Theoretical and Applied Genetics

, Volume 119, Issue 2, pp 353–369 | Cite as

Quantitative trait loci conferring grain mineral nutrient concentrations in durum wheat × wild emmer wheat RIL population

  • Zvi Peleg
  • Ismail Cakmak
  • Levent Ozturk
  • Atilla Yazici
  • Yan Jun
  • Hikmet Budak
  • Abraham B. Korol
  • Tzion Fahima
  • Yehoshua Saranga
Original Paper


Mineral nutrient malnutrition, and particularly deficiency in zinc and iron, afflicts over 3 billion people worldwide. Wild emmer wheat, Triticum turgidum ssp. dicoccoides, genepool harbors a rich allelic repertoire for mineral nutrients in the grain. The genetic and physiological basis of grain protein, micronutrients (zinc, iron, copper and manganese) and macronutrients (calcium, magnesium, potassium, phosphorus and sulfur) concentration was studied in tetraploid wheat population of 152 recombinant inbred lines (RILs), derived from a cross between durum wheat (cv. Langdon) and wild emmer (accession G18-16). Wide genetic variation was found among the RILs for all grain minerals, with considerable transgressive effect. A total of 82 QTLs were mapped for 10 minerals with LOD score range of 3.2–16.7. Most QTLs were in favor of the wild allele (50 QTLs). Fourteen pairs of QTLs for the same trait were mapped to seemingly homoeologous positions, reflecting synteny between the A and B genomes. Significant positive correlation was found between grain protein concentration (GPC), Zn, Fe and Cu, which was supported by significant overlap between the respective QTLs, suggesting common physiological and/or genetic factors controlling the concentrations of these mineral nutrients. Few genomic regions (chromosomes 2A, 5A, 6B and 7A) were found to harbor clusters of QTLs for GPC and other nutrients. These identified QTLs may facilitate the use of wild alleles for improving grain nutritional quality of elite wheat cultivars, especially in terms of protein, Zn and Fe.


Durum Wheat Phytic Acid Mineral Nutrient Mineral Concentration Grain Yield 
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 study was supported by HarvestPlus Biofortification Challenge Program ( The authors are also grateful to The Israel Science Foundation (ISF) grant #1089/04 and State Planning Organization of the Turkish Republic for providing additional support to this study. We greatly acknowledge A. Avneri, M. Chatzav and U. Uner for their excellent assistance in the field experiments. Z. Peleg is indebted to the Israel Council for Higher Education postdoctoral fellowships award.


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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Zvi Peleg
    • 1
    • 2
  • Ismail Cakmak
    • 3
  • Levent Ozturk
    • 3
  • Atilla Yazici
    • 3
  • Yan Jun
    • 1
    • 2
  • Hikmet Budak
    • 3
  • Abraham B. Korol
    • 2
  • Tzion Fahima
    • 2
  • Yehoshua Saranga
    • 1
  1. 1.The Robert H. Smith Institute of Plant Science and Genetics in AgricultureThe Hebrew University of JerusalemRehovotIsrael
  2. 2.Department of Evolutionary and Environmental Biology, Faculty of Science and Science Education, The Institute of EvolutionUniversity of HaifaHaifaIsrael
  3. 3.Faculty of Engineering and Natural SciencesSabanci UniversityIstanbulTurkey

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