000			03868nab|a22004817a|4500
001			66317
003			MX-TxCIM
005			20240919021234.0
008			20234s2023||||mx |||p|op||||00||0|eng|d
022			_a2073-4425
024	8		_ahttps://doi.org/10.3390/genes14040927
040			_aMX-TxCIM
041			_aeng
100	1		_aMontesinos-Lopez, O.A. _8I1706800 _92700 _gGenetic Resources Program
245	1	0	_aOptimizing sparse testing for genomic prediction of plant breeding crops
260			_bMDPI, _c2023. _aBasel (Switzerland) :
500			_aPeer review
500			_aOpen Access
520			_aWhile sparse testing methods have been proposed by researchers to improve the efficiency of genomic selection (GS) in breeding programs, there are several factors that can hinder this. In this research, we evaluated four methods (M1–M4) for sparse testing allocation of lines to environments under multi-environmental trails for genomic prediction of unobserved lines. The sparse testing methods described in this study are applied in a two-stage analysis to build the genomic training and testing sets in a strategy that allows each location or environment to evaluate only a subset of all genotypes rather than all of them. To ensure a valid implementation, the sparse testing methods presented here require BLUEs (or BLUPs) of the lines to be computed at the first stage using an appropriate experimental design and statistical analyses in each location (or environment). The evaluation of the four cultivar allocation methods to environments of the second stage was done with four data sets (two large and two small) under a multi-trait and uni-trait framework. We found that the multi-trait model produced better genomic prediction (GP) accuracy than the uni-trait model and that methods M3 and M4 were slightly better than methods M1 and M2 for the allocation of lines to environments. Some of the most important findings, however, were that even under a scenario where we used a training-testing relation of 15–85%, the prediction accuracy of the four methods barely decreased. This indicates that genomic sparse testing methods for data sets under these scenarios can save considerable operational and financial resources with only a small loss in precision, which can be shown in our cost-benefit analysis.
546			_aText in English
591			_aMontesinos-Lopez, O.A. : No CIMMYT Affiliation
650		7	_aMaize _2AGROVOC _91173
650		7	_aTesting _2AGROVOC _912144
650		7	_aWheat _2AGROVOC _91310
650		7	_aPlant breeding _gAGROVOC _2 _91203
650		7	_aCrops _2AGROVOC _91069
700	1		_aSaint Pierre, C. _8INT2731 _9855 _gGlobal Wheat Program
700	1		_aGezan, S.A. _931067
700	1		_aBentley, A.R. _8001712492 _gFormerly Global Wheat Program _99599
700	1		_aMosqueda-Gonzalez, B.A. _919441
700	1		_aMontesinos-Lopez, A. _92702
700	1		_aEeuwijk, F.A. van _99549
700	1		_aBeyene, Y. _8INT2891 _9870 _gGlobal Maize Program
700	1		_aGowda, M. _8I1705963 _9795 _gGlobal Maize Program
700	1		_aGardner, K.A. _8001712617 _gGenetic Resources Program _917393
700	1		_aGerard, G.S. _81713398 _911490 _gGlobal Wheat Program
700	1		_aCrespo-Herrera, L.A. _8I1706538 _92608 _gGlobal Wheat Program
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
773	0		_tGenes _gv. 14, no. 4, art. 927 _dBasel (Switzerland) : MDPI, 2023 _x2073-4425
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/22625
942			_cJA _n0 _2ddc
999			_c66317 _d66309