000			02865nab|a22003137a|4500
999			_c62560 _d62552
001			62560
003			MX-TxCIM
005			20240919020952.0
008			200911s2021||||xxk|||p|op||||00||0|eng|d
022			_a1365-2540 (Online)
024	8		_ahttps://doi.org/10.1038/s41437-020-00353-1
040			_aMX-TxCIM
041			_aeng
100	1		_915939 _aCosta-Neto, G. _8001712813 _gGenetic Resources Program
245	1	0	_aNonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials
260			_aHarlow (United Kingdom) : _bSpringer Nature, _c2021.
500			_aPeer review
500			_aOpen Access
520			_aModern whole-genome prediction (WGP) frameworks that focus on multi-environment trials (MET) integrate large-scale genomics, phenomics, and envirotyping data. However, the more complex the statistical model, the longer the computational processing times, which do not always result in accuracy gains. We investigated the use of new kernel methods and modeling structures involving genomics and nongenomic sources of variation in two MET maize data sets. Five WGP models were considered, advancing in complexity from a main-effect additive model (A) to more complex structures, including dominance deviations (D), genotype x environment interaction (AE and DE), and the reaction-norm model using environmental covariables (W) and their interaction with A and D (AW + DW). A combination of those models built with three different kernel methods, Gaussian kernel (GK), Deep kernel (DK), and the benchmark genomic best linear-unbiased predictor (GBLUP/GB), was tested under three prediction scenarios: newly developed hybrids (CV1), sparse MET conditions (CV2), and new environments (CV0). GK and DK outperformed GB in prediction accuracy and reduction of computation time (similar to up to 20%) under all model-kernel scenarios. GK was more efficient in capturing the variation due to A + AE and D + DE effects and translated it into accuracy gains (similar to up to 85% compared with GB). DK provided more consistent predictions, even for more complex structures such as W + AW + DW. Our results suggest that DK and GK are more efficient in translating model complexity into accuracy, and more suitable for including dominance and reaction-norm effects in a biologically accurate and faster way.
546			_aText in English
650		7	_aEvolution _2AGROVOC _98815
650		7	_aGenomics _2AGROVOC _91132
650		7	_aModels _2AGROVOC _94859
700	1		_aFritsche-Neto, R. _96507
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
773	0		_dHarlow (United Kingdom) : Springer Nature, 2021. _x0018-067X _gv. 126, p. 92-106 _tHeredity _wu444336
856	8		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/20953
942			_cJA _n0 _2ddc