Increased prediction accuracy using combined genomic information and physiological traits in a soft wheat panel evaluated in multi-environments

Authors: GUO, JIA - University Of Florida; PRADHAN, SUMIT - University Of Florida; RSHAHI, DEPENDRA - University Of Florida; KHAN, JAHANGIR - University Of Florida; MCBREEN, JORDAN - University Of Florida; Bai, Guihua; MURPHY, PAUL - North Carolina State University; BABAR, MD ALI - University Of Florida

Submitted to: Scientific Reports
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/11/2020
Publication Date: 4/27/2020
Citation: Guo, J., Pradhan, S., Rshahi, D., Khan, J., Mcbreen, J., Bai, G., Murphy, P., Babar, M. 2020. Increased prediction accuracy using combined genomic information and physiological traits in a soft wheat panel evaluated in multi-environments. Scientific Reports. 10. Article 7023. https://doi.org/10.1038/s41598-020-63919-3.
DOI: https://doi.org/10.1038/s41598-020-63919-3

Interpretive Summary: Breeding programs need accurate mathematical models that combine available information to help select the best breeding lines. We compared the prediction accuracy for grain yield between models using lab-based genomic data and/or field-based physiological data such as canopy temperature, chlorophyll content, membrane thermostability, rate of senescence, stay-green trait, and normalized vegetation index values from four environments. Prediction accuracies of grain yield from the four environments were improved when physiological traits and their interaction effects were included in the models. Integrated yield prediction models should increase the selection efficiency in wheat breeding programs.

Technical Abstract: An integration of field-based phenotypic and genomic data can potentially increase the genetic gain in wheat breeding for complex traits such as grain and biomass yield. To validate this hypothesis in empirical field experiments, we compared the prediction accuracy between multi-kernel physiological and genomic best linear unbiased prediction (BLUP) model to a single-kernel physiological or genomic BLUP model for grain yield (GY) using a soft wheat population that was evaluated in four environments. The physiological data including canopy temperature (CT), SPAD chlorophyll content (SPAD), membrane thermostability (MT), rate of senescence (RS), stay green trait (SGT), and NDVI values were collected at four environments (2016, 2017, and 2018 at Citra, FL; 2017 at Quincy, FL). Using a genotyping-by-sequencing (GBS) approach, a total of 19,353 SNPs were generated and used to estimate prediction model accuracy. Prediction accuracies of grain yield evaluated in four environments improved when physiological traits and/or interaction effects (genotype × environment or physiology × environment) were included in the model compared to models with only genomic data. The proposed multi-kernel models that combined physiological and genomic data showed 35 to 169% increase in prediction accuracy compared to models with only genomic data included when heading date was used as a covariate. In general, higher response to selection was captured by the model combing effects of physiological and genotype × environment interaction compared to other models. The results of this study support the integration of field-based physiological data into GY prediction to improve genetic gain from selection in soft wheat under a multi-environment context.