000			03611nab|a22004217a|4500
999			_c60940 _d60932
001			60940
003			MX-TxCIM
005			20240919021226.0
008			190913s2019||||xxu|||p|op||||00||0|eng|d
022			_a2160-1836
024	8		_ahttps://doi.org/10.1534/g3.119.400508
040			_aMX-TxCIM
041			_aeng
100	1		_aHoward, R. _97898
245	1	0	_aJoint use of genome, pedigree, and their interaction with environment for predicting the performance of wheat lines in new environments
260			_aBethesda, MD (USA) : _bGenetics Society of America, _c2019.
500			_aPeer review
500			_aOpen Access
520			_aGenome-enabled prediction plays an essential role in wheat breeding because it has the potential to increase the rate of genetic gain relative to traditional phenotypic and pedigree-based selection. Since the performance of wheat lines is highly influenced by environmental stimuli, it is important to accurately model the environment and its interaction with genetic factors in prediction models. Arguably, multi-environmental best linear unbiased prediction (BLUP) may deliver better prediction performance than single-environment genomic BLUP. We evaluated pedigree and genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree information as prediction inputs in two different validation schemes. All models included main effects, but some considered interactions between the different types of pedigree and genomic covariates via Hadamard products of similarity kernels. Pedigree models always gave better prediction of new lines in observed environments than genome-based models when only main effects were fitted. However, for all traits, the highest predictive abilities were obtained when interactions between pedigree, genomes, and environments were included. When new lines were predicted in unobserved environments, in almost all trait/year combinations, the marker main-effects model was the best. These results provide strong evidence that the different sources of genetic information (molecular markers and pedigree) are not equally useful at different stages of the breeding pipelines, and can be employed differentially to improve the design and prediction of the outcome of future breeding programs.
546			_aText in English
650		7	_aGenomes _gAGROVOC _2 _91131
650		7	_2AGROVOC _91133 _aGenotype environment interaction
650		7	_aPlant breeding _gAGROVOC _2 _91203
650		7	_2AGROVOC _97028 _aBreeding lines
650		7	_aWheat _gAGROVOC _2 _91310
700	1		_aGianola, D. _97797
700	1		_aMontesinos-Lopez, O.A. _8I1706800 _92700 _gGenetic Resources Program
700	1		_aJULIANA P. _8001710082 _gFormerly ​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​Global Wheat Program _gFormerly BISA _92690
700	1		_aSingh, R.P. _gGlobal Wheat Program _8INT0610 _9825
700	1		_aPoland, J.A. _92092
700	1		_aShrestha, S. _98259
700	1		_aPerez-Rodriguez, P. _92703
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_aJarquín, D. _91934
773	0		_tG3: Genes, Genomes, Genetics _gv. 9, no. 9, p. 2925-2934 _dBethesda, MD (USA) : Genetics Society of America, 2019. _x2160-1836 _wu56922
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/20542
942			_cJA _n0 _2ddc