000			03077nab a22003857a 4500
999			_c59300 _d59292
001			59300
003			MX-TxCIM
005			20240919020949.0
008			180226s2017 mdu|||p|op||| 00| 0 eng d
024	8		_ahttps://doi.org/10.1534/g3.117.042341
040			_aMX-TxCIM
041			_aeng
100	1		_96505 _aBandeira e Sousa, M.
245	1	0	_aGenomic-enabled prediction in maize using kernel models with genotype x environment interaction
260			_aBethesda, Maryland, U.S. : _bGenetics Society of America, _c2017.
500			_aPeer review
500			_aOpen Access
520			_aMulti-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied.
526			_aMCRP _bFP2
546			_aText in English
650		7	_92701 _aForecasting _2AGROVOC
650		7	_91132 _aGenomics _2AGROVOC
650		7	_aMaize _gAGROVOC _2 _91173
650		7	_91133 _aGenotype environment interaction _2AGROVOC
700	1		_94437 _aCuevas, J.
700	1		_96506 _aDe Oliveira Couto, E.G.
700	1		_92703 _aPerez-Rodriguez, P.
700	1		_91934 _aJarquín, D.
700	1		_96507 _aFritsche-Neto, R.
700	1		_9907 _aBurgueño, J. _gGenetic Resources Program _8INT3239
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
773	0		_gv. 7, no, 6, p. 1995-2014 _tG3: Genes, Genomes, Genetics _wu56922 _x2160-1836
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/19328
942			_2ddc _cJA _n0