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Multi-trait multi-environment genomic prediction of durum wheat

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

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Title Multi-trait multi-environment genomic prediction of durum wheat
 
Identifier https://hdl.handle.net/11529/10548262
 
Creator Montesinos-López, Osval A.
Montesinos-López, Abelardo
Tuberosa, Roberto
Maccaferri, Marco
Sciara, Giuseppe
Ammar, Karim
Crossa, Jose
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description In this paper we cover multi-trait prediction of grain yield (GY), days to heading (DH) and plant height (PH) of 270 durum wheat lines that were evaluated in 43 environments (location-year combinations) in Bologna, Italy. The results of the multi-trait deep learning method also were compared with univariate predictions of the genomic best linear unbiased predictor (GBLUP) method and the univariate counterpart of the multi-trait deep learning method. All models were implemented with and without the genotype×environment interaction term. We found that the best predictions were observed without the genotype×environment interaction term in the univariate and multivariate deep learning methods, but under the GBLUP method, the best predictions were observed taking into account the interaction term. We also found that in general the best predictions were observed under the GBLUP model but the predictions of the multi-trait deep learning model were very similar to those of the GBLUP model.
 
Subject Agricultural Sciences
Agricultural research
Wheat
Triticum durum
Genomic best linear unbiased predictor
GBLUP
Grain yield
Days to heading
Plant height
 
Language English
 
Date 2019
 
Contributor Shrestha, Rosemary
Genetic Resources Program (GRP)
Biometrics and Statistics Unit (BSU)
Global Wheat Program (GWP)
CGIAR Research Program on Wheat (WHEAT)
CGIAR
 
Type Experimental data, Phenotypic data