TY  - JA
AU  - Montesinos-Lopez,O.A.
AU  - Montesinos-Lopez,A.
AU  - Tuberosa,R.
AU  - Maccaferri,M.
AU  - Sciara,G.
AU  - Ammar,K.
AU  - Crossa,J.
TI  - Multi-trait, multi-environment genomic prediction of durum wheat with genomic best linear unbiased predictor and deep learning methods
SN  - 1664-462X
PY  - 2019///
CY  - Switzerland
PB  - Frontiers
KW  - Hard wheat
KW  - AGROVOC
KW  - Marker-assisted selection
KW  - Agronomic characters
N1  - Peer review; Open Access
N2  - 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 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
UR  - https://hdl.handle.net/10883/20598
T2  - Frontiers in Plant Science
DO  - https://doi.org/10.3389/fpls.2019.01311
ER  -