Molecular Breeding

, Volume 31, Issue 2, pp 279–287

Multiple-line cross quantitative trait locus mapping in sugar beet (Beta vulgaris L.)

  • Diana D. Schwegler
  • Wenxin Liu
  • Manje Gowda
  • Tobias Würschum
  • Britta Schulz
  • Jochen C. Reif
Article

Abstract

Linkage mapping based on multiple-line crosses is a promising strategy for mapping quantitative trait loci (QTL) underlying important agronomic traits. The main goal of this survey was to study the advantages of QTL mapping across versus within biparental populations using experimental data from three connected sugar beet (Beta vulgaris L.) populations evaluated for beet yield and potassium and sodium content. For the combined analysis across populations, we used two approaches for cofactor selection. In Model A, we assumed identical cofactors for every segregating population. In contrast, in Model B we selected cofactors specific for every segregating population. Model A performed better than Model B with respect to the number of QTL detected and the total proportion of phenotypic variance explained. The QTL analyses across populations revealed a substantially higher number of QTL compared to the analyses of single biparental populations. This clearly emphasizes the potential to increase QTL detection power with a joint analysis across biparental populations.

Keywords

QTL mapping Epistasis MC-QTL mapping Sugar beet 

Supplementary material

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Supplementary material 1 (DOC 25 kb)
11032_2012_9788_MOESM2_ESM.doc (110 kb)
Supplementary material 2 (DOC 110 kb)
11032_2012_9788_MOESM3_ESM.doc (4.9 mb)
Supplementary material 3 (DOC 5034 kb)

References

  1. Beavis WD, Grant D, Albertsen M, Fincher R (1991) Quantitative trait loci for plant height in four maize populations and their associations with qualitative genetic loci. Theor Appl Genet 83:141–145CrossRefGoogle Scholar
  2. 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
  3. Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ et al (2009) The genetic architecture of maize flowering time. Science 325:714–718PubMedCrossRefGoogle Scholar
  4. Cochran WG, Cox GM (1957) Experimental designs, 2nd edn. Wiley, New YorkGoogle Scholar
  5. Coles ND, McMullen MD, Balint-Kurti PJ, Pratt RC, Holland JB (2010) Genetic control of photoperiod sensitivity in maize revealed by joint multiple population analysis. Genetics 184:799–812PubMedCrossRefGoogle Scholar
  6. Doerge RW, Churchill GA (1996) Permutation tests for multiple loci affecting a quantitative character. Genetics 142:285–294PubMedGoogle Scholar
  7. Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Theor Appl Genet 103:601–606Google Scholar
  8. Hallden C, Hjerdin A, Rading IM, Sall T, Fridlundh B, Johannisdottir G, Tuvesson S, Akesson C, Nilsson N (1996) A high density RFLP linkage map of sugar beet. Genome 39:634–645PubMedCrossRefGoogle Scholar
  9. Holland JB, Portyanko VA, Hoffman DL, Lee M (2002) Genomic regions controlling vernalization and photoperiod responses in oat. Theor Appl Genet 105:113–126PubMedCrossRefGoogle Scholar
  10. Jannink J, Jansen R (2001) Mapping epistatic quantitative trait loci with one-dimensional genome searches. Genetics 157:445–454PubMedGoogle Scholar
  11. Jansen RC, Stam P (1994) High resolution of quantitative traits into multiple loci via interval mapping. Genetics 148:1203–1213Google Scholar
  12. Kosambi D (1944) The estimation of map distances from recombination values. Ann Eugen 12:172–175Google Scholar
  13. Lander ES, Botstein S (1989) Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121:185–199PubMedGoogle Scholar
  14. Liu W, Reif JC, Cossic F, Würschum T (2012) Comparison of biometrical approaches for QTL detection in multiple segregating populations. Theor Appl Genet 125:987–998PubMedCrossRefGoogle Scholar
  15. Medini M, Hamza S, Rebaï A, Baum M (2005) Analysis of genetic diversity in Tunisian durum wheat cultivars and related wild species by SSR and AFLP markers. Genet Resour Crop Evol 52:21–31CrossRefGoogle Scholar
  16. Mihaljevic R, Utz HF, Melchinger AE (2004) Congruency of quantitative trait loci detected for agronomic traits in testcrosses of five populations of European maize. Crop Sci 44:114–124CrossRefGoogle Scholar
  17. Muranty H (1996) Power of tests for quantitative trait loci detection using full-sib familiars in different schemes. Heredity 76:56–165CrossRefGoogle Scholar
  18. Negeri AT, Coles ND, Holland JB, Balint-Kurti PJ (2011) Mapping QTL controlling southern leaf blight resistance by joint analysis of three related recombinant inbred line populations. Crop Sci 51:1571–1579CrossRefGoogle Scholar
  19. Piepho HP (2000) Optimal marker density for interval mapping in a backcross population. Heredity 84:437–440PubMedCrossRefGoogle Scholar
  20. R Development Core Team (2010) R: a language and environment for statistical computing. Available at http://www.R-project
  21. Rebaï 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
  22. Rebaï A, Goffinet B (2000) More about quantitative trait locus mapping with diallel designs. Genet Res 75:243–247PubMedCrossRefGoogle Scholar
  23. Reif JC, Liu W, Gowda M, Maurer HP, Möhring J, Fischer S, Schechert A, Würschum T (2010) Genetic basis of agronomically important traits in sugar beet (Beta vulgaris L.) investigated with joint linkage association mapping. Theor Appl Genet 121:1489–1499PubMedCrossRefGoogle Scholar
  24. SAS Institute (2008) SAS/STAT 9.2 user’s guide. SAS Institute, CaryGoogle Scholar
  25. Schneider K, Kulosa D, Soerensen TR, Möhring S, Heine M, Durstewitz G, Polley A, Weber E, Jamsari, Lein J, Hohmann U, Tahiro E, Weisshaar B, Schulz B, Koch G, Jung C, Ganal M (2007) Analysis of DNA polymorphisms in sugar beet (Beta vulgaris L.) and development of an SNP-based map of expressed genes. Theor Appl Genet 115:601–615PubMedCrossRefGoogle Scholar
  26. Schumacher K, Schondelmaier J, Barzen E, Steinrücken G, Borchardt D, Weber WE, Jung C, Salamini F (1997) Combining different linkage maps in sugar beet (Beta vulgaris L.) to make one map. Plant Breed 116:23–38CrossRefGoogle Scholar
  27. Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464CrossRefGoogle Scholar
  28. Steinhoff J, Liu W, Maurer HP, Würschum T, Longin CFH (2011) Multiple-line cross QTL-mapping in European elite maize. Crop Sci 51:2505–2516CrossRefGoogle Scholar
  29. Steinhoff J, Liu W, Maurer HP, Würschum T, Longin CFH, Ranc N, Reif JC (2012a) Exploitation of elite maize (Zea mays L.) germplasm across maturity zones. Crop Sci 52:1534–1542. doi:10.2135/cropsci2011.10.0533 CrossRefGoogle Scholar
  30. Steinhoff J, Liu W, Reif JC, Porta GD, Ranc N, Würschum T (2012b) Detection of QTL for flowering time in multiple families of elite maize. Theor Appl Genet. doi:10.1007/s00122-012-1933-4 PubMedGoogle Scholar
  31. Utz HF, Melchinger AE, Schön CC (2000) Bias and sampling error of the estimated proportion of genotypic variance explained by quantitative trait loci determined from experimental data in maize using cross validation and validation with independent samples. Genetics 154:1839–1849PubMedGoogle Scholar
  32. van Ooijen JW, Voorrips RE (2001) JoinMap 3.0: Software for the calculation of genetic linkage maps. Plant Research International BV, WageningenGoogle Scholar
  33. Verhoeven KJF, Jannink J, McIntyre LM (2006) Using mating designs to uncover QTL and the genetic architecture of complex traits. Heredity 96:139–149PubMedCrossRefGoogle Scholar
  34. Visscher PM, Thompson R, Haley CS (1996) Confidence intervals in QTL mapping by bootstrapping. Genetics 143:1013–1020PubMedGoogle Scholar
  35. 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
  36. Weber WE, Borchardt DC, Koch G (2000) Marker analysis for quantitative traits in sugar beet. Plant Breed 119:97–106CrossRefGoogle Scholar
  37. Weißhaar B, Dohm JC, Minoche A, Schulz B, Kraft T, Wolf M, Holtgraewe D, Himmelbauer H (2011) The draft genome sequence of sugar beet (Beta vulgaris). Plant & Animal Genomes XIX Conference W563: Sugar BeetGoogle Scholar
  38. Würschum T (2012) Mapping QTL for agronomic traits in breeding populations. Theor Appl Genet 125:201–210PubMedCrossRefGoogle Scholar
  39. Würschum T, Maurer HP, Schulz B, Möhring J, Reif JC (2011a) Genome-wide association mapping reveals epistasis and genetic interaction networks in sugar beet. Theor Appl Genet 123:109–118PubMedCrossRefGoogle Scholar
  40. Würschum T, Maurer HP, Kraft T, Janssen G, Nilsson C, Reif JC (2011b) Genome-wide association mapping of agronomic traits in sugar beet. Theor Appl Genet 123:1121–1131PubMedCrossRefGoogle Scholar
  41. Würschum T, Liu W, Gowda M, Maurer HP, Fischer S, Schechert A, Reif JC (2012) Comparison of biometrical models for joint linkage association mapping. Heredity 108:332–340PubMedCrossRefGoogle Scholar
  42. Xu S (1998) Mapping quantitative trait loci using multiple families of line crosses. Genetics 148:517–524PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Diana D. Schwegler
    • 1
  • Wenxin Liu
    • 2
  • Manje Gowda
    • 1
  • Tobias Würschum
    • 1
  • Britta Schulz
    • 3
  • Jochen C. Reif
    • 1
  1. 1.State Plant Breeding InstituteUniversity of HohenheimStuttgartGermany
  2. 2.Crop Genetics and Breeding DepartmentChina Agricultural UniversityBeijingChina
  3. 3.KWS SAAT AGEinbeckGermany

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