000			04040nab|a22005057a|4500
999			_c59907 _d59899
001			59907
003			MX-TxCIM
005			20240919021003.0
008			190115s2019||||gw |||p|op||||00||0|eng|d
024	8		_ahttps://doi.org/10.1007/s00122-018-3206-3
040			_aMX-TxCIM
041			_aeng
100	1		_aJULIANA P. _8001710082 _gFormerly ​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​Global Wheat Program _gFormerly BISA _92690
245	1		_aIntegrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat _h[Electronic Resource]
260			_aGermany : _bSpringer, _c2019.
500			_aPeer review
500			_aOpen Access
520			_aGenomic selection and high-throughput phenotyping (HTP) are promising tools to accelerate breeding gains for high-yielding and climate-resilient wheat varieties. Hence, our objective was to evaluate them for predicting grain yield (GY) in drought-stressed (DS) and late-sown heat-stressed (HS) environments of the International maize and wheat improvement center?s elite yield trial nurseries. We observed that the average genomic prediction accuracies using fivefold cross-validations were 0.50 and 0.51 in the DS and HS environments, respectively. However, when a different nursery/year was used to predict another nursery/year, the average genomic prediction accuracies in the DS and HS environments decreased to 0.18 and 0.23, respectively. While genomic predictions clearly outperformed pedigree-based predictions across nurseries, they were similar to pedigree-based predictions within nurseries due to small family sizes. In populations with some full-sibs in the training population, the genomic and pedigree-based prediction accuracies were on average 0.27 and 0.35 higher than the accuracies in populations with only one progeny per cross, indicating the importance of genetic relatedness between the training and validation populations for good predictions. We also evaluated the item-based collaborative filtering approach for multivariate prediction of GY using the green normalized difference vegetation index from HTP. This approach proved to be the best strategy for across-nursery predictions, with average accuracies of 0.56 and 0.62 in the DS and HS environments, respectively. We conclude that GY is a challenging trait for across-year predictions, but GS and HTP can be integrated in increasing the size of populations screened and evaluating unphenotyped large nurseries for stress?resilience within years.
526			_aWC _cFP3
546			_aText in English
591			_aGonzalez-Perez, L. : Not in IRS Staff list but CIMMYT Affiliation
650		7	_2AGROVOC _91132 _aGenomics
650		7	_2AGROVOC _93634 _aPhenotypes
650		7	_aBreeding _gAGROVOC _2 _91029
650		7	_2AGROVOC _91048 _aClimatic factors
650		7	_91265 _aSoft wheat _2AGROVOC
650		7	_2AGROVOC _91045 _aClimate change
650		7	_2AGROVOC _95030 _aResilience
700	1		_aMontesinos-Lopez, O.A. _92700
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_aMondal, S. _gFormerly Global Wheat Program _8INT3211 _9904
700	1		_93850 _aGonzalez-Perez, L.
700	1		_92092 _aPoland, J.A.
700	1		_aHuerta-Espino, J. _gGlobal Wheat Program _8CHUE01 _9397
700	1		_92608 _aCrespo-Herrera, L.A. _gGlobal Wheat Program _8I1706538
700	1		_aVelu, G. _8INT2983 _9880 _gGlobal Wheat Program
700	1		_9851 _aDreisigacker, S. _gGlobal Wheat Program _8INT2692
700	1		_aShrestha, S. _98259
700	1		_92703 _aPerez-Rodriguez, P.
700	1		_aPinto Espinosa, F. _8I1707012 _gFormerly Global Wheat Program _94431
700	1		_aSingh, R.P. _gGlobal Wheat Program _8INT0610 _9825
773	0		_dGermany _gv. 132, no. 1, p. 177-194 _tTheoretical and Applied Genetics _wu444762 _x1432-2242
856	4		_uhttps://hdl.handle.net/10883/19787 _yOpen Access through DSpace
942			_cJA _2ddc _n0