000			03239nab a22004697a 4500
999			_c58151 _d58143
001			58151
003			MX-TxCIM
005			20250211020917.0
008			161010s2016 -uk|||p|op||| 00| 00eng d
024	8		_ahttps://doi.org/10.1038/srep27312
040			_aMX-TxCIM
041			_aeng
100	1		_8INT2731 _9855 _aSaint Pierre, C. _gGlobal Wheat Program
245	1	0	_aGenomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones _h[Electronic Resource]
260			_aLondon : _bNature Publishing Group, _c2016.
500			_aPeer review
500			_aOpen Access
520			_aGenomic and pedigree predictions for grain yield and agronomic traits were carried out using high density molecular data on a set of 803 spring wheat lines that were evaluated in 5 sites characterized by several environmental co-variables. Seven statistical models were tested using two random cross-validations schemes. Two other prediction problems were studied, namely predicting the lines’ performance at one site with another (pairwise-site) and at untested sites (leave-one-site-out). Grain yield ranged from 3.7 to 9.0 t ha−1 across sites. The best predictability was observed when genotypic and pedigree data were included in the models and their interaction with sites and the environmental co-variables. The leave-one-site-out increased average prediction accuracy over pairwise-site for all the traits, specifically from 0.27 to 0.36 for grain yield. Days to anthesis, maturity, and plant height predictions had high heritability and gave the highest accuracy for prediction models. Genomic and pedigree models coupled with environmental co-variables gave high prediction accuracy due to high genetic correlation between sites. This study provides an example of model prediction considering climate data along-with genomic and pedigree information. Such comprehensive models can be used to achieve rapid enhancement of wheat yield enhancement in current and future climate change scenario.
546			_aText in English
591			_bCIMMYT Informa: 1987 (March 23, 2017)
650		7	_aWheat _gAGROVOC _2 _91310
650		7	_93995 _aAgroecology _2AGROVOC
650		7	_91132 _aGenomics _2AGROVOC
700	1		_9907 _aBurgueño, J. _gGenetic Resources Program _8INT3239
700	1		_aFuentes-Dávila, G. _93412
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_91863 _aFigueroa López, P.
700	1		_91861 _aSolís Moya, E.
700	1		_91865 _aIreta Moreno, J.
700	1		_93413 _aHernández Muela, V.M.
700	1		_93416 _aZamora Villa, V.
700	1		_8I1705725 _9785 _aVikram, P. _gGenetic Resources Program
700	1		_93392 _aMathews, K.
700	1		_9766 _aSansaloni, C.P. _gGenetic Resources Program _8CSAC01
700	1		_8INT3332 _9922 _aSehgal, D. _gGlobal Wheat Program
700	1		_91934 _aJarquín, D.
700	1		_8INT3049 _9885 _aWenzl, P. _gGenetic Resources Program
700	1		_8INT3098 _9892 _aSukhwinder-Singh _gGenetic Resources Program
773	0		_wa58025 _x2045-2322 _dLondon : Nature Publishing Group, 2011- _tNature Scientific reports _gv. 6, no. 27312
856	4		_uhttp://hdl.handle.net/10883/18329 _yOpen Access through DSpace
942			_2ddc _cJA _n0