Theoretical and Applied Genetics

, Volume 132, Issue 6, pp 1705–1720 | Cite as

High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage

  • Jin Sun
  • Jesse A. Poland
  • Suchismita Mondal
  • José Crossa
  • Philomin Juliana
  • Ravi P. Singh
  • Jessica E. Rutkoski
  • Jean-Luc Jannink
  • Leonardo Crespo-Herrera
  • Govindan Velu
  • Julio Huerta-Espino
  • Mark E. SorrellsEmail author
Original Article


Genomic selection (GS) models have been validated for many quantitative traits in wheat (Triticum aestivum L.) breeding. However, those models are mostly constrained within the same growing cycle and the extension of GS to the case of across cycles has been a challenge, mainly due to the low predictive accuracy resulting from two factors: reduced genetic relationships between different families and augmented environmental variances between cycles. Using the data collected from diverse field conditions at the International Wheat and Maize Improvement Center, we evaluated GS for grain yield in three elite yield trials across three wheat growing cycles. The objective of this project was to employ the secondary traits, canopy temperature, and green normalized difference vegetation index, which are closely associated with grain yield from high-throughput phenotyping platforms, to improve prediction accuracy for grain yield. The ability to predict grain yield was evaluated reciprocally across three cycles with or without secondary traits. Our results indicate that prediction accuracy increased by an average of 146% for grain yield across cycles with secondary traits. In addition, our results suggest that secondary traits phenotyped during wheat heading and early grain filling stages were optimal for enhancing the prediction accuracy for grain yield.



Best linear unbiased predictions


Canopy temperature


Genomic selection


High-throughput phenotyping


Green normalized difference vegetation index



The research was funded by the United States Agency for International Development (USAID) “Feed the Future Initiative” (Cooperative Agreement #AID-OAA-A-13-00051) and by participating US and Host Country institutions. We also thank the Delivering Genetic Gain in Wheat project, supported by aid from the U.K. Government’s Department of International Development (DFID) and the Bill & Melinda Gates Foundation (OPP113319). Partial funding was provided by Hatch project 149-430. This work was also partially supported by the Agriculture and Food Research Initiative Competitive Grants 2011-68002-30029 (Triticeae-CAP) and 2017-67007-25939 (Wheat-CAP) from the USDA National Institute of Food and Agriculture.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

122_2019_3309_MOESM1_ESM.docx (234 kb)
Supplementary material 1 (DOCX 233 kb)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Jin Sun
    • 1
  • Jesse A. Poland
    • 2
  • Suchismita Mondal
    • 3
  • José Crossa
    • 3
  • Philomin Juliana
    • 3
  • Ravi P. Singh
    • 3
  • Jessica E. Rutkoski
    • 1
    • 4
  • Jean-Luc Jannink
    • 1
    • 5
  • Leonardo Crespo-Herrera
    • 3
  • Govindan Velu
    • 3
  • Julio Huerta-Espino
    • 6
  • Mark E. Sorrells
    • 1
    Email author
  1. 1.Plant Breeding and Genetics Section, School of Integrative Plant ScienceCornell UniversityIthacaUSA
  2. 2.Department of Plant Pathology and Department of AgronomyKansas State UniversityManhattanUSA
  3. 3.International Maize and Wheat Improvement Center (CIMMYT)TexcocoMexico
  4. 4.International Rice Research InstituteLos BañosPhilippines
  5. 5.USDA-ARS R.W. Holley Center for Agriculture and HealthIthacaUSA
  6. 6.Campo Experimental Valle de México INIFAPChapingoMexico

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