000			03164nab|a22003617a|4500
999			_c61165 _d61157
001			61165
003			MX-TxCIM
005			20240919020951.0
008			191208s2019||||sz |||p|op||||00||0|eng|d
022			_a1664-462X
024	8		_ahttps://doi.org/10.3389/fpls.2019.01311
040			_aMX-TxCIM
041			_aeng
100	1		_aMontesinos-Lopez, O.A. _8I1706800 _92700 _gGenetic Resources Program
245	1	0	_aMulti-trait, multi-environment genomic prediction of durum wheat with genomic best linear unbiased predictor and deep learning methods
260			_aSwitzerland : _bFrontiers, _c2019.
500			_aPeer review
500			_aOpen Access
520			_aAlthough 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.
546			_aText in English
650		7	_aHard wheat _2AGROVOC _91142
650		7	_aMarker-assisted selection _2AGROVOC _910737
650		7	_aAgronomic characters _gAGROVOC _2 _91008
700	1		_aMontesinos-Lopez, A. _92702
700	1		_aTuberosa, R. _92806
700	1		_aMaccaferri, M. _92805
700	1		_aSciara, G. _911000
700	1		_aAmmar, K. _8INT2585 _9844 _gGlobal Wheat Program
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
773	0		_gv. 10, art. 1311 _dSwitzerland : Frontiers, 2019. _x1664-462X _tFrontiers in Plant Science _wu56875
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/20598
942			_cJA _n0 _2ddc