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. Sorrells

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1705-1720
Number of pages16
JournalTheoretical and Applied Genetics
Volume132
Issue number6
DOIs
StatePublished - Jun 1 2019
Externally publishedYes

Fingerprint

marker-assisted selection
Triticum
grain yield
phenotype
wheat
Population
prediction
Zea mays
Breeding
heading
quantitative traits
filling period
genetic relationships
Triticum aestivum
Temperature
canopy
corn
breeding
temperature

ASJC Scopus subject areas

  • Biotechnology
  • Agronomy and Crop Science
  • Genetics

Cite this

High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage. / Sun, Jin; Poland, Jesse A.; Mondal, Suchismita; Crossa, José; Juliana, Philomin; Singh, Ravi P.; Rutkoski, Jessica E.; Jannink, Jean Luc; Crespo-Herrera, Leonardo; Velu, Govindan; Huerta-Espino, Julio; Sorrells, Mark E.

In: Theoretical and Applied Genetics, Vol. 132, No. 6, 01.06.2019, p. 1705-1720.

Research output: Contribution to journalArticle

Sun, J, Poland, JA, Mondal, S, Crossa, J, Juliana, P, Singh, RP, Rutkoski, JE, Jannink, JL, Crespo-Herrera, L, Velu, G, Huerta-Espino, J & Sorrells, ME 2019, 'High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage', Theoretical and Applied Genetics, vol. 132, no. 6, pp. 1705-1720. https://doi.org/10.1007/s00122-019-03309-0
Sun, Jin ; Poland, Jesse A. ; Mondal, Suchismita ; Crossa, José ; Juliana, Philomin ; Singh, Ravi P. ; Rutkoski, Jessica E. ; Jannink, Jean Luc ; Crespo-Herrera, Leonardo ; Velu, Govindan ; Huerta-Espino, Julio ; Sorrells, Mark E. / High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage. In: Theoretical and Applied Genetics. 2019 ; Vol. 132, No. 6. pp. 1705-1720.
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