Skip to main content
ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Publications at this Location » Publication #365681

Research Project: Database Tools for Managing and Analyzing Big Data Sets to Enhance Small Grains Breeding

Location: Plant, Soil and Nutrition Research

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

Author
item SUN, JIN - Cornell University - New York
item POLAND, JESSE - Kansas State University
item MONDAL, SUCHISMITA - International Maize & Wheat Improvement Center (CIMMYT)
item CROSSA, JOSE - International Maize & Wheat Improvement Center (CIMMYT)
item PHILOMIN, JULIANA - International Maize & Wheat Improvement Center (CIMMYT)
item SINGH, RAVI - International Maize & Wheat Improvement Center (CIMMYT)
item RUTKOSKI, JESSICA - Cornell University - New York
item Jannink, Jean-Luc
item CRESPO-HERRERA, LEONARDO - International Maize & Wheat Improvement Center (CIMMYT)
item VELU, GOVINDAN - International Maize & Wheat Improvement Center (CIMMYT)
item HUERTA-ESPINO, JULIO - Instituto Nacional De Investigaciones Forestales Y Agropecuarias (INIFAP)
item SORRELS, MARK - Cornell University - New York

Submitted to: Theoretical and Applied Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/6/2019
Publication Date: 2/18/2019
Citation: Sun, J., Poland, J.A., Mondal, S., Crossa, J., Philomin, J., Singh, R.P., Rutkoski, J.E., Jannink, J., Crespo-Herrera, L., Velu, G., Huerta-Espino, J., Sorrels, M. 2019. High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage. Theoretical and Applied Genetics. 132:1705–1720. https://doi.org/10.1007/s00122-019-03309-0.
DOI: https://doi.org/10.1007/s00122-019-03309-0

Interpretive Summary: Recent years have seen increased use of DNA markers to predict the performance of new breeding lines. These predictions, however, cannot account for variation in the environment. We used rapidly-measured canopy traits to help account for environment and improve prediction. We evaluated prediction of grain yield in three elite yield trials across three wheat growing cycles The ability to predict grain yield was evaluated with or without canopy traits. We showed that prediction accuracy increased by an average of 146% with canopy traits, and that these are best-measured during wheat heading and grain-filling stages.

Technical 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.