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

dc.creatorMontesinos-Lopez, O.A.
dc.creatorMontesinos-Lopez, A.
dc.creatorTuberosa, R.
dc.creatorMaccaferri, M.
dc.creatorSciara, G.
dc.creatorAmmar, K.
dc.creatorCrossa, J.
dc.date2019-12-19T01:10:18Z
dc.date2019-12-19T01:10:18Z
dc.date2019
dc.date.accessioned2023-07-17T20:05:18Z
dc.date.available2023-07-17T20:05:18Z
dc.identifier1664-462X (Print)
dc.identifierhttps://hdl.handle.net/10883/20598
dc.identifier10.3389/fpls.2019.01311
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7512401
dc.descriptionAlthough 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 x environment interaction term. We found that the best predictions were observed without the genotype x environment interaction term in the UDL and MTDL methods. However, under the GBLUP method, the best predictions were observed when the genotype x 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.
dc.formatPDF
dc.languageEnglish
dc.publisherFrontiers
dc.relationhttp://hdl.handle.net/11529/10548262
dc.rightsOpen Access
dc.sourceart. 1311
dc.source10
dc.sourceFrontiers in Plant Science
dc.subjectHARD WHEAT
dc.subjectMARKER-ASSISTED SELECTION
dc.subjectAGRONOMIC CHARACTERS
dc.titleMulti-trait, multi-environment genomic prediction of durum wheat with genomic best linear unbiased predictor and deep learning methods
dc.typeArticle
dc.typePublished Version
dc.coverageSwitzerland


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