a blueprint for other ex situ collections.



The Federal Ministry of Education and Research of Germany is acknowledged for funding (Grants FKZ031B0184A (AWS) and FKZ031B0190A (MYG)).

Author contribution statement

MYG, AG, YZ, NP, and JCR designed the study. MYG, AWS, and JCR wrote the manuscript. MYG, YZ, and NP designed and performed the computational experiments. SW, AB, and MO cleansed and compiled phenotypic data. All authors helped to improve the manuscript. All authors agree with the current statement.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical statement

All experiments were performed under the current laws of Germany.

Supplementary material

122_2018_3129_MOESM1_ESM.docx (1.6 mb)
Supplementary material 1 (DOCX 1596 kb)


  1. Austin RB, Bingham J, Blackwell RD, Evans LT, Ford MA, Morgan CL, Taylor M (1980) Genetic improvements in winter wheat yields since 1900 and associated physiological changes. J Agric Sci 94(3):675–689. CrossRefGoogle Scholar
  2. Balzarini M (2002) Applications of mixed models in plant breeding. In: Kang MS (ed) Quantitative genetics, genomics, and plant breeding. CABI Publishing, New York, pp 353–363Google Scholar
  3. Bernal-Vasquez AM, Utz HF, Piepho HP (2016) Outlier detection methods for generalized lattices: a case study on the transition from ANOVA to REML. Theor Appl Genet 129(4):787–804. CrossRefPubMedGoogle Scholar
  4. Bernardo R (1994) Prediction of maize single-cross performance using RFLPs and information from related hybrids. Crop Sci 34:20–25. CrossRefGoogle Scholar
  5. Bernardo R (1996) Best linear unbiased prediction of maize single-cross performance. Crop Sci 36:50–56. CrossRefGoogle Scholar
  6. Bischl B, Mersmann O, Trautmann H, Weihs C (2012) Resampling methods for meta-model validation with recommendations for evolutionary computation. Evol Comput 20(2):249–275. CrossRefPubMedGoogle Scholar
  7. Borlaug NE (1968) Wheat breeding and its impact on world food supply. In: Finley KW, Sheppard KW (eds) Proceedings of 3rd international wheat genetics symposium. Australian Academy of Sciences, Canberra, pp 1–36Google Scholar
  8. Börner A (2006) Preservation of plant genetic resources in the biotechnology era. Biotechnol J 1:1393–1404. CrossRefPubMedGoogle Scholar
  9. Brancourt-Hulmel M, Doussinault G, Lecomte C, Bérard B, Le Buanec B, Trottet M (2003) Genetic improvement of agronomic traits of winter wheat cultivars released in France from 1946 to 1992. Crop Sci 43:37–45. CrossRefGoogle Scholar
  10. Butler DG, Cullis BR, Gilmour AR, Gogel BJ (2009) ASReml-R reference manual, release 3.0. Queensland Department of Primary Industries, BrisbaneGoogle Scholar
  11. Crossa J, Jarquin D, Franco J, Pérez-Rodríguez P, Burgueño J, Saint-Pierre C, Vikram P, Sansaloni C, Petroli C, Akdemir D, Sneller C, Reynolds M, Tattaris M, Payne T, Guzman C, Peña RJ, Wenzl P, Singh S (2016) Genomic prediction of gene bank wheat landraces. G3 6:1819–1834. CrossRefPubMedGoogle Scholar
  12. Davies LR, Allender CJ (2017) Who is sowing our seeds? A systematic review of the use of plant genetic resources in research. Genet Resour Crop Evol 64:1999–2008. CrossRefGoogle Scholar
  13. de Carvalho MAAP, Bebeli PJ, Bettencourt E, Costa G, Dias S, Dos Santos TMM, Slaski JJ (2013) Cereal landraces genetic resources in worldwide GeneBanks: a review. Agron Sustain Dev 33:177–203. CrossRefGoogle Scholar
  14. Desta ZA, Ortiz R (2014) Genomic selection: genome-wide prediction in plant improvement. Trends Plant Sci 19(9):592–601. CrossRefPubMedGoogle Scholar
  15. Endresen DTF, Street K, Mackay M, Bari A, De Pauw E (2011) Predictive association between biotic stress traits and eco-geographic data for wheat and barley landraces. Crop Sci 51:2036–2055. CrossRefGoogle Scholar
  16. Estaghvirou SBO, Ogutu JO, Piepho HP (2014) Influence of outliers on accuracy estimation in genomic prediction in plant breeding. G3 4:2317–2328. CrossRefPubMedGoogle Scholar
  17. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Longman, HarlowGoogle Scholar
  18. FAO (2010) The second report on the state of the world’s plant genetic resources for food and agriculture. Commission on genetic resources for food and agriculture, food and agriculture organization of the United Nations, Rome. Accessed 12 Dec 2017
  19. FAO (2017) Food outlook: biannual report on global food markets. Accessed 12 Dec 2017
  20. Ford B, Deng W, Clausen J, Oliver S, Boden S, Hemming M, Trevaskis B (2016) Barley (Hordeum vulgare) circadian clock genes can respond rapidly to temperature in an EARLY FLOWERING 3-dependet manner. J Exp Bot 67:5517–5528. CrossRefPubMedPubMedCentralGoogle Scholar
  21. Global Crop Diversity Trust (2008) Global strategy for the ex situ conservation and use of barley germplasm. Accessed 12 Dec 2017
  22. Graebner RC, Hayes PM, Hagerty CH, Cuesta-Marcos A (2016) A comparison of polymorphism information content and mean of transformed kinships as criteria for selecting informative subsets of barley (Hordeum vulgare L. s. l.) from the USDA Barley Core Collection. Genet Resour Crop Evol 63(3):477–482. CrossRefGoogle Scholar
  23. Grausgruber H, Bointner H, Tumpold R, Ruckenbauer P, Fischbeck G (2002) Genetic improvement of agronomic and qualitative traits of spring barley. Plant Breed 121:411–416. CrossRefGoogle Scholar
  24. Hartung K, Piepho HP, Knüpffer H (2006) Analysis of genebank evaluation data by using geostatistical methods. Genet Resour Crop Evol 53:737–751. CrossRefGoogle Scholar
  25. Haseneyer G, Stracke S, Paul C, Einfeldt C, Broda A, Piepho HP, Graner A, Geiger HH (2010) Population structure and phenotypic variation of a spring barley world collection set up for association studies. Plant Breed 129:271–279. CrossRefGoogle Scholar
  26. He S, Schulthess AW, Mirdita V, Zhao Y, Korzun V, Bothe R, Ebmeyer E, Reif JC, Jiang Y (2016) Genomic selection in a commercial winter wheat population. Theor Appl Genet 129:641–651. CrossRefPubMedGoogle Scholar
  27. Heffner EL, Lorenz JA, Jannink J, Sorrells ME (2010) Plant breeding with genomic selection: gain per unit time and cost. Crop Sci 50:1681–1690. CrossRefGoogle Scholar
  28. Henderson CR (1975) Best linear unbiased estimation and prediction under a selection model. Biometrics 31:423–447CrossRefPubMedGoogle Scholar
  29. Jarquin D, Specht J, Lorenz A (2016) Prospects of genomic prediction in the USDA soybean germplasm collection: historical data creates robust models for enhancing selection of accessions. G3 6(8):2329–2341. CrossRefPubMedGoogle Scholar
  30. Jin L, Lu Y, Xiao P, Sun M, Corke H, Bao J (2010) Genetic diversity and population structure of a diverse set of rice germplasm for association mapping. Theor Appl Genet 121(3):475–487. CrossRefPubMedGoogle Scholar
  31. Keilwagen J, Kilian B, Özkan H, Babben S, Perovic D, Mayer KFX, Walther A, Poskar CH, Ordon F, Eversole K, Börner A, Ganal M, Knüpffer H, Graner A, Friedel S (2014) Separating the wheat from the chaff—a strategy to utilize plant genetic resources from ex situ genebanks. Sci Rep 4:5231. CrossRefPubMedPubMedCentralGoogle Scholar
  32. Kilian B, Graner A (2012) NGS technologies for analyzing germplasm diversity in genebanks. Brief Funct Genomics 11(1):38–50. CrossRefPubMedPubMedCentralGoogle Scholar
  33. Krajewski P, Chen D, Ćwiek H, van Dijk ADJ, Fiorani F, Kersey P, Klukas C, Lange M, Markiewicz A, Nap JP, Oeveren JV, Pommier C, Scholz U, Schriek MV, Usadel B, Weise S (2015) Towards recommendations for metadata and data handling in plant phenotyping. J Exp Bot 66:5417–5427. CrossRefPubMedGoogle Scholar
  34. Laidig F, Piepho HP, Drobek T, Meyer U (2014) Genetic and non-genetic long-term trends of 12 different crops in German official variety performance trials and on-farm yield trends. Theor Appl Genet 127:2599–2617. CrossRefPubMedPubMedCentralGoogle Scholar
  35. Lorenz AJ, Chao S, Asoro FG, Heffner EL, Hayashi T, Hiroyoshi I, Smith KP, Sorrells ME, Jannink JL (2011) Genomic selection in plant breeding: knowledge and prospects. Adv Agron 110:77–123. CrossRefGoogle Scholar
  36. Malysheva-Otto LV, Ganal MW, Röder MS (2006) Analysis of molecular diversity, population structure and linkage disequilibrium in a worldwide survey of cultivated barley germplasm (Hordeum vulgare L.). BMC Genet 7:6. CrossRefPubMedPubMedCentralGoogle Scholar
  37. McCouch S, Baute GJ, Bradeen J, Bramel P, Bretting PK, Buckler E, Burke JM, Charest D, Cloutier S, Cole G, Dempewolf H, Dingkuhn M, Feuillet C, Gepts P, Grattapaglia D, Guarino L, Jackson S, Knapp S, Langridge P, Lawton-Rauh A, Lijua Q, Lusty C, Michael T, Myles S, Naito K, Nelson RL, Pontarollo R, Richards CM, Rieseberg L, Ross-Ibarra J, Rounsley S, Hamilton RS, Schurr U, Stein N, Tomooka N, van der Knaap E, van Tassel D, Toll J, Valls J, Varshney RK, Ward J, Waugh R, Wenzl P, Zamir D (2013) Agriculture: feeding the future. Nature 499:23–24. CrossRefPubMedGoogle Scholar
  38. McKevith B (2004) Nutritional aspects of cereals. Nutr Bull 29(2):111–142. CrossRefGoogle Scholar
  39. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4):1819–1829PubMedPubMedCentralGoogle Scholar
  40. Muleta KT, Bulli P, Zhang Z, Chen X, Pumphrey M (2017) Unlocking diversity in germplasm collections via genomic selection: a case study based on quantitative adult plant resistance to stripe rust in spring wheat. Plant Genome 10(3):1–15. CrossRefGoogle Scholar
  41. Patterson H, Thompson R (1971) Recovery of inter-block information when block sizes are unequal. Biometrika 58(3):545–554. CrossRefGoogle Scholar
  42. Pauli D, Muehlbauer GJ, Smith KP, Cooper B, Hole D, Obert DE, Ullrich SE, Blake TK (2014) Association mapping of agronomic QTLs in US spring barley breeding germplasm. Plant Genome 7:1–15. CrossRefGoogle Scholar
  43. Philipp N, Liu G, Zhao Y, He S, Spiller M, Stiewe G, Pillen K, Reif JC, Li Z (2016) Genomic prediction of barley hybrid performance. Plant Genome. PubMedCrossRefGoogle Scholar
  44. Piepho HP, Möhring J (2006) Selection in cultivar trials—Is it ignorable? Crop Sci 46:192–201. CrossRefGoogle Scholar
  45. Piepho HP, Möhring J (2007) Computing heritability and selection response from unbalanced plant breeding trials. Genetics 177(3):1881–1888. CrossRefPubMedPubMedCentralGoogle Scholar
  46. Piepho HP, Büchse A, Emrich K (2003) A hitchhiker’s guide to mixed models for randomized experiments. J Agron Crop Sci 189:310–322. CrossRefGoogle Scholar
  47. R Core Team (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  48. Roa C, Hamilton RS, Wenzl P, Powell W (2016) Plant genetic resources: needs, rights, and opportunities. Trends Plant Sci 21(8):633–636. CrossRefPubMedGoogle Scholar
  49. Saade S, Maurer A, Shahid M, Oakey H, Schmöckel SM, Negrão S, Pillen K, Tester M (2016) Yield-related salinity tolerance traits identified in a nested association mapping (NAM) population of wild barley. Sci Rep 6:32586. CrossRefPubMedPubMedCentralGoogle Scholar
  50. Sato K, Flavell A, Russell J, Börner A, Valkoun J (2014) Genetic diversity and germplasm management: wild barley, landraces, breeding materials. In: Kumlehn J, Stein N (eds) Biotechnological approaches to barley improvement. Springer, Berlin, pp 3–20Google Scholar
  51. Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J Roy Stat Soc B 36(1):111–147Google Scholar
  52. Stracke S, Presterl T, Stein N, Perovic D, Ordon F, Graner A (2007) Effects of introgression and recombination on haplotype structure and linkage disequilibrium surrounding a locus encoding Bymovirus resistance in barley. Genetics 175:805–817. CrossRefPubMedPubMedCentralGoogle Scholar
  53. Thiel T, Michalek W, Varshney R, Granier A (2003) Exploiting EST databases for the development and characterization of gene-derived SSR-markers in barley (Hordeum vulgare L.). Theor Appl Genet 106:411–422. CrossRefPubMedGoogle Scholar
  54. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J (2017) 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet 101:5–22. CrossRefPubMedPubMedCentralGoogle Scholar
  55. Wrigley C (2017) The cereal grains: providing our food, feed and fuel needs. In: Wrigley C, Batey I, Miskelly D (eds) Cereal grains, 2nd edn. Elsevier, Oxford.
  56. Yan J, Warburton M, Crouch J (2011) Association mapping for enhancing maize (Zea mays L.) genetic improvement. Crop Sci 51(2):433–449. CrossRefGoogle Scholar
  57. Yu X, Li X, Guo T, Zhu C, Wu Y, Mitchell SE, Roozeboom KL, Wang D, Wang ML, Pederson GA, Tesso TT, Schnable PS, Bernardo R, Yu J (2016) Genomic prediction contributing to a promising global strategy to turbocharge gene banks. Nat Plants 2:16150. CrossRefPubMedGoogle Scholar
  58. Zakhrabekova S, Gough SP, Braumann I, Müller AH, Lundqvist J, Ahmann K, Dockter C, Matyszczak I, Kurowska M, Druka A, Waugh R, Graner A, Stein N, Steuernagel B, Lundqvist U, Hansson M (2012) Induced mutations in circadian clock regulator Mat—a facilitated short-season adaptation and range extension in cultivated barley. Proc Natl Acad Sci USA 109:4326–4331. CrossRefPubMedGoogle Scholar
  59. Zhao Y, Mette MF, Gowda M, Longin CFH, Reif JC (2014) Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat. Heredity 112:638–645. CrossRefPubMedPubMedCentralGoogle Scholar
  60. Zhong S, Dekkers JCM, Fernando RL, Jannink JL (2009) Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a barley case study. Genetics 182:355–364. CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Maria Y. González
    • 1
  • Norman Philipp
    • 1
  • Albert W. Schulthess
    • 1
  • Stephan Weise
    • 2
  • Yusheng Zhao
    • 1
  • Andreas Börner
    • 2
  • Markus Oppermann
    • 2
  • Andreas Graner
    • 2
  • Jochen C. Reif
    • 1
    Email author
  1. 1.Department of Breeding ResearchLeibniz Institute of Plant Genetics and Crop Plant Research (IPK)GaterslebenGermany
  2. 2.Department of GenebankLeibniz Institute of Plant Genetics and Crop Plant Research (IPK)GaterslebenGermany

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