Theoretical and Applied Genetics

, Volume 125, Issue 3, pp 561–575 | Cite as

QTL mapping of terminal heat tolerance in hexaploid wheat (T. aestivum L.)

  • Rajneesh Paliwal
  • Marion S. Röder
  • Uttam Kumar
  • J. P. Srivastava
  • Arun Kumar Joshi
Original Paper


High temperature (>30 °C) at the time of grain filling is one of the major causes of yield reduction in wheat in many parts of the world, especially in tropical countries. To identify quantitative trait loci (QTL) for heat tolerance under terminal heat stress, a set of 148 recombinant inbred lines was developed by crossing a heat-tolerant hexaploid wheat (Triticum aestivum L.) cultivar (NW1014) and a heat-susceptible (HUW468) cultivar. The F5, F6, and F7 generations were evaluated in two different sowing dates under field conditions for 2 years. Using the trait values from controlled and stressed trials, four different traits (1) heat susceptibility index (HSI) of thousand grain weight (HSITGW); (2) HSI of grain fill duration (HSIGFD); (3) HSI of grain yield (HSIYLD); and (4) canopy temperature depression (CTD) were used to determine heat tolerance. Days to maturity was also investigated. A linkage map comprising 160 simple sequence repeat markers was prepared covering the whole genome of wheat. Using composite interval mapping, significant genomic regions on 2B, 7B and 7D were found to be associated with heat tolerance. Of these, two (2B and 7B) were co-localized QTL and explained more than 15 % phenotypic variation for HSITGW, HSIGFD and CTD. In pooled analysis over three trials, QTL explained phenotypic variation ranging from 9.78 to 20.34 %. No QTL × trial interaction was detected for the identified QTL. The three major QTL obtained can be used in marker-assisted selection for heat stress in wheat.


Quantitative Trait Locus Heat Stress Quantitative Trait Locus Analysis Heat Tolerance Quantitative Trait Locus Region 
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.



Molecular mapping part of this research was supported by the DAAD Leibniz (German Academic Exchange Service) fellowship granted to R. Paliwal. We gratefully acknowledge the off-season facility provided by IARI Regional Station, Wellington, Tamil Nadu, India. The authors are thankful to Munna Lal, Anette Heber, and Sonja Allner for their excellent technical assistance. Help rendered by Dr. B. Arun, Banaras Hindu University, in field experimentation and R.R. Mir, ICRISAT (Hyderabad, India) in the analysis of data is gratefully acknowledged. We thank three anonymous reviewers for making useful suggestions that improved the quality of the article.

Supplementary material

122_2012_1853_MOESM1_ESM.doc (427 kb)
Supplementary material 1 (DOC 427 kb)


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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Rajneesh Paliwal
    • 1
    • 2
  • Marion S. Röder
    • 1
  • Uttam Kumar
    • 1
    • 3
  • J. P. Srivastava
    • 4
  • Arun Kumar Joshi
    • 2
    • 5
  1. 1.Leibniz Institute of Plant Genetics and Crop Plant Research (IPK)GaterslebenGermany
  2. 2.Department of Genetics and Plant Breeding, Institute of Agricultural SciencesBanaras Hindu UniversityVaranasiIndia
  3. 3.The Energy and Resources InstituteNew DelhiIndia
  4. 4.Department of Plant Physiology, Institute of Agricultural SciencesBanaras Hindu UniversityVaranasiIndia
  5. 5.International Maize and Wheat Improvement Center (CIMMYT), South Asia Regional OfficeKathmanduNepal

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