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 #346626
Title: Multitrait, random regression, or simple repeatability model in high-throughput phenotyping data improve genomic prediction for wheat grain yield

Author
item SUN, JIN - Cornell University
item RUTKOSKI, JESSICA - Cornell University
item POLAND, JESSE - Kansas State University
item CROSSA, JOSE - International Maize & Wheat Improvement Center (CIMMYT)
item Jannink, Jean-Luc
item SORRELLS, MARK - Cornell University

Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/6/2017
Publication Date: 5/18/2017
Citation: Sun, J., Rutkoski, J., Poland, J., Crossa, J., Jannink, J., Sorrells, M. 2017. Multitrait, random regression, or simple repeatability model in high-throughput phenotyping data improve genomic prediction for wheat grain yield. The Plant Genome. 10(2):1-12.

Interpretive Summary: High-throughput phenotyping (HTP) platforms can be used to measure traits that are genetically correlated with wheat (Triticum aestivum L.) grain yield repeatedly over the season. Incorporating such secondary traits in yield prediction models would be desirable to improve indirect selection for grain yield. In this study, we evaluated three statistical models for the repeated measures of secondary traits and compared their predictive abilities for grain yield. Grain yield and secondary traits, canopy temperature (CT) and normalized difference vegetation index (NDVI), were collected in five environments for 557 wheat lines with available pedigree and genomic information. First, secondary traits were fitted separately within each environment. Secondary trait estimates from this step were then used in multivariate prediction models. Predictive ability was improved by 70%, on average, from multivariate pedigree and genomic models when including secondary traits in both training and test populations. Additionally, predictive abilities slightly varied for the three models in this data set, with different models performing best in different environments. The substantial improvement in prediction accuracy suggests that HTP platforms will help predict yield.

Technical Abstract: High-throughput phenotyping (HTP) platforms can be used to measure traits that are genetically correlated with wheat (Triticum aestivum L.) grain yield across time. Incorporating such secondary traits in the multivariate pedigree and genomic prediction models would be desirable to improve indirect selection for grain yield. In this study, we evaluated three statistical models, simple repeatability (SR), multitrait (MT), and random regression (RR), for the longitudinal data of secondary traits and compared the impact of the proposed models for secondary traits on their predictive abilities for grain yield. Grain yield and secondary traits, canopy temperature (CT) and normalized difference vegetation index (NDVI), were collected in five diverse environments for 557 wheat lines with available pedigree and genomic information. A two-stage analysis was applied for pedigree and genomic selection (GS). First, secondary traits were fitted by SR, MT, or RR models, separately, within each environment. Then, best linear unbiased predictions (BLUPs) of secondary traits from the above models were used in the multivariate prediction models to compare predictive abilities for grain yield. Predictive ability was substantially improved by 70%, on average, from multivariate pedigree and genomic models when including secondary traits in both training and test populations. Additionally, (i) predictive abilities slightly varied for MT, RR, or SR models in this data set, (ii) results indicated that including BLUPs of secondary traits from the MT model was the best in severe drought, and (iii) the RR model was slightly better than SR and MT models under drought environment.