000			03136nab|a22004337a|4500
999			_c62979 _d62971
001			62979
003			MX-TxCIM
005			20240919020952.0
008			200910s2020||||xxu|||p|op||||00||0|eng|d
022			_a2160-1836 (Online)
024	8		_ahttps://doi.org/10.1534/g3.120.401172
040			_aMX-TxCIM
041			_aeng
100	1		_aMageto, E.K. _95413
245	1	0	_aGenomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm
260			_aBethesda, MD (USA) : _bGenetics Society of America, _c2020.
500			_aPeer review
500			_aOpen Access
520			_aZinc (Zn) deficiency is a major risk factor for human health, affecting about 30% of the world’s population. To study the potential of genomic selection (GS) for maize with increased Zn concentration, an association panel and two doubled haploid (DH) populations were evaluated in three environments. Three genomic prediction models, M (M1: Environment + Line, M2: Environment + Line + Genomic, and M3: Environment + Line + Genomic + Genomic x Environment) incorporating main effects (lines and genomic) and the interaction between genomic and environment (G x E) were assessed to estimate the prediction ability (rMP) for each model. Two distinct cross-validation (CV) schemes simulating two genomic prediction breeding scenarios were used. CV1 predicts the performance of newly developed lines, whereas CV2 predicts the performance of lines tested in sparse multi-location trials. Predictions for Zn in CV1 ranged from -0.01 to 0.56 for DH1, 0.04 to 0.50 for DH2 and -0.001 to 0.47 for the association panel. For CV2, rMP values ranged from 0.67 to 0.71 for DH1, 0.40 to 0.56 for DH2 and 0.64 to 0.72 for the association panel. The genomic prediction model which included G x E had the highest average rMP for both CV1 (0.39 and 0.44) and CV2 (0.71 and 0.51) for the association panel and DH2 population, respectively. These results suggest that GS has potential to accelerate breeding for enhanced kernel Zn concentration by facilitating selection of superior genotypes.
526			_aMCRP _cFP2
546			_aText in English
650		7	_2AGROVOC _91314 _aZea mays
650		7	_2AGROVOC _91130 _aGenetics
650		7	_aBreeding _gAGROVOC _2 _91029
650		7	_2AGROVOC _91315 _aZinc
650		7	_2AGROVOC _91133 _aGenotype environment interaction
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_92703 _aPerez-Rodriguez, P.
700	1		_9935 _aDhliwayo, T. _8INT3355 _gGlobal Maize Program
700	1		_9850 _aPalacios-Rojas, N. _8INT2691 _gGlobal Maize Program
700	1		_95361 _aLee, M.
700	0		_911572 _aRui Guo
700	1		_9884 _aSan Vicente, F.M. _8INT3035 _gGlobal Maize Program
700	0		_aXuecai Zhang _gGlobal Maize Program _8INT3400 _9951
700	1		_96100 _aHindu, V.
773	0		_tG3: Genes, Genomes, Genetics _gv. 10, no. 8, p. 2629-2639 _dBethesda, MD (USA) : Genetics Society of America, 2020. _x2160-1836 _wu56922
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/21065
942			_cJA _n0 _2ddc