000			03171nab a22003737a 4500
999			_c59712 _d59704
001			59712
003			MX-TxCIM
005			20240919021226.0
008			180918s2018||||mdu|||p|op||||00||0|eng|d
024	8		_ahttps://doi.org/10.1534/g3.118.200430
040			_aMX-TxCIM
041			_aeng
100	1		_aVillar-Hernandez, B.d.J. _97952
245	1	0	_aA Bayesian decision theory approach for genomic selection _h[Electronic Resource]
260			_aBethesda, Md., U.S. : _bGenetics Society of America, _c2018.
500			_aPeer review
500			_aOpen Access
520			_aPlant and animal breeders are interested in selecting the best individuals from a candidate set for the next breeding cycle. In this paper, we propose a formal method under the Bayesian decision theory framework to tackle the selection problem based on genomic selection (GS) in single- and multi-trait settings. We proposed and tested three univariate loss functions (Kullback-Leibler, KL; Continuous Ranked Probability Score, CRPS; Linear-Linear loss, LinLin) and their corresponding multivariate generalizations (Kullback-Leibler, KL; Energy Score, EnergyS; and the Multivariate Asymmetric Loss Function, MALF). We derived and expressed all the loss functions in terms of heritability and tested them on a real wheat dataset for one cycle of selection and in a simulated selection program. The performance of each univariate loss function was compared with the standard method of selection (Std) that does not use loss functions. We compared the performance in terms of the selection response and the decrease in the population's genetic variance during recurrent breeding cycles. Results suggest that it is possible to obtain better performance in a long-term breeding program using the single-trait scheme by selecting 30% of the best individuals in each cycle but not by selecting 10% of the best individuals. For the multi-trait approach, results show that the population mean for all traits under consideration had positive gains, even though two of the traits were negatively correlated. The corresponding population variances were not statistically different from the different loss function during the 10th selection cycle. Using the loss function should be a useful criterion when selecting the candidates for selection for the next breeding cycle.
546			_aText in English
591			_bCIMMYT Informa : 2022 (Octubre 25, 2018)
650		7	_2AGROVOC _94013 _aBayesian theory
650		7	_2AGROVOC _91132 _aGenomics
650		7	_aPlant breeding _gAGROVOC _2 _91203
650		7	_2AGROVOC _94749 _aSelection
650		7	_2AGROVOC _98687 _aSimulation
700	1		_94434 _aPerez-Elizalde, S.
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_92703 _aPerez-Rodriguez, P.
700	1		_aToledo, F.H. _8I1706676 _91999 _gGenetic Resources Program
700	1		_9907 _aBurgueño, J. _gGenetic Resources Program _8INT3239
773	0		_tG3: Genes, Genomes, Genetics _gv. 8, no. 9, p. 3019-3037 _dGenetics Society of America, 2018 _x(Online) 2160-1836 _wu56922
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/19624
942			_cJA _n0 _2ddc