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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Plant Sci. | doi: 10.3389/fpls.2019.01311

Multi-trait multi-environment genomic prediction of durum wheat with genomic best linear unbiased predictor and deep learning methods

 Jose Crossa1*, Osval Montesinos-Lopez2, Avelardo Montesinos-Lopez3,  Roberto Tuberosa4,  Marco Maccaferri4 and  Giuseppe Sciara4
  • 1The International Maize and Wheat Improvement Center (CIMMYT), India
  • 2University of Colima, Mexico
  • 3University of Guadalajara, Mexico
  • 4University of Bologna, Italy

Although durum wheat (Triticum turgidum var. durum Desf.) is a minor cereal crop representing just 5% of the total wheat crop, it is a staple food for Mediterranean people who use it to make pasta, couscous, bulgur and bread. For this reason, there are research programs (mostly in Europe) working to improve durum wheat production and quality. 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. Multi-trait prediction analyses were performed by implementing a multi-trait deep learning model with a feed-forward network architecture and a rectified linear unit activation function with a grid search approach for the selection of hyper-parameters. 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. This result provides more evidence that the GBLUP model is a powerful approach for genomic prediction, but also that the deep learning method is a practical approach for predicting univariate and multivariate traits in the context of genomic selection.

Keywords: Durum wheat (T. Durum L.), genomic selection, Multi trait, Multi environment, Single trait, prediction accuracy

Received: 18 Jun 2019; Accepted: 20 Sep 2019.

Copyright: © 2019 Crossa, Montesinos-Lopez, Montesinos-Lopez, Tuberosa, Maccaferri and Sciara. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Jose Crossa, The International Maize and Wheat Improvement Center (CIMMYT), New Delhi, National Capital Territory of Delhi, India, j.crossa@cgiar.org