Theoretical and Applied Genetics

, Volume 113, Issue 2, pp 206–224 | Cite as

Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize

  • G. Blanc
  • A. Charcosset
  • B. Mangin
  • A. Gallais
  • L. MoreauEmail author
Original Paper


Quantitative trait loci (QTL) detection experiments have often been restricted to large biallelic populations. Use of connected multiparental crosses has been proposed to increase the genetic variability addressed and to test for epistatic interactions between QTL and the genetic background. We present here the results of a QTL detection performed on six connected F2 populations of 150 F2:3 families each, derived from four maize inbreds and evaluated for three traits of agronomic interest. The QTL detection was carried out by composite interval mapping on each population separately, then on the global design either by taking into account the connections between populations or not. Epistatic interactions between loci and with the genetic background were tested. Taking into account the connections between populations increased the number of QTL detected and the accuracy of QTL position estimates. We detected many epistatic interactions, particularly for grain yield QTL (R 2 increase of 9.6%). Use of connections for the QTL detection also allowed a global ranking of alleles at each QTL. Allelic relationships and epistasis both contribute to the lack of consistency for QTL positions observed among populations, in addition to the limited power of the tests. The potential benefit of assembling favorable alleles by marker-assisted selection are discussed.


Quantitative Trait Locus Epistatic Interaction Epistatic Effect Quantitative Trait Locus Effect Allelic Effect 
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.



This research program was funded by INRA and Agriobtention. We are grateful to our colleagues involved in marker analyses at le Moulon (D. Madur, V. Combes, F. Dumas), and colleagues involved in material production and field testing at INRA le Moulon (P. Bertin, P. Jamin, D. Coubriche, S. Jouanne), at INRA Dreux, at INRA Rennes, at INRA Lusignan and at INRA Mons. We also thank the BIA unit at INRA Toulouse for the maintenance of MCQTL software (B. Mangin, B. Ngom, J. Marcel). We are grateful to Guy Decoux for the informatic program used for the graphical display of the maps. We are grateful to Rex Bernardo for very helpful advices on the writing of this manuscript.


  1. Asins MJ (2002) Present and future of quantitative trait locus analysis in plant breeding. Plant Breed 121:281–291CrossRefGoogle Scholar
  2. 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
  3. Bernardo R (2002) Quantitative traits in plants. Stemma, WoodburyGoogle Scholar
  4. 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
  5. Butron A, Velasco P, Ordas A, Malvar RA (2004) Yield evaluation of maize cultivars across environments with different levels of pink stem borer infestation. Crop Sci 44:741–747CrossRefGoogle Scholar
  6. Charcosset A, Causse M, Moreau L, Gallais A (1994) Investigation into the effect of genetic background on QTL expression using three recombinant inbred lines (RIL) populations. In: van Ooijen JW, Jansen J (eds) Biometrics in plant breeding: applications of molecular markers. CRPO-DLO, Wageningen, The Netherlands, pp 75–84Google Scholar
  7. Charcosset A, Mangin B, Moreau L, Combes L, Jourjon MF et al. (2000) Heterosis in maize investigated using connected RIL populations. In: Quantitative genetics and breeding methods: the way ahead. INRA, Paris, France, pp 89–98Google Scholar
  8. Chardon F, Virlon B, Moreau L, Falque M, Joets J et al (2004) Genetic architecture of flowering time in maize as inferred from QTL meta-analysis and synteny conservation with the rice genome. Genetics 168:2169–2185CrossRefPubMedGoogle Scholar
  9. Crepieux S, Lebreton C, Servin B, Charmet G (2004) IBD-based QTL detection in inbred pedigrees: a case study of cereal breeding programs—IBD-based multi-cross QTL mapping. Euphytica 137:101–109CrossRefGoogle Scholar
  10. Doebley J, Stec A, Hubbard L (1997) The evolution of apical dominance in maize. Nature 386:485–488CrossRefPubMedGoogle Scholar
  11. Eta-Ndu JT, Openshaw SJ (1999) Epistasis for grain yield in two F2 populations of maize. Crop Sci 39:346–352Google Scholar
  12. Haldane JBS (1919) The combination of linkage values and the calculation of distances between the loci of linked factors. J Genet 8:299–309CrossRefGoogle Scholar
  13. Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69:315–324PubMedGoogle Scholar
  14. Hinze LL, Lamkey KR (2003) Absence of epistasis for grain yield in elite maize hybrids. Crop Sci 43:46–56CrossRefGoogle Scholar
  15. Jannink JL, Jansen RC (2000) The diallel mating design for mapping interacting QTLs. In: Quantitative genetics and breeding methods: the way ahead. INRA, Paris, France, pp 81–88Google Scholar
  16. Jannink JL, Jansen R (2001) Mapping epistatic quantitative trait loci with one-dimensional genome searches. Genetics 157:445–454PubMedGoogle Scholar
  17. Jannink JL,Wu XL(2003) Estimating allelic number and identity in state of QTLs in interconnected families. Genet Res 81:133–144CrossRefPubMedGoogle Scholar
  18. Jansen RC (1993) Interval mapping of multiple quantitative trait loci. Genetics 135:205–211PubMedGoogle Scholar
  19. Jansen RC, Jannink JL, Beavis WD (2003) Mapping quantitative trait loci in plant breeding populations: use of parental haplotype sharing. Crop Sci 43:829–834CrossRefGoogle Scholar
  20. Jourjon MF, Jasson S, Marcel J, Ngom B, Mangin B (2005) MCQTL: multi-allelic QTL mapping in multi-cross design. Bioinformatics 21:128–130CrossRefPubMedGoogle Scholar
  21. Kearsey MJ, Farquhar AG (1998) QTL analysis in plants; where are we now? Heredity 80:137–142CrossRefPubMedGoogle Scholar
  22. Khavkin E, Coe E (1997) Mapped genomic locations for developmental functions and QTLs reflect concerted groups in maize (Zea mays L.). Theor Appl Genet 95:343–352CrossRefGoogle Scholar
  23. Knapp SJ, Stroup WW, Ross WM (1985) Exact confidence intervals for heritability on a progeny mean basis. Crop Sci 25:192–194CrossRefGoogle Scholar
  24. Kojima S, Takahashi Y, Kobayashi Y, Monna L, Sasaki T et al. (2002) Hd3a, a rice ortholog of the Arabidopsis FT gene, promotes transition to flowering downstream of Hd1 under short-day conditions. Plant Cell Physiol 43:1096–1105CrossRefPubMedGoogle Scholar
  25. Lamkey KR, Schnicker BJ, Melchinger AE (1995) Epistasis in an elite maize hybrid and choice of generation for inbred line development. Crop Sci 35:1272–1281CrossRefGoogle Scholar
  26. Lander ES, Green P, Abrahamson J, Barlow A, Daly MJ et al. (1987) MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1:174–181CrossRefPubMedGoogle Scholar
  27. Li RM, Lyons A, Wittenburg H, Paigen B,Churchill GA (2005) Combining data from multiple inbred line crosses improves the power and resolution of QTL mapping. Genetics: genetics 104.033993Google Scholar
  28. Liu J, Van Eck J, Cong B, Tanksley SD (2002) A new class of regulatory genes underlying the cause of pear-shaped tomato fruit. Proc Natl Acad Sci USA 99:13302–13306CrossRefPubMedGoogle Scholar
  29. Liu JP, Cong B,Tanksley SD (2003) Generation and analysis of an artificial gene dosage series in tomato to study the mechanisms by which the cloned quantitative trait locus fw2.2 controls fruit size. Plant Physiol 132:292–299CrossRefPubMedGoogle Scholar
  30. Lynch M, Walsh B (1998) Mapping QTLs: inbred line crosses—precision of ML estimates of QTL position. In: Sinauer Associates (ed) Genetics and analysis of quantitative traits. Sinauer, Sunderland, pp 448–450Google Scholar
  31. McKhann HI, Camilleri C, Berard A, Bataillon T, David JL et al. (2004) Nested core collections maximizing genetic diversity in Arabidopsis thaliana. Plant J 38:193–202CrossRefPubMedGoogle Scholar
  32. Melchinger AE, Schmidt WGHH (1988) Comparison of testcrosses produced from F2 and first backcross populations in maize. Crop Sci 28:743–749CrossRefGoogle Scholar
  33. 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
  34. Moreau L, Monod H, Charcosset A, Gallais A (1999) Marker-assisted selection with spatial analysis of unreplicated field trials. Theor Appl Genet 98:234–242CrossRefGoogle Scholar
  35. Moreau L, Charcosset A, Gallais A (2004) Use of trial clustering to study QTL*environment effects for grain yield and related traits in maize. Theor Appl Genet 110(1):92–105CrossRefPubMedGoogle Scholar
  36. Moreno-Gonzalez J, Dudley JW (1981) Epistasis in unrelated maize hybrids determined by three methods. Crop Sci 21:644–651CrossRefGoogle Scholar
  37. Muranty H (1996) Power of tests for quantitative trait loci detection using full-sib families in different schemes. Heredity 76:156–165CrossRefGoogle Scholar
  38. van Ooijen JW (1992) Accuracy of mapping quantitative trait loci in autogamous species. Theor Appl Genet 84:803–811CrossRefGoogle Scholar
  39. 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
  40. Rebai A, Goffinet B (2000) More about quantitative trait locus mapping with diallel designs. Genet Res 75:243–247CrossRefPubMedGoogle Scholar
  41. Rebai A, Goffinet B, Mangin B, Perret D (1994) QTL detection with diallel schemes. In: van Ooijen JW, Jansen J (eds) Biometrics in plant breeding: applications of molecular markers. CRPO-DLO, Wageningen, The Netherlands, pp 170–177Google Scholar
  42. Rebai A, Blanchard P, Perret D, Vincourt P (1997) Mapping quantitative trait loci controlling silking date in a diallel cross among four lines of maize. Theor Appl Genet 95:451–459CrossRefGoogle Scholar
  43. Salvi S, Sponza G, Morgante MFK, Meeley R, Ananiev E et al. (2005) The maize QTL Vgt1 corresponds to a ca. 2.7-kb non-coding region upstream of an Ap2-like gene. In: Maize meeting, Lake Geneva, Wisconsin, pp 195Google Scholar
  44. SAS (1989a) SAS IML user’s guide, Version 6.03, 4th edition. SAS Institute, CarryGoogle Scholar
  45. SAS (1989b) SAS procedures guide, Version 6, 3rd edition. SAS Institute, CarryGoogle Scholar
  46. SAS (1990) SAS/STAT user’s guide, Version 6, 4th edition. SAS Institute, CarryGoogle Scholar
  47. Servin B, Martin OC, Mezard M, Hospital F (2004) Toward a theory of marker-assisted gene pyramiding. Genetics 168:513–523CrossRefPubMedGoogle Scholar
  48. Storey JD, Tibshirani R (2003) Statistical significance for genome wide studies. PNAS 100:9440–9445CrossRefPubMedGoogle Scholar
  49. Takahashi Y, Shomura A, Sasaki T, Yano M (2001) Hd6, a rice quantitative trait locus involved in photoperiod sensitivity, encodes the alpha subunit of protein kinase CK2. Proc Natl Acad Sci USA 98:7922–7927CrossRefPubMedGoogle Scholar
  50. Wang DL, Zhu J, Li ZK, Paterson AH (1999) Mapping QTLs with epistatic effects and QTLxenvironment interactions by mixed linear model approaches. Theor Appl Genet 99:1255–1264CrossRefGoogle Scholar
  51. Xu SZ (1998) Mapping quantitative trait loci using multiple families of line crosses. Genetics 148:517–524PubMedGoogle Scholar
  52. Yi N, Xu S (2002) Mapping quantitative trait loci with epistatic effects. Genet Res 79:185–198CrossRefPubMedGoogle Scholar
  53. Yi N, Yandell BS, Churchill GA, Allison DB, Eisen EJ et al. (2005) Bayesian model selection for genome-wide epistatic quantitative trait loci analysis. Genetics 170:1333–1344CrossRefPubMedGoogle Scholar
  54. Zeng ZB (1994) Precision mapping of quantitative trait loci. Genetics 136:1457–1468PubMedGoogle Scholar
  55. Zhu H, Briceño G, Dovel R, Hayes PM, Liu BH et al. (1999) Molecular breeding for grain yield in Barley: an evaluation of QTL effects in a spring barley cross. Theor Appl Genet 98:772–779CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • G. Blanc
    • 1
  • A. Charcosset
    • 1
  • B. Mangin
    • 2
  • A. Gallais
    • 1
  • L. Moreau
    • 1
    Email author
  1. 1.INRA/INA-PG/UPS/CNRS, UMR de Génétique VégétaleGif sur YvetteFrance
  2. 2.INRA, Unité de Biométrie et Intelligence ArtificielleCastanet Tolosan CedexFrance

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