000			03407nab|a22004097a|4500
001			65174
003			MX-TxCIM
005			20240919020954.0
008			20221s2022||||mx |||p|op||||00||0|eng|d
022			_a0040-5752
022			_a1432-2242 (Online)
024	8		_ahttps://doi.org/10.1007/s00122-022-04085-0
040			_aMX-TxCIM
041			_aeng
100	1		_aAtanda, A.S. _8001711295 _8001712571 _98531 _gGlobal Maize Program _gFormerly Global Wheat Program
245	1	1	_aSparse testing using genomic prediction improves selection for breeding targets in elite spring wheat
260			_bSpringer, _c2022. _aBerlin (Germany) :
500			_aPeer review
520			_aKey message: Sparse testing using genomic prediction can be efficiently used to increase the number of testing environments while maintaining selection intensity in the early yield testing stage without increasing the breeding budget. Abstract: Sparse testing using genomic prediction enables expanded use of selection environments in early-stage yield testing without increasing phenotyping cost. We evaluated different sparse testing strategies in the yield testing stage of a CIMMYT spring wheat breeding pipeline characterized by multiple populations each with small family sizes of 1–9 individuals. Our results indicated that a substantial overlap between lines across environments should be used to achieve optimal prediction accuracy. As sparse testing leverages information generated within and across environments, the genetic correlations between environments and genomic relationships of lines across environments were the main drivers of prediction accuracy in multi-environment yield trials. Including information from previous evaluation years did not consistently improve the prediction performance. Genomic best linear unbiased prediction was found to be the best predictor of true breeding value, and therefore, we propose that it should be used as a selection decision metric in the early yield testing stages. We also propose it as a proxy for assessing prediction performance to mirror breeder’s advancement decisions in a breeding program so that it can be readily applied for advancement decisions by breeding programs.
546			_aText in English
650		7	_aGenes _2AGROVOC _93563
650		7	_aBreeding programmes _2AGROVOC _921704
650		7	_aGenomics _91132 _2AGROVOC
650		7	_aAccuracy _2AGROVOC _927100
650		7	_aEnvironment _2AGROVOC _91098
650		7	_aSpring wheat _2AGROVOC _91806
650		7	_aForecasting _2AGROVOC _92701
700	1		_aVelu, G. _8INT2983 _9880 _gGlobal Wheat Program
700	1		_aSingh, R.P. _8INT0610 _9825 _gGlobal Wheat Program
700	1		_aRobbins, K. _95987
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_aBentley, A.R. _8001712492 _gFormerly Global Wheat Program _99599
773	0		_tTheoretical and Applied Genetics _dBerlin (Germany) : Springer, 2022 _x0040-5752 _gv 135, no. 6, p. 1939–1950 _wG444762
856			_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/22045
942			_cJA _n0 _2ddc
999			_c65174 _d65166