Multi-trait multi-environment genomic prediction of durum wheat

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.

Bibliographic Details

Main Authors:	Montesinos-López, Osval A., Montesinos-López, Abelardo, Tuberosa, Roberto, Maccaferri, Marco, Sciara, Giuseppe, Ammar, Karim, Crossa, Jose
Other Authors:	Shrestha, Rosemary
Format:	Experimental data, Phenotypic data biblioteca
Language:	English
Published:	CIMMYT Research Data & Software Repository Network 2019
Subjects:	Agricultural Sciences, Agricultural research, Wheat, Triticum durum, Genomic best linear unbiased predictor, GBLUP, Grain yield, Days to heading, Plant height,
Online Access:	https://hdl.handle.net/11529/10548262
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