Theoretical and Applied Genetics

, Volume 115, Issue 7, pp 1015–1027 | Cite as

Genetic dissection of grain yield in bread wheat. II. QTL-by-environment interaction

  • H. Kuchel
  • K. Williams
  • P. Langridge
  • H. A. Eagles
  • S. P. Jefferies
Original Paper


The grain yield of wheat is influenced by genotype, environment and genotype-by-environment interaction. A mapping population consisting of 182 doubled haploid progeny derived from a cross between the southern Australian varieties ‘Trident’ and ‘Molineux’, was used to characterise the interaction of previously mapped grain yield quantitative trait locus (QTL) with specific environmental covariables. Environments (17) used for grain yield assessment were characterised for latitude, rainfall, various temperature-based variables and stripe rust infection severity. The number of days in the growing season in which the maximum temperature exceeded 30°C was identified as the variable with the largest effect on site mean grain yield. However, the greatest QTL-by-environmental covariable interactions were observed with the severity of stripe rust infection. The rust resistance allele at the Lr37/Sr38/Yr17 locus had the greatest positive effect on grain yield when an environment experienced a combination of high-stripe rust infection and cool days. The grain yield QTL, QGyld.agt-4D, showed a very similar QTL-by-environment covariable interaction pattern to the Lr37/Sr38/Yr17 locus, suggesting a possible role in rust resistance or tolerance. Another putative grain yield per se QTL, QGyld.agt-1B, displayed interactions with the quantity of winter and spring rainfall, the number of days in which the maximum temperature exceeded 30°C, and the number of days with a minimum temperature below 10°C. However, no cross-over interaction effect was observed for this locus, and the ‘Molineux’ allele remained associated with higher grain yield in response to all environmental covariables. The results presented here confirm that QGyld.agt-1B may be a prime candidate for marker-assisted selection for improved grain yield and wide adaptation in wheat. The benefit of analysing the interaction of QTL and environmental covariables, such as employed here, is discussed.


Quantitative Trait Locus Doubled Haploid Rust Resistance Stripe Rust Doubled Haploid Line 
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.



Doubled haploid


Genotype-by-environmental covariable interaction


Genotype-by-environment interaction


Quantitative trait locus-by-environmental covariable interaction


Quantitative trait locus-by-environment interaction


Quantitative trait locus




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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • H. Kuchel
    • 1
    • 2
    • 3
  • K. 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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