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Theoretical and Applied Genetics

, Volume 117, Issue 7, pp 1167–1179 | Cite as

Association mapping in multiple segregating populations of sugar beet (Beta vulgaris L.)

  • Benjamin Stich
  • Albrecht E. Melchinger
  • Martin Heckenberger
  • Jens Möhring
  • Axel Schechert
  • Hans-Peter Piepho
Original Paper

Abstract

Association mapping in multiple segregating populations (AMMSP) combines high power to detect QTL in genome-wide approaches of linkage mapping with high mapping resolution of association mapping. The main objectives of this study were to (1) examine the applicability of AMMSP in a plant breeding context based on segregating populations of various size of sugar beet (Beta vulgaris L.), (2) compare different biometric approaches for AMMSP, and (3) detect markers with significant main effect across locations for nine traits in sugar beet. We used 768 F n (n = 2, 3, 4) sugar beet genotypes which were randomly derived from 19 crosses among diploid elite sugar beet clones. For all nine traits, the genotypic and genotype × location interaction variances were highly significant (P < 0.01). Using a one-step AMMSP approach, the total number of significant (P < 0.05) marker-phenotype associations was 44. The identification of genome regions associated with the traits under consideration indicated that not only segregating populations derived from crosses of parental genotypes in a systematic manner could be used for AMMSP but also populations routinely derived in plant breeding programs from multiple, related crosses. Furthermore, our results suggest that data sets, whose size does not permit analysis by the one-step AMMSP approach, might be analyzed using the two-step approach based on adjusted entry means for each location without losing too much power for detection of marker-phenotype associations.

Keywords

Quantitative Trait Locus Sugar Beet Quantitative Trait Locus Detection Allele Substitution Effect Testcross Progeny 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research was conducted within the Breeding and Informatics (BRAIN) project of the Genome Analysis of the Plant Biological System (GABI) initiative (http://www.gabi.de). The authors appreciate the editorial work of Dr. J. Muminović, whose suggestions considerably improved the style of the manuscript. The authors thank two anonymous reviewers for their valuable suggestions.

References

  1. Andersen JR, Lübberstedt T (2003) Functional markers in plants. Trends Plant Sci 8:554–560PubMedCrossRefGoogle Scholar
  2. Barzen E, Mechelke W, Ritter E, Seitzer JF, Salamini F (1992) RFLP markers for sugar beet breeding: chromosomal linkage maps and location of major genes for rhizomania resistance, monogermy and hypocotyl colour. Plant J 2:601–611CrossRefGoogle Scholar
  3. Barzen E, Mechelke W, Ritter E, Schulte-Kappert E, Salamini F (1995) An extended map of sugar beet genome containing RFLP and RAPD loci. Theor Appl Genet 90:189–193CrossRefGoogle Scholar
  4. Beavis WD (1994) The power and deceit of QTL-experiments: lessons from comparative QTL studies. In: 49th annual corn and sorghum industry research conference. American Seed Trade Association, Washington, DC, pp 250–266Google Scholar
  5. Blanc G, Charcosset A, Mangin B, Gallais A, Moreau L (2006) Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize. Theor Appl Genet 113:206–224PubMedCrossRefGoogle Scholar
  6. Boer MP, Wright D, Feng L, Podlich DW, Luo L, Cooper M, van Eeuwijk FA (2007) A mixed-model quantitative trait loci (QTL) analysis for multiple-environment trial data using environmental covariables for QTL-by-environment interactions, with an example in maize. Genetics 177:1801–1813PubMedCrossRefGoogle Scholar
  7. Bouchez A, Hospital F, Causse M, Gallais A, Charcosset A (2002) Marker-assisted introgression of favorable alleles at quantitative trait loci between maize elite inbred lines. Genetics 162:1945–1959PubMedGoogle Scholar
  8. Breseghello F, Sorrells ME (2006) Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics 172:1165–1177PubMedCrossRefGoogle Scholar
  9. Burba M, Puscz W (1976) Über die Verwendung von Aluminiumsalzen an Stelle von basischen Bleiacetaten zur Klärung von kalten wäßrigen Breiextrakten der Rübe. Z Zuckerindustrie 26:249–251Google Scholar
  10. Cai D, Kleine M, Kifle S, Harloff H-J, Sandal NN, Marcker KA, Klein-Lankhorst RM, Salentijn EMJ, Lange W, Stiekema WJ, Wyss U, Grundler FMW, Jung C (1997) Positional cloning of a gene for nematode resistance in sugar beet. Science 275:832–834PubMedCrossRefGoogle Scholar
  11. Chen L, Storey JD (2006) Relaxed significance criteria for linkage analysis. Genetics 173:2371–2381PubMedCrossRefGoogle Scholar
  12. Cockerham CC, Zeng Z-B (1996) Design III with marker loci. Genetics 143:1437–1456PubMedGoogle Scholar
  13. Crepieux S, Lebreton C, Servin B, Charmet G (2004) Quantitative trait loci (QTL) detection in multicross inbred designs: recovering QTL identical-by-descent status information from marker data. Genetics 168:1737–1748PubMedCrossRefGoogle Scholar
  14. Curnow RN (1988) The use of correlated information on treatment effects when selecting the best treatment. Biometrika 75:287–293CrossRefGoogle Scholar
  15. El-Mezway A, Dreyer F, Jacobs G, Jung C (2002) High-resolution mapping of the bolting gene B of sugar beet. Theor Appl Genet 105:100–105CrossRefGoogle Scholar
  16. Emrich K, Wilde F, Miedaner T, Piepho H-P (2008) REML approach for adjusting the Fusarium head blight rating to a phenological date in inoculated selection experiments of wheat. Theor Appl Genet 117:65–73PubMedCrossRefGoogle Scholar
  17. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Longman Group Ltd, LondonGoogle Scholar
  18. Flint-Garcia SA, Thornsberry JM, Buckler ES (2003) Structure of linkage disequilibrium in plants. Annu Rev Plant Biol 54:357–374PubMedCrossRefGoogle Scholar
  19. Gilmour AR, Gogel BJ, Cullis BR, Thompson R (2006) ASReml User Guide Release 2.0 VSN International Ltd, Hermel Hempstead, UKGoogle Scholar
  20. Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70Google Scholar
  21. Jung C, Hohmann U (2006) Establishment of a TILLING platform for sugar beet. In: Proceedings of the 14th plant and animal genome conference, San DiegoGoogle Scholar
  22. Kraakman ATW, Niks RE, Van den Berg PMMM, Stam P, Van Eeuwijk FA (2004) Linkage disequilibrium mapping of yield and yield stability in modern spring barley cultivars. Genetics 168:435–446PubMedCrossRefGoogle Scholar
  23. Lander ES, Botstein D (1989) Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121:185–199PubMedGoogle Scholar
  24. Lewis PO, Zaykin D (1999) Genetic data analysis. Computer program for the analysis of allelic data. Version 1.0.Google Scholar
  25. Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Sinauer Assoc., SunderlandGoogle Scholar
  26. Melchinger AE (1988) Means, variances, and covariances between relatives in hybrid populations with disequilibrium in the parent population. In: Weir BS, Eisen EJ, Goodman MM, Nomkoong G (eds) Proceedings of the 2nd international conference on quantitative genetics. Sinauer Associates, Raleigh, pp 400–415Google Scholar
  27. Melchinger AE, Utz HF, Schön CC (1998) Quantitative trait locus (QTL) mapping using different testers and independent population samples in maize reveals low power of QTL detection and larger bias in estimates of QTL effects. Genetics 149:383–403PubMedGoogle Scholar
  28. Moreau L, Charcosset A, Gallais A (2004) Use of trail clustering to study QTL × environment effects for grain yield and related traits in maize. Theor Appl Genet 110:92–105PubMedCrossRefGoogle Scholar
  29. Ozaki K, Ohnishi Y, Iida A, Sekine A , Yamada R, Tsunado T, Sato H, Hori M, Nakamura Y, Tanaka T (2002) Functional SNPs in the lymphotoxin-α gene that are associated with susceptibility to myocardial infarction. Nat Genet 32:650–654PubMedCrossRefGoogle Scholar
  30. Palaisa K, Morgante M, Tingey S, Rafalski A (2004) Long-range patterns of diversity and linkage disequilibrium surrounding the maize Y1 gene are indicative of an asymmetric selective sweep. PNAS (USA) 101:9885–9890CrossRefGoogle Scholar
  31. Piepho H-P (2000) A mixed model approach to mapping quantitative trait loci in barley on the basis of multiple environment data. Genetics 156:253–260Google Scholar
  32. Piepho H-P, Möhring J (2007) On weighting in two-stage analyses of series of experiments. Biul Oceny Odmian 32:109–212Google Scholar
  33. Piepho H-P, Pillen K (2004) Mixed-modelling for QTL × environment interaction analysis. Euphytica 137:147–153CrossRefGoogle Scholar
  34. Piepho H-P, Williams ER, Fleck M (2006) A note on the analysis of designed experiments with complex treatment structure. HortScience 41:446–452Google Scholar
  35. Pillen K, Steinrücken G, Wricke G, Herrmann RG, Jung C (1992) A linkage map of sugar beet (Beta vulgaris L.). Theor Appl Genet 84:129–135CrossRefGoogle Scholar
  36. Pillen K, Steinrücken G, Wricke G, Herrmann RG, Jung C (1993) A extended linkage map of sugar beet (Beta vulgaris L.) including nine putative lethal genes and restorer gene X. Plant Breed 111:265–272CrossRefGoogle Scholar
  37. Rebai A, Goffinet B (1993) Power of tests for QTL detection using replicated progenies derived from a diallel cross. Theor Appl Genet 86:1014–1022CrossRefGoogle Scholar
  38. SAS Institute (2004) SAS Version 9.1. SAS Institute, CaryGoogle Scholar
  39. Schneider K, Schäfer-Pregl R, Borchardt DC, Salamini F (2002) Mapping QTLs for sucrose content, yield and quality in a sugar beet population fingerprinted by EST-related markers. Theor Appl Genet 104:1107–1113PubMedCrossRefGoogle Scholar
  40. Schön CC, Utz HF, Groh S, Truberg B, Openshaw S, Melchinger AE (2004) Quantitative trait locus mapping based on resampling in a vast maize testcross experiment and its relevance to quantitative genetics for complex traits. Genetics 167:485–498PubMedCrossRefGoogle Scholar
  41. Searle SR, Casella G, and McCulloch CE (1992) Variance components. Wiley, New YorkGoogle Scholar
  42. Smith A, Cullis B, Gilmour A (2001) The analysis of crop variety evaluation data in Australia. Aust N Z J Stat 43:129–145CrossRefGoogle Scholar
  43. Stich B, Yu J, Melchinger AE, Piepho H-P, Utz HF, Maurer HP, Buckler ES (2007) Power to detect higher-order epistatic interactions in a metabolic pathway using a new mapping strategy. Genetics 176:563–570PubMedCrossRefGoogle Scholar
  44. Stich B, Möhring J, Piepho H-P, Heckenberger M, Buckler ES, Melchinger AE (2008) Comparison of mixed-model approaches for association mapping. Genetics 178:1745–1754PubMedCrossRefGoogle Scholar
  45. Utz HF, Melchinger AE (1994) Comparison of different approaches to interval mapping of quantitative trait loci. In: Van Ooijen JW, Jansen J (eds) Biometrics in plant breeding: applications of molecular markers. Proceedings of the ninth meeting of the EUCARPIA section biometrics in plant breeding. CPRO-DLO, Wageningen, NetherlandsGoogle Scholar
  46. Weber WE, Borchardt DC, Koch G (1999) Combined linkage maps and QTLs in sugar beet (Beta vulgaris L.) from different populations. Plant Breed 118:193–204CrossRefGoogle Scholar
  47. Weber WE, Borchardt DC, Koch G (2000) Marker analysis for quantitative traits in sugar beet. Plant Breed 119:97–106CrossRefGoogle Scholar
  48. Weir BS (1996) Genetic data analysis II, 2nd edn. Sinauer Associates, SunderlandGoogle Scholar
  49. Wilson LM, Whitt SR, Ibáñez AM, Rocheford TR, Goodman MM, Buckler ES (2004) Dissection of maize kernel composition and starch production by candidate gene association. Plant Cell 16:2719–2733PubMedCrossRefGoogle Scholar
  50. Yu J, Buckler E (2006) Genetic association mapping and genome organization of maize. Curr Opin Biotechnol 17:155–160PubMedGoogle Scholar
  51. Yu J, Holland JB, McMullen MD, Buckler ES (2008) Genetic design and statistical power of nested association mapping in maize. Genetics 178:539–551PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Benjamin Stich
    • 1
  • Albrecht E. Melchinger
    • 1
  • Martin Heckenberger
    • 1
  • Jens Möhring
    • 2
  • Axel Schechert
    • 3
  • Hans-Peter Piepho
    • 2
  1. 1.Institute for Plant Breeding, Seed Science, and Population GeneticsUniversity of HohenheimStuttgartGermany
  2. 2.Institute for Crop Production and Grassland ResearchUniversity of HohenheimStuttgartGermany
  3. 3.Fr. Strube Saatzucht GmbH & Co. KGSöllingenGermany

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