Theoretical and Applied Genetics

, Volume 127, Issue 12, pp 2619–2633 | Cite as

Applying association mapping and genomic selection to the dissection of key traits in elite European wheat

  • Alison R. Bentley
  • Marco Scutari
  • Nicolas Gosman
  • Sebastien Faure
  • Felicity Bedford
  • Phil Howell
  • James Cockram
  • Gemma A. Rose
  • Tobias Barber
  • Jose Irigoyen
  • Richard Horsnell
  • Claire Pumfrey
  • Emma Winnie
  • Johannes Schacht
  • Katia Beauchêne
  • Sebastien Praud
  • Andy Greenland
  • David Balding
  • Ian J. Mackay
Original Paper

Abstract

Key message

We show the application of association mapping and genomic selection for key breeding targets using a large panel of elite winter wheat varieties and a large volume of agronomic data.

Abstract

The heightening urgency to increase wheat production in line with the needs of a growing population, and in the face of climatic uncertainty, mean new approaches, including association mapping (AM) and genomic selection (GS) need to be validated and applied in wheat breeding. Key adaptive responses are the cornerstone of regional breeding. There is evidence that new ideotypes for long-standing traits such as flowering time may be required. In order to detect targets for future marker-assisted improvement and validate the practical application of GS for wheat breeding we genotyped 376 elite wheat varieties with 3,046 DArT, single nucleotide polymorphism and gene markers and measured seven traits in replicated yield trials over 2 years in France, Germany and the UK. The scale of the phenotyping exceeds the breadth of previous AM and GS studies in these key economic wheat production regions of Northern Europe. Mixed-linear modelling (MLM) detected significant marker-trait associations across and within regions. Genomic prediction using elastic net gave low to high prediction accuracies depending on the trait, and could be experimentally increased by modifying the constituents of the training population (TP). We also tested the use of differentially penalised regression to integrate candidate gene and genome-wide markers to predict traits, demonstrating the validity and simplicity of this approach. Overall, our results suggest that whilst AM offers potential for application in both research and breeding, GS represents an exciting opportunity to select key traits, and that optimisation of the TP is crucial to its successful implementation.

Supplementary material

122_2014_2403_MOESM1_ESM.docx (807 kb)
Supplementary material 1 (DOCX 807 kb)

References

  1. Allen AM, Barker GLA, Wilkinson P, Burridge A, Winfield M, Coghill J, Uauy C, Griffiths S, Jack P, Berry S et al (2012) Discovery and development of exome-based, co-dominant single nucleotide polymorphism markers in hexaploid wheat (Triticum aestivum L.). Plant Biotech J 11:279–295CrossRefGoogle Scholar
  2. Båga M, Chodaparambi SV, Limin AE, Pecar M, Fowler DB, Chibbar RN (2007) Identification of quantitative trait loci and associated candidate genes for low-temperature tolerance in cold-hardy winter wheat. Funct Integr Genomics 7:53–68PubMedCrossRefGoogle Scholar
  3. Balyan HS, Gupta PK, Kumar S, Dhariwal R, Jaiswal V, Tyagi S, Agarwal P, Gahlaut V, Kumari S (2013) Genetic improvement of grain protein content and other health-related constituents of wheat grain. Plant Breed. doi:10.1111/pbr.12047 Google Scholar
  4. Beales J, Turner A, Griffiths S, Snape JW, Laurie DA (2007) A Pseudo-Response Regulator is misexpressed in the photoperiod insensitive Ppd-D1a mutant of wheat (Triticum aestivum L.). Theor Appl Genet 115:721–733PubMedCrossRefGoogle Scholar
  5. Beddington J (2009) Food, energy, water and the climate: a perfect storm of global events? http://www.dius.gov.uk/assets/goscience/docs/p/perfect-storm-paper.pdf. Accessed 9 Jan 2014
  6. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B57:289–300Google Scholar
  7. Bennett D, Izanloo A, Reynolds M, Kuchel H, Langridge P, Schnurbusch T (2012a) Genetic dissection of grain yield and physical grain quality in bread wheat (Triticum aestivum L.) under water-limited environments. Theor Appl Genet 125:255–271PubMedCrossRefGoogle Scholar
  8. Bennett D, Reynolds M, Mullan D, Izanloo A, Kuchel H, Langridge P, Schnurbusch T (2012b) Detection of two major grain yield QTL in bread wheat (Triticum aestivum L.) under heat, drought and high yield potential environments. Theor Appl Genet 125:1473–1485PubMedCrossRefGoogle Scholar
  9. Bentley AR, Turner AS, Gosman N, Leigh FJ, Maccaferri M, Dreisigacker S, Greenland A, Laurie DA (2011) Frequency of the photoperiod-insensitive Ppd-A1a alleles in tetraploid, hexaploid and synthetic hexaploid wheat germplasm. Plant Breed 130:10–15CrossRefGoogle Scholar
  10. Bentley AR, Horsnell R, Werner CP, Turner AS, Rose GA, Bedard C, Howell P, Wilhelm EP, Mackay IJ, Howells RM et al (2013) Short, natural, and extended photoperiod response in BC2F4 lines of bread wheat with different Photoperiod-1 (Ppd-1) alleles. Exp Bot 64:1783–1793CrossRefGoogle Scholar
  11. Bernardo R (2009) Genomewide selection for rapid introgression of exotic germplasm in maize. Crop Sci 49:419–425CrossRefGoogle Scholar
  12. Bodmer WF (1987) Human genetics: the molecular challenge. BioEssays 7:41–45PubMedCrossRefGoogle Scholar
  13. Borrell A, Incoll LD, Dalling MJ (1990) The influence of the Rht 1 and Rht 2 alleles on the growth of wheat stems and ears. Ann Bot 67:103–110Google Scholar
  14. Carter AH, Santra DK, Kidwell KK (2012) Assessment of the effects of the Gpc-B1 allele on senescence rate, grain protein concentration and mineral content in hard red spring wheat (Triticum aestivum L.) from the Pacific Northwest Region of the USA. Plant Breed 131:62–68CrossRefGoogle Scholar
  15. Clark SA, Hickey JM, van der Werf JHJ (2011) Different models of genetic variation and their effect on genomic evaluation. Genet Sel Evol 43:18PubMedCentralPubMedCrossRefGoogle Scholar
  16. Crossa J, Burgueno J, Dreisigacker S, Vargas M, Herrera-Foessel SA, Lillemo M, Singh RP, Trethowan R, Warburton M, Franco J et al (2007) Association analysis of historical bread wheat germplasm using additive genetic covariance of relatives and population structure. Genetics 177:1889–1913PubMedCentralPubMedCrossRefGoogle Scholar
  17. Davison CA, Hinkley DV (1997) Bootstrap methods and their applications. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  18. de Froidmont D (1998) A co-dominant marker for the 1BL/1RS wheat-rye translocation via multiplex PCR. J Cereal Sci 27:229–232CrossRefGoogle Scholar
  19. Diaz A, Zikhali M, Turner AS, Isaac P, Laurie DA (2012) Copy number variation affecting the Photoperiod-B1 and Vernalisation-A1 genes is associated with altered flowering time in wheat (Triticum aestivum). PLoS ONE 7:e33234PubMedCentralPubMedCrossRefGoogle Scholar
  20. Distelfeld A, Uauy C, Olmos S, Schlatter AR, Dubcovsky J, Fahima T (2004) Microcolinearity between a 2-cM region encompassing the grain protein content locus Gpc-6B1 on wheat chromosome 6B and a 350-kb region on rice chromosome 2. Funct Integr Genomics 4:59–66PubMedCrossRefGoogle Scholar
  21. Distelfeld A, Uauy C, Fahima T, Dubcovsky J (2006) Physical map of the wheat high-grain protein content gene Gpc-B1 and development of a high-throughput molecular marker. New Phytol 169:753–763PubMedCrossRefGoogle Scholar
  22. Ellis MH, Spielmeyer W, Gale KR, Rebetzke GJ, Richards RA (2002) “Perfect” markers for the Rht-B1b and Rht-D1b dwarfing genes in wheat. Theor Appl Genet 105:1038–1042PubMedCrossRefGoogle Scholar
  23. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620PubMedCrossRefGoogle Scholar
  24. Fu D, Szuchs P, Yan L, Helguera M, Skinner JS, von Zitzewitz J, Hayes PM, Dubcovsky J (2005) Large deletions within the first intron in VRN-1 are associated with spring growth habit in barley and wheat. Mol Genet Genomics 273:54–65PubMedCrossRefGoogle Scholar
  25. Fulton TM, Chunwongse J, Tanksley SD (1995) Microprep protocol for extraction of DNA from tomato and other herbaceous plants. Plant Mol Biol Rep 13:207–209CrossRefGoogle Scholar
  26. Goeman JJ, Meijer R, Chaturvedi N (2012) Penalized. R package version 0.9-41. http://cran.r-project.org/web/packages/penalized/penalized.pdf
  27. Griffiths S, Simmonds J, Leverington M, Wang Y, Fish L, Sayers L, Alibert L, Orford S, Wingen L, Herry L et al (2009) Meta-QTL analysis of the genetic control of ear emergence in elite European winter wheat germplasm. Theor Appl Genet 119:383–395PubMedCrossRefGoogle Scholar
  28. Groos C, Robert N, Bervas E, Charmet G (2003) Genetic analysis of grain protein-content, grain yield and thousand-kernel weight in bread wheat. Theor Appl Genet 106:1032–1040PubMedGoogle Scholar
  29. Habier D, Tetens J, Seefried F-R, Lichtner P, Thaller G (2010) The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genet Sel Evol 42:5PubMedCentralPubMedCrossRefGoogle Scholar
  30. Hastie T, Tibshirani R, Narasimhan B and Chu G (2013) Impute: Imputation for microarray data. R package version 1.34.0. http://master.bioconductor.org/packages/release/bioc/manuals/impute/man/impute.pdf
  31. Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME (2009) Genomic selection in dairy cattle: progress and challenges. J Dairy Sci 92:433–444PubMedCrossRefGoogle Scholar
  32. Hedden P (2003) The genes of the green revolution. Trends Genet 19:5–9PubMedCrossRefGoogle Scholar
  33. Hickey JM, Dreisigacker S, Crossa J, Hearne S, Babu R, Prasanna BM, Grondona M, Zambelli A, Windhausen VS, Mathews K, Gorjanc G (2014) Evaluation of genomic selection training population designs and genotyping strategies in plant breeding programs using simulation. Crop Sci 54:1476–1488CrossRefGoogle Scholar
  34. Hook SCW (1984) Specific weight and wheat quality. J Sci Food Agric 35:1136–1141CrossRefGoogle Scholar
  35. Huang XQ, Cloutier S, Lycar L, Radovanovic N, Humphreys DG, Noll JS, Somers DJ, Brown PD (2006) Molecular detection of QTLs for agronomic and quality traits in a doubled haploid population derived from two Canadian wheats (Triticum aestivum L.). Theor Appl Genet 113:753–766PubMedCrossRefGoogle Scholar
  36. Huang BE, George AW, Forrest KL, Kilian A, Hayden MJ, Morell MK, Cavanagh CR (2012) A multiparent advanced generation inter-cross population for genetic analysis in wheat. Plant Biotech J 10:826–839CrossRefGoogle Scholar
  37. Jannink JL (2007) Identifying quantitative trait locus by genetic background interactions in association studies. Genetics 176:553–561PubMedCentralPubMedCrossRefGoogle Scholar
  38. Joppa LR, Du C, Hart GE, Hareland GA (1997) Mapping gene(s) for grain protein in tetraploid wheat (Triticum turgidum L.) using a population of recombinant inbred chromosome lines. Crop Sci 37:1586–1589CrossRefGoogle Scholar
  39. Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, Eskin E (2008) Efficient control of population structure in model organism association mapping. Genetics 178:1709–1723PubMedCentralPubMedCrossRefGoogle Scholar
  40. Kato K, Miura H, Sawada S (2000) Mapping QTLs controlling grain yield and its components on chromosome 5A of wheat. Theor Appl Genet 101:1114–1121CrossRefGoogle Scholar
  41. Kibite S, Evans LE (1984) Causes of negative correlations between grain yield and grain protein concentration in common wheat. Euphytica 33:801–810CrossRefGoogle Scholar
  42. Korzun V, Roder MS, Ganal MW, Worland AJ, Law CN (1998) Genetic analysis of the dwarfing gene (Rht8) in wheat. Part I. Molecular mapping of Rht8 on the short arm of chromosome 2D of bread wheat. (Triticum aestivum L.). Theor Appl Genet 96:1104–1109CrossRefGoogle Scholar
  43. Lewis CM (2002) Genetic association studies: design, analysis and interpretation. Brief Bioinform 3:146–153PubMedCrossRefGoogle Scholar
  44. Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ, Gore M, Buckler ES, Zhang Z (2012) GAPIT: genome association and prediction integrated tool. Bioinformatics 28:2397–2399PubMedCrossRefGoogle Scholar
  45. Maccaferri M, Sanguineti MC, Demontis A, El-Ahmed A, Garcia del Moral L, Maalouf F, Nachit M, Nserallah N, Ouabbou H, Rhouma S et al (2011) Association mapping in durum wheat grown across a broad range of water regimes. J Exp Bot 62:409–438PubMedCrossRefGoogle Scholar
  46. Mackay I, Horwell A, Garner J, White J, McKee J, Philpott H (2011) Reanalyses of the historical series of UK variety trials to quantify the contributions of genetic and environmental factors to trends and variability in yield over time. Theor Appl Genet 122:225–238PubMedCrossRefGoogle Scholar
  47. Marchini JL (2013). Popgen: statistical and population genetics. R package version 1.0-3. http://cran.r-project.org/web/packages/popgen/popgen.pdf
  48. Marza F, Bai G-H, Carver BF, Zhou W-C (2006) Quantitative trait loci for yield and related traits in the wheat population Ning7840 x Clark. Theor Appl Genet 112:688–698PubMedCrossRefGoogle Scholar
  49. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829PubMedCentralPubMedGoogle Scholar
  50. Neumann K, Kobiljski B, Dencic S, Varshney RK, Börner A (2011) Genome-wide association mapping: a case study in bread wheat (Triticum aestivum L.). Mol Breed 27:37–58CrossRefGoogle Scholar
  51. Olmos S, Distelfeld A, Chicaiza O, Schlatter AR, Fahima T, Echenique V, Dubcovsky J (2003) Precise mapping of a locus affecting grain protein content in durum wheat. Theor Appl Genet 107:1243–1251PubMedCrossRefGoogle Scholar
  52. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedCentralPubMedGoogle Scholar
  53. Quarrie SA, Steed A, Calestani C, Semikhodskii A, Lebreton C, Chinoy C, Steele N, Pljevljakusic D, Waterman E, Weyen J et al (2005) A high-density genetic map of hexaploid wheat (Triticum aestivum L.) from the cross Chinese Spring x SQ1 and its use to compare QTLs for grain yield across a range of environments. Theor Appl Genet 110:865–880PubMedCrossRefGoogle Scholar
  54. Reif JC, Gowda M, Maurer HP, Longin CFH, Korzun V, Edmeyer E, Bothe R, Pietsch C, Würschum T (2011) Association mapping for quality traits in soft winter wheat. Theor Appl Genet 122:961–970PubMedCrossRefGoogle Scholar
  55. Segura V, Vilhjálmsson BJ, Platt A, Kortel A, Seren Ü, Long Q, Nordborg M (2012) An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat Genet 44:825–832PubMedCentralPubMedCrossRefGoogle Scholar
  56. Simmonds NW (1995) The relation between yield and protein in cereal grain. J Sci Food Agric 67:309–315CrossRefGoogle Scholar
  57. Snape JW, Foulkes MJ, Simmonds J, Leverington M, Fish LJ, Wang Y, Ciavarrella M (2007) Dissecting gene x environmental effects on wheat yields via QTL and physiological analysis. Euphytica 154:401–408CrossRefGoogle Scholar
  58. Sourdille P, Cadalen T, Guyomarch H, Snape JW, Perretant MR, Charmet G, Boeuf C, Bernard S, Bernard M (2003) An update of the Courtot x Chinese Spring intervarietal molecular marker linkage map for the QTL detection of agronomic traits in wheat. Theor Appl Genet 106:530–538PubMedGoogle Scholar
  59. Speed D, Balding DJ (2014) MultiBLUP: improved SNP-based prediction for complex traits. Genome Res. doi:10.1101/gr.169375.113 PubMedCentralPubMedGoogle Scholar
  60. Wilhelm EP, Turner AS, Laurie DA (2009) Photoperiod insensitive Ppd-A1a mutations in tetraploid wheat (Triticum durum Desf.). Theor Appl Genet 118:285–294PubMedCrossRefGoogle Scholar
  61. Wilhelm EP, Boulton MI, Al-Kaff N, Balfourier F, Bordes J, Greenland A, Powell W, Mackay IJ (2013) Rht-1 and Ppd-D1 associations with height, GA sensitivity, and days to heading in a worldwide bread wheat collection. Theor Appl Genet. doi:10.1007/s00122-013-2130-9 Google Scholar
  62. Wimmer V, Albrecht T, Auinger HJ, Schön CC (2012) synbreed: a framework for the analysis of genomic prediction data using R. Bioinformatics 18:2086–2087CrossRefGoogle Scholar
  63. Worland AJ (1996) The influence of flowering time genes on environmental adaptability in European wheat. Euphytica 89:49–57CrossRefGoogle Scholar
  64. Yan L, Helguera M, Kato K, Fukuyama S, Sherman J, Dubcovsky J (2004) Allelic variation at the VRN-1 promoter region in polyploidy wheat. Theor Appl Genet 118:1677–1686CrossRefGoogle Scholar
  65. Yan L, Fu C, Li C, Blechl A, Tranquilli G, Bonafede M, Sanchez A, Valarik M, Yasuda S, Dubcovsky J (2006) The wheat and barley vernalization gene VRN3 is an orthologue of FT. PNAS 103:19581–19586PubMedCentralPubMedCrossRefGoogle Scholar
  66. Zadoks JC, Chang TT, Konzak CF (1974) A decimal code for the growth stages of cereals. Weed Res 14:415–421Google Scholar
  67. Zhao K, Aranzana MJ, Kim S, Lister C, Shindo C, Tang C, Toomaijan C, Zheng H, Dean C, Marjoram P, Nordborg M (2007) An Arabidopsis example of association mapping in structured samples. PLoS Genet 3:e4Google Scholar
  68. Zhao Y, Gowda M, Wurschum T, Longin CFH, Korzun V, Kollers S, Schachschneider R, Zeng J, Fernando R, Dubcovsky J et al (2013) Dissecting the genetic architecture of frost tolerance in Central European winter wheat. J Exp Bot 64:4453–4460PubMedCentralPubMedCrossRefGoogle Scholar
  69. 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 112:638–645PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Alison R. Bentley
    • 1
  • Marco Scutari
    • 2
  • Nicolas Gosman
    • 1
  • Sebastien Faure
    • 3
  • Felicity Bedford
    • 1
  • Phil Howell
    • 1
  • James Cockram
    • 1
  • Gemma A. Rose
    • 1
  • Tobias Barber
    • 1
  • Jose Irigoyen
    • 1
  • Richard Horsnell
    • 1
  • Claire Pumfrey
    • 1
  • Emma Winnie
    • 1
  • Johannes Schacht
    • 4
  • Katia Beauchêne
    • 5
  • Sebastien Praud
    • 3
  • Andy Greenland
    • 1
  • David Balding
    • 2
  • Ian J. Mackay
    • 1
  1. 1.The John Bingham Laboratory, NIABCambridgeUK
  2. 2.Genetics InstituteUniversity College LondonLondonUK
  3. 3.BiogemmaChappesFrance
  4. 4.Limagrain GmbHPeine-RosenthalGermany
  5. 5.ARVALIS-Institut du végétalOuzouer Le MarchéFrance

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