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Theoretical and Applied Genetics

, Volume 129, Issue 8, pp 1595–1605 | Cite as

Genomic prediction for grain zinc and iron concentrations in spring wheat

  • Govindan VeluEmail author
  • Jose Crossa
  • Ravi P. Singh
  • Yuanfeng Hao
  • Susanne Dreisigacker
  • Paulino Perez-Rodriguez
  • Arun K. Joshi
  • Ravish Chatrath
  • Vikas Gupta
  • Arun Balasubramaniam
  • Chhavi Tiwari
  • Vinod K. Mishra
  • Virinder Singh Sohu
  • Gurvinder Singh Mavi
Original Article

Abstract

Key message

Predictability estimated through cross-validation approach showed moderate to high level; hence, genomic selection approach holds great potential for biofortification breeding to enhance grain zinc and iron concentrations in wheat.

Abstract

Wheat (Triticum aestivum L.) is a major staple crop, providing 20 % of dietary energy and protein consumption worldwide. It is an important source of mineral micronutrients such as zinc (Zn) and iron (Fe) for resource poor consumers. Genomic selection (GS) approaches have great potential to accelerate development of Fe- and Zn-enriched wheat. Here, we present the results of large-scale genomic and phenotypic data from the HarvestPlus Association Mapping (HPAM) panel consisting of 330 diverse wheat lines to perform genomic predictions for grain Zn (GZnC) and Fe (GFeC) concentrations, thousand-kernel weight (TKW) and days to maturity (DTM) in wheat. The HPAM lines were phenotyped in three different locations in India and Mexico in two successive crop seasons (2011–12 and 2012–13) for GZnC, GFeC, TKW and DTM. The genomic prediction models revealed that the estimated prediction abilities ranged from 0.331 to 0.694 for Zn and from 0.324 to 0.734 for Fe according to different environments, whereas prediction abilities for TKW and DTM were as high as 0.76 and 0.64, respectively, suggesting that GS holds great potential in biofortification breeding to enhance grain Zn and Fe concentrations in bread wheat germplasm.

Keywords

Genomic Selection Genomic Prediction Prediction Ability Genomic Selection Model Genomic Prediction Model 
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.

Notes

Acknowledgments

The authors acknowledge the financial support from the HarvestPlus challenge program and CGIAR research program on Agriculture for Nutrition and Health and special thanks to anonymous reviewer for the useful comments given in the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

122_2016_2726_MOESM1_ESM.docx (22 kb)
Supplementary material 1 (DOCX 21 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Govindan Velu
    • 1
    Email author
  • Jose Crossa
    • 1
  • Ravi P. Singh
    • 1
  • Yuanfeng Hao
    • 1
  • Susanne Dreisigacker
    • 1
  • Paulino Perez-Rodriguez
    • 2
  • Arun K. Joshi
    • 3
  • Ravish Chatrath
    • 4
  • Vikas Gupta
    • 4
  • Arun Balasubramaniam
    • 5
  • Chhavi Tiwari
    • 5
  • Vinod K. Mishra
    • 5
  • Virinder Singh Sohu
    • 6
  • Gurvinder Singh Mavi
    • 6
  1. 1.International Maize and Wheat Improvement Center (CIMMYT)MexicoMexico
  2. 2.Colegio de Postgraduados en Ciencias AgricolasTexcocoMexico
  3. 3.International Maize and Wheat Improvement Center (CIMMYT)South Asia OfficeKathmanduNepal
  4. 4.Indian Institute of Wheat and Barley Research (IIWBR)KarnalIndia
  5. 5.Banaras Hindu University (BHU)VaranasiIndia
  6. 6.Punjab Agricultural University (PAU)LudhianaIndia

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