000			03048nab|a22003617a|4500
999			_c59490 _d59482
001			59490
003			MX-TxCIM
005			20240919020950.0
008			180425s2018||||xxu|||p|op||||00||0|eng|d
024	8		_ahttps://doi.org/10.1534/g3.117.300454
040			_aMX-TxCIM
041			_aeng
100	1		_94437 _aCuevas, J.
245	1	0	_aGenomic-enabled prediction Kernel models with random intercepts for multi-environment trials _h[Electronic Resource]
260			_aBethesda, Md, U.S. : _bGenetics Society of America, _c2018.
500			_aPeer review
500			_aOpen Access
520			_aIn this study, we compared the prediction accuracy of the main genotypic effect model (MM) without G×E interactions, the multi-environment single variance G×E deviation model (MDs), and the multienvironment environment-specific variance G×E deviation model (MDe) where the random genetic effects of the lines are modeled with the markers (or pedigree). With the objective of further modeling the genetic residual of the lines, we incorporated the random intercepts of the lines (l) and generated another three models. Each of these 6 models were fitted with a linear kernel method (Genomic Best Linear Unbiased Predictor, GB) and a Gaussian Kernel (GK) method. We compared these 12 model-method combinations with another two multi-environment G×E interactions models with unstructured variance-covariances (MUC) using GB and GK kernels (4 model-method). Thus, we compared the genomic-enabled prediction accuracy of a total of 16 model-method combinations on two maize data sets with positive phenotypic correlations among environments, and on two wheat data sets with complex G×E that includes some negative and close to zero phenotypic correlations among environments. The two models (MDs and MDE with the random intercept of the lines and the GK method) were computationally efficient and gave high prediction accuracy in the two maize data sets. Regarding the more complex G×E wheat data sets, the prediction accuracy of the model-method combination with G×E, MDs and MDe, including the random intercepts of the lines with GK method had important savings in computing time as compared with the G×E interaction multi-environment models with unstructured variance-covariances but with lower genomic prediction accuracy.
546			_aText in English
650		7	_91168 _aKernels _2AGROVOC
650		7	_aMaize _gAGROVOC _2 _91173
650		7	_91133 _aGenotype environment interaction _2AGROVOC
650		7	_91132 _aGenomics _2AGROVOC
700	1		_97519 _aGranato, I.
700	1		_96507 _aFritsche-Neto, R.
700	1		_aMontesinos-Lopez, O.A. _92700
700	1		_9907 _aBurgueño, J. _gGenetic Resources Program _8INT3239
700	1		_96505 _aBandeira e Sousa, M.
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
773	0		_tG3: Genes, Genomes, Genetics _gv. 8, no. 4, p. 1347-1365 _dG3, 2018 _x2160-1836 (Online) _wu56922
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/19499
942			_cJA _n0 _2ddc