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008			20255s2025|||||-us||p|op||||00||0|eng|dd
022			_a2160-1836
024	8		_ahttps://doi.org/10.1093/g3journal/jkaf087
040			_aMX-TxCIM
041			_aeng
100	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
245	1	0	_aEvaluating the effectiveness of selection indices and their genomic prediction using environmental and historical rice data
260			_aBethesda, MD (United States of America) : _bOxford University Press, _c2025.
500			_aPeer review
500			_aOpen Access
520			_aImproving genetic gains in rice breeding programs requires accurate prediction methods for selection indices. Effective use of genomic prediction could significantly accelerate breeding cycles. The Smith index method (SIM), the eigenvalue selection index method (ESIM), and the desired gain index (DG) are linear combinations of trait phenotypic values y (I=b ' y), and while the SIM and ESIM predict the net genetics merit (H=w ' c), where w is the vector of economic weights and c is the unobserved genotypic values, the DG predicts the mean of genotypic values. To enhance genomic prediction accuracy, mixed linear and Bayesian models incorporate molecular markers to estimate genomic effects, resulting in genomic estimated breeding values. This study evaluated (1) the efficiency of the SIM, ESIM, and DG through their main parameters and (2) the predictive accuracy of 5 genomic prediction models utilizing historical rice (Oryza sativa) data from 2018 to 2021 to predict selection indices for 2022. The correlation between observed and predicted indices assessed the effectiveness of each genomic model. Models incorporating year-specific and environmental covariates significantly improved predictive performance. These findings underscore the importance of environmental covariates and indicate that the SIM is the most effective method for maximizing key index parameters, while the ESIM provides the best predictive accuracy for indices. Consequently, rice breeders are encouraged to use these indices to enhance genetic gains per selection cycle.
546			_aText in English
591			_aCeron Rojas, J.J. : Not in IRS staff list but CIMMYT Affiliation
591			_aMontesinos-Lopez, O.A. : No CIMMYT Affiliation
597			_dLouisiana State University Agricultural Center (LSU AgCenter)
650		7	_aGenomics _2AGROVOC _91132
650		7	_aBreeding _2AGROVOC _91029
650		7	_aForecasting _2AGROVOC _92701
650		7	_aRice _91243 _2AGROVOC
700	1		_aCeron Rojas, J.J. _91932
700	1		_aMontesinos-Lopez, A. _92702
700	1		_aMontesinos-Lopez, O.A. _gGenetic Resources Program _8I1706800 _92700
700	1		_aPunzalan, J. _939082
700	1		_aFamoso, A. _939083
700	1		_aFritsche-Neto, R. _96507
773	0		_tG3: Genes, Genomes, Genetics _dBethesda, MD (United States of America) : Oxford University Press, 2025. _x2160-1836 _gv. 15, no. 6, art. jkaf087 _w 56922
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/35699
942			_cJA _n0 _2ddc
999			_c68822 _d68814