Multi-Trait, Multi-Environment Genomic Prediction of Durum Wheat With Genomic Best Linear Unbiased Predictor and Deep Learning Methods
Although durum wheat (Triticum turgidum var. durum Desf.) is a minor cereal crop representing just 5–7% of the world’s total wheat crop, it is a staple food in Mediterranean countries, where it is used to produce pasta, couscous, bulgur and bread. In this paper, we cover multi-trait prediction of gr...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-11-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fpls.2019.01311/full |
| _version_ | 1829143196568911872 |
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| author | Osval A. Montesinos-López Abelardo Montesinos-López Roberto Tuberosa Marco Maccaferri Giuseppe Sciara Karim Ammar José Crossa |
| author_facet | Osval A. Montesinos-López Abelardo Montesinos-López Roberto Tuberosa Marco Maccaferri Giuseppe Sciara Karim Ammar José Crossa |
| author_sort | Osval A. Montesinos-López |
| collection | DOAJ |
| description | Although durum wheat (Triticum turgidum var. durum Desf.) is a minor cereal crop representing just 5–7% of the world’s total wheat crop, it is a staple food in Mediterranean countries, where it is used to produce pasta, couscous, bulgur and bread. 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 (country–location–year combinations) across a broad range of water regimes in the Mediterranean Basin and other locations. Multi-trait prediction analyses were performed by implementing a multi-trait deep learning model (MTDL) with a feed-forward network topology 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 were also compared with univariate predictions of the genomic best linear unbiased predictor (GBLUP) method and the univariate counterpart of the multi-trait deep learning method (UDL). 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 UDL and MTDL methods. However, under the GBLUP method, the best predictions were observed when the genotype × environment interaction term was taken into account. We also found that in general the best predictions were observed under the GBLUP model; however, the predictions of the MTDL 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. |
| first_indexed | 2024-12-14T20:45:53Z |
| format | Article |
| id | doaj.art-a4d15e0ae18c44588a46fd940195aee6 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-12-14T20:45:53Z |
| publishDate | 2019-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-a4d15e0ae18c44588a46fd940195aee62022-12-21T22:48:03ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2019-11-011010.3389/fpls.2019.01311479328Multi-Trait, Multi-Environment Genomic Prediction of Durum Wheat With Genomic Best Linear Unbiased Predictor and Deep Learning MethodsOsval A. Montesinos-López0Abelardo Montesinos-López1Roberto Tuberosa2Marco Maccaferri3Giuseppe Sciara4Karim Ammar5José Crossa6Facultad de Telemática, Universidad de Colima, Colima, MexicoDepartamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, MexicoDepartment of Agricultural and Food Sciences, University of Bologna, Bologna, ItalyDepartment of Agricultural and Food Sciences, University of Bologna, Bologna, ItalyDepartment of Agricultural and Food Sciences, University of Bologna, Bologna, ItalyGlobal Wheat Breeding Program, International Maize and Wheat Improvement Center (CIMMYT), Mexico City, MexicoGlobal Wheat Breeding Program, International Maize and Wheat Improvement Center (CIMMYT), Mexico City, MexicoAlthough durum wheat (Triticum turgidum var. durum Desf.) is a minor cereal crop representing just 5–7% of the world’s total wheat crop, it is a staple food in Mediterranean countries, where it is used to produce pasta, couscous, bulgur and bread. 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 (country–location–year combinations) across a broad range of water regimes in the Mediterranean Basin and other locations. Multi-trait prediction analyses were performed by implementing a multi-trait deep learning model (MTDL) with a feed-forward network topology 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 were also compared with univariate predictions of the genomic best linear unbiased predictor (GBLUP) method and the univariate counterpart of the multi-trait deep learning method (UDL). 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 UDL and MTDL methods. However, under the GBLUP method, the best predictions were observed when the genotype × environment interaction term was taken into account. We also found that in general the best predictions were observed under the GBLUP model; however, the predictions of the MTDL 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.https://www.frontiersin.org/article/10.3389/fpls.2019.01311/fulldurum wheatdeep learningmulti-traitunivariate traitGBLUPgenomic selection |
| spellingShingle | Osval A. Montesinos-López Abelardo Montesinos-López Roberto Tuberosa Marco Maccaferri Giuseppe Sciara Karim Ammar José Crossa Multi-Trait, Multi-Environment Genomic Prediction of Durum Wheat With Genomic Best Linear Unbiased Predictor and Deep Learning Methods Frontiers in Plant Science durum wheat deep learning multi-trait univariate trait GBLUP genomic selection |
| title | Multi-Trait, Multi-Environment Genomic Prediction of Durum Wheat With Genomic Best Linear Unbiased Predictor and Deep Learning Methods |
| title_full | Multi-Trait, Multi-Environment Genomic Prediction of Durum Wheat With Genomic Best Linear Unbiased Predictor and Deep Learning Methods |
| title_fullStr | Multi-Trait, Multi-Environment Genomic Prediction of Durum Wheat With Genomic Best Linear Unbiased Predictor and Deep Learning Methods |
| title_full_unstemmed | Multi-Trait, Multi-Environment Genomic Prediction of Durum Wheat With Genomic Best Linear Unbiased Predictor and Deep Learning Methods |
| title_short | Multi-Trait, Multi-Environment Genomic Prediction of Durum Wheat With Genomic Best Linear Unbiased Predictor and Deep Learning Methods |
| title_sort | multi trait multi environment genomic prediction of durum wheat with genomic best linear unbiased predictor and deep learning methods |
| topic | durum wheat deep learning multi-trait univariate trait GBLUP genomic selection |
| url | https://www.frontiersin.org/article/10.3389/fpls.2019.01311/full |
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