Theoretical and Applied Genetics

, Volume 115, Issue 8, pp 1029–1041 | Cite as

Genetic dissection of grain yield in bread wheat. I. QTL analysis

  • H. KuchelEmail author
  • K. J. Williams
  • P. Langridge
  • H. A. Eagles
  • S. P. Jefferies
Original Paper


Grain yield forms one of the key economic drivers behind a successful wheat (Triticum aestivum L.) cropping enterprise and is consequently a major target for wheat breeding programmes. However, due to its complex nature, little is known regarding the genetic control of grain yield. A doubled-haploid population, comprising 182 individuals, produced from a cross between two cultivars ‘Trident’ and ‘Molineux’, was used to construct a linkage map based largely on microsatellite molecular makers. ‘Trident’ represents a lineage of wheat varieties from southern Australia that has achieved consistently high relative grain yield across a range of environments. In comparison, ‘Molineux’ would be rated as a variety with low to moderate grain yield. The doubled-haploid population was grown from 2002 to 2005 in replicated field experiments at a range of environments across the southern Australian wheat belt. In total, grain yield data were recorded for the population at 18 site-year combinations. Grain yield components were also measured at three of these environments. Many loci previously found to be involved in the control of plant height, rust resistance and ear-emergence were found to influence grain yield and grain yield components in this population. An additional nine QTL, apparently unrelated to these traits, were also associated with grain yield. A QTL associated with grain yield on chromosome 1B, with no significant relationship with plant height, ear-emergence or rust resistance, was detected (LOD ≥2) at eight of the 18 environments. The mean yield, across 18 environments, of individuals carrying the ‘Molineux’ allele at the 1B locus was 4.8% higher than the mean grain yield of those lines carrying the ‘Trident’ allele at this locus. Another QTL identified on chromosome 4D was also associated with overall gain yield at six of the 18 environments. Of the nine grain yield QTL not shown to be associated with plant height, phenology or rust resistance, two were located near QTL associated with grain yield components. A third QTL, associated with grain yield components at each of the environments used for testing, was located on chromosome 7D. However, this QTL was not associated with grain yield at any of the environments. The implications of these findings on marker-assisted selection for grain yield are discussed.


Bread wheat Grain size Grain yield Grain yield components Quantitative trait locus Triticum aestivum 





Grains per square metre


Grains per head


Heads per plant


Marker assisted selection


Multiple environment trial


Quantitative trait locus


Thousand grain weight





The authors would like to thank the staff at AGT for their assistance collecting phenotypic data and the staff at the SARDI molecular genetic laboratory for the production of the genetic linkage map used in this study. We would also like to thank Mr. P. Eckermann for his help calculating predicted QTL genotypes, and a reviewer of an earlier version of this paper for their helpful suggestions. Our appreciation is extended to the Molecular Plant Breeding Cooperative Research Centre and the Grains Research and Development Corporation for their financial assistance.


  1. Bariana HS, McIntosh RA (1993) Cytogenetic studies in wheat XV. Location of rust resistance genes in VPM1 and their genetic linkage with other disease resistance genes in chromosome 2A. Genome 36:476–482CrossRefPubMedGoogle Scholar
  2. Basford KE, Cooper M (1998) Genotype x environment interaction and some consideration of their implication for wheat breeding in Australia. Aust J Agric Res 49:153–174CrossRefGoogle Scholar
  3. Bernardo R (2002) Breeding for quantitative traits in plants. Stemma Press, Woodbury, USAGoogle Scholar
  4. Borlaug NE (1968) Wheat breeding and its impact on world food supply. In: Finlay KW, Shepherd KW (eds) International wheat genetics symposium, 1st edn. Butterworths, Canberra, Australia, pp 1–36Google Scholar
  5. Borner A, Schumann E, Furste A, Coster H, Leithold B, Roder MS, Weber WE (2002) Mapping of quantitative trait loci determining agronomic important characters in hexaploid wheat (Triticum aestivum L.). Theor Appl Genet 105:921–936PubMedCrossRefGoogle Scholar
  6. Butler JD, Byrne PF, Mohammadi V, Chapman PL, Haley SD (2005) Agronomic performace of Rht Alleles in a spring wheat population across a range of moisture levels. Crop Sci 45:939–947CrossRefGoogle Scholar
  7. Dyck JA, Matus-Cadiz MA, Hucl P, Talbert L, Hunt T, Dubuc JP, Nass H, Clayton G, Dobb J, Quick J (2004) Agronomic performance of hard red spring wheat isolines sensitive and insensitive to photoperiod. Crop Sci 44:1976–1981CrossRefGoogle Scholar
  8. Ellis MH, Speilmeyer 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
  9. Flintham JE, Boerner A, Worland AJ, Gale MD (1997) Optimizing wheat grain yield: effects of Rht (gibberellin-insensitive) dwarfing genes. J Agric Sci 128:11–25CrossRefGoogle Scholar
  10. Flintham JE, Gale MD (1983) The tom thumb dwarfing gene Rht3 in wheat. Theor Appl Genet 66:249–256CrossRefGoogle Scholar
  11. Foulkes MJ, Sylvester-Bradley R, Worland AJ, Snape JW (2004) Effects of a photoperiod-response gene Ppd-D1 on yield potential and drought resistance in UK winter wheat. Euphytica 135:63–73CrossRefGoogle Scholar
  12. Gilmour AF, Cullis BR, Verbyla A (1997) Accounting for natural and extraneous variation in the analysis of field experiments. J Agric Biol Environ St 2:269–293CrossRefGoogle Scholar
  13. 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
  14. Huang XQ, Kempf H, Ganal MW, Roder MS (2004) Advanced backcross QTL analysis in progenies derived from a cross between a German elite winter wheat variety and a synthetic wheat (Triticum aestivum L.). Theor Appl Genet 109:933–943PubMedCrossRefGoogle Scholar
  15. Jansen RC, Stam P (1994) High resolution of quantitative traits into multiple loci via interval mapping. Genetics 136:1447–1455PubMedGoogle Scholar
  16. Jefferies SP, King BJ, Barr AR, Warner P, Logue SJ, Langridge P (2003) Marker-assisted backcross introgression of the Yd2 gene conferring resistance to barley yellow dwarf virus in barley. Plant Breed 122:52–56CrossRefGoogle Scholar
  17. Kuchel H, Hollamby GJ, Langridge P, Williams KJ, Jefferies SP (2006) Identification of genetic loci associated with ear-emergence in bread wheat. Theor Appl Genet 113:1103–1112PubMedCrossRefGoogle Scholar
  18. Law CN, Worland AJ (1997) Genetic analysis of some flowering time and adaptive traits in wheat. New Phytol 137:19–28CrossRefGoogle Scholar
  19. Manly KF, Olson JM (1999) Overview of QTL mapping software and introduction to MAP MANAGER QT. Mamm Genome 10:327–334PubMedCrossRefGoogle Scholar
  20. Marza F, Bai G-H, Carver BF, Zhou W-C (2006) Quantitative trait loci for yield and related traits in the wheat population Nin7840 X Clark. Theor Appl Genet 112:688–698PubMedCrossRefGoogle Scholar
  21. McCartney CA, Somers DJ, Humphreys DG, Lukow O, Ames N, Noll J, Cloutier S, McCallum BD (2005) Mapping quantitative trait loci controlling agronomic traits in the spring wheat cross RL4452 x ‘AC Domain’. Genome 48:870–883PubMedGoogle Scholar
  22. Nizam Uddin M, Marshall DR (1989) Effects of dwarfing genes on yield and yield components under irrigated and rainfed conditions in wheat (Triticum aestivum L.). Euphytica 42:127–134CrossRefGoogle Scholar
  23. Nyquist WE (1991) Estimation of heritability and prediction of selection response in plant populations. Crit Rev Plant Sci 10:235–322CrossRefGoogle Scholar
  24. Payne RW, Baird DB, Cherry M, Gilmour AR, Harding SA, Kane AK, Lane PW, Murray DA, Soutar DM, Thompson R, Todd AD, Tunnicliffe Wilson G, Webster R, Welham SJ (2002) GenStat Rlease 6.1 reference manual. VSN International, Oxford, UKGoogle Scholar
  25. Quarrie SA, Steed A, Calestani C, Semikbodskii A, Lebreton C, Chinoy C, Steele N, Pljevlajakusic D, Habash DZ, Farmer P, Saker L, Clarkson DT, Abugalieva A, Yessimbekova M, Turuspekov Y, Abugalieva S, Tuberosa R, Sanguineti M-C, Hollington PA, Aragues R, Royo A, Dodig D (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
  26. Ranjbar GA (1997) Production and utilisation of doubled haploid lines in wheat breeding programmes. Plant science. University of Adelaide, AdelaideGoogle Scholar
  27. Rebetzke GJ, Richards RA (2000) Gibberellic acid-sensitive dwarfing genes reduce plant height to increase kernel number and grain yield of wheat. Aust J Agric Res 51:235–245CrossRefGoogle Scholar
  28. Richards RA (1992) The effect of dwarfing genes in spring wheat in dry environments. I. Agronomic characteristics. Aust J Agric Res 43:517–527CrossRefGoogle Scholar
  29. Seah S, Bariana H, Jahier J, Sivasithamparam K, Lagudah ES (2001) The introgressed segment carrying rust resistance genes Yr17, Lr37 and Sr38 in wheat can be assayed by a cloned disease resistance gene-like sequence. Theor Appl Genet 102:600–605CrossRefGoogle Scholar
  30. Syme JR (1970) A high-yielding Mexican semi-dwarf wheat and the relationship of yield to harvest index and other varietal characteristics. Aust J Exp Agric Anim Husb 10:350–353CrossRefGoogle Scholar
  31. The TT, Latter BDH, McIntosh RA, Ellison FW, Brennan PS, Fisher J, Hollamby GJ, Rathjen AJ, Wilson RE (1988) Grain yields of near-isogenic lines with added genes for stem rust resistance. In: Miller TE, Koebner RMD (eds) Seventh international wheat genetics symposium. Bath Press, Cambridge, England, pp 901–906Google Scholar
  32. Whittaker JC, Thompson R, Visscher PM (1996) On the mapping of QTL by regression on marker-type. Heredity 77:23–32CrossRefGoogle Scholar
  33. Williams KJ, Willsmore KJ, Olson S, Matic M, Kuchel H (2006) Mapping of a novel QTL for resistance to cereal cyst nematode in wheat. Theor Appl Genet 112:1480–1486PubMedCrossRefGoogle Scholar
  34. Worland AJ (1996) The influence of flowering time genes on environmental adaptability in European wheats. Euphytica 89:49–57CrossRefGoogle Scholar
  35. Worland AJ, Borner A, Korzun V, Li WM, Petrovic S, Sayers EJ (1998) The influence of photoperiod genes on the adaptability of European winter wheats. Euphytica 100:385–394CrossRefGoogle Scholar
  36. Yousef GG, Juvik JA (2001) Comparison of phenotypic and marker-assisted Selection for quantitative traits in sweet corn. Crop Sci 41:645–655CrossRefGoogle Scholar
  37. Yu K, Park SJ, Poysa V (2000) Marker-assisted selection of common beans for resistance to common bacterial blight: efficacy and economics. Plant Breed 119:411–415CrossRefGoogle Scholar
  38. Zhou W-C, Kolb FL, Bai G-H, Dolmier LL, Boze LK, Smith NJ (2003) Validation of a major QTL for scab resistance with SSR markers and use of marker-assisted selection in wheat. Plant Breed 122:40–46CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • H. Kuchel
    • 1
    • 2
    • 3
    Email author
  • K. J. Williams
    • 3
    • 4
  • P. Langridge
    • 2
    • 5
  • H. A. Eagles
    • 2
    • 3
  • S. P. Jefferies
    • 1
    • 2
    • 3
  1. 1.Australian Grain Technologies Pty LtdUniversity of AdelaideRoseworthyAustralia
  2. 2.School of Agriculture, Food and WineUniversity of AdelaideGlen OsmondAustralia
  3. 3.Molecular Plant Breeding Cooperative Research CentreUniversity of AdelaideGlen OsmondAustralia
  4. 4.South Australian Research and Development InstituteGlen OsmondAustralia
  5. 5.Australian Centre for Plant Functional GenomicsGlen OsmondAustralia

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