000			04235nab|a22005297a|4500
001			68736
003			MX-TxCIM
005			20251223170053.0
008			20254s22025||||-us||p|op||||00||0|eng|dd
022			_a2160-1836
024	8		_ahttps://doi.org/10.1093/g3journal/jkaf038
040			_aMX-TxCIM
041			_aeng
100	1		_8001713327 _aVitale, P. _gGlobal Wheat Program _gGenetic Resources Program _931497
245	1	0	_aImproving wheat grain yield genomic prediction accuracy using historical data
260			_aBethesda, MD (United States of America) : _bOxford University Press, _c2025.
500			_aPeer review
500			_aOpen Access
520			_aGenomic selection is an essential tool to improve genetic gain in wheat breeding. This study aimed to enhance prediction accuracy for grain yield across various selection environments using CIMMYT's (International Maize and Wheat Improvement Center) historical dataset. Ten years of grain yield data from 6 selection environments were analyzed, with the populations of 5 years (2018-2023) as the validation population and earlier years (back to 2013-2014) as the training population. Generally, we observed that as the number of training years increased, the prediction accuracy tended to improve or stabilize. For instance, in the late heat stress selection environment (beds late heat stress), prediction accuracy increased from 0.11 (1 training year) to 0.23 (5 years), stabilizing at 0.26. Similar trends were observed in the intermediate drought selection environment (beds with 2 irrigations), with prediction accuracy rising from 0.12 (1 year) to 0.21 (4 years) but minimal improvement beyond that. Conversely, some selection environments, such as flat 5 irrigations (flat optimal environment), did not significantly increase, with the prediction accuracy fluctuating around 0.09-0.14 regardless of the number of training years used. Additionally, average genetic diversity within the training population and the validation population influenced prediction accuracy. Indeed, a negative correlation between prediction accuracy and the genetic distance was observed. This highlights the need to balance genetic diversity to enhance the predictive power of genomic selection models. These findings exhibit the benefits of using an extended historical dataset while considering genetic diversity to maximize prediction accuracy in genomic selection strategies for wheat breeding, ultimately supporting the development of high-yielding varieties.
546			_aText in English
591			_aMontesinos-Lopez, O.A. ; Not in IRS staff list but CIMMYT Affiliation
597			_dBill & Melinda Gates Foundation (BMGF) _dAccelerating Genetic Gains in Maize and Wheat (AGG) _fBreeding for Tomorrow _uhttps://hdl.handle.net/10568/179135
650		7	_aGenomics _2AGROVOC _91132
650		7	_aForecasting _2AGROVOC _92701
650		7	_aPlant breeding _2AGROVOC _91203
650		7	_aWheat _2AGROVOC _91310
650		7	_aData _2AGROVOC _99002
650		7	_aGrain _2AGROVOC _91138
650		7	_aYields _2AGROVOC _91313
700	1		_aMontesinos-Lopez, O.A. _gGenetic Resources Program _8I1706800 _92700
700	1		_aGerard, G.S. _81713398 _gGlobal Wheat Program _911490
700	1		_aVelu, G. _gGlobal Wheat Program _8INT2983 _9880
700	1		_aTarekegn, Z.T. _8001713397 _gGlobal Wheat Program _931150
700	1		_aMontesinos-Lopez, A. _92702
700	1		_aDreisigacker, S. _gGlobal Wheat Program _8INT2692 _9851
700	1		_aPacheco Gil, R.A. _8N1705917 _gGenetic Resources Program _96455
700	1		_aToledo, F.H. _gGenetic Resources Program _8I1706676 _91999
700	1		_aSaint Pierre, C. _gGlobal Wheat Program _8INT2731 _9855
700	1		_aPerez-Rodriguez, P. _92703
700	1		_aGardner, K.A. _8001712617 _gGenetic Resources Program _917393
700	1		_aCrespo-Herrera, L.A. _gGlobal Wheat Program _8I1706538 _92608
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
773	0		_tG3: Genes, Genomes, Genetics _dBethesda, MD (United States of America) : Oxford University Press, 2025 _x2160-1836 _gv. 15, no. 4, art. jkaf038 _w56922
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/35621
942			_cJA _n0 _2ddc
999			_c68736 _d68728