000			02932nab a22003137a 4500
999			_c58571 _d58563
001			58571
003			MX-TxCIM
005			20240919020949.0
008			151020s2017 xxu|||p|op||| 00| 0 eng d
024	8		_ahttps://doi.org/10.2135/cropsci2016.06.0558
040			_aMX-TxCIM
041			_aeng
100	1		_aSukumaran, S. _gFormerly Global Wheat Program _8INT3330 _9920
245	1	0	_aPedigree-based prediction models with genotype × environment interaction in multi-environment trials of CIMMYT wheat
260			_aUSA : _bCSSA, _c2017.
500			_aPeer review
500			_aOpen Access
520			_aGenotype x environment (G x E) interaction can be studied through multienvironment trials used to select wheat (Triticum aestivum L.) lines. We used spring wheat yield data from 136 international environments to evaluate the predictive ability (PA) of different models in diverse environments by modeling G x E using the pedigree-derived additive relationship matrix (A matrix). These analyses focused on 109 wheat lines from three Wheat Yield Collaboration Yield Trials (WYCYTs) and 168 lines from four Stress Adapted Trait Yield Nurseries (SATYNs) developed by CIMMYT for yield potential conditions and stress conditions, respectively. The main objectives of this study were to use various pedigree-based reaction norm models to predict sites included in each of the three WYCYT nurseries and each of the four SATYN nurseries (individual population) and to predict environments (site-year combinations) when combining the three WYCYT and four SATYN trials (combined population). Results of the PA for the individual- and combined-population analyses indicated that best predictive Model 6 (E + L + A + AE + e) always included the G X E denoted as the interaction between the A matrix and environments. The most predictable sites in WYCYTs were Iran DZ (Dezful) and Pak I (Islamabad), whereas the most predictable sites in SATYNs were India I (Indore), Iran DZ, and Mex CM (Cd. Obregon). Heritability was correlated with PA for individual-population prediction analyses, but not for combined-population prediction analyses. Our results indicate pedigree-based reaction norm models with G X E can be useful for predicting the performance of lines and selecting good predictable key sites (or environments) to reduce phenotyping costs.
610		7	_9978 _aCentro Internacional de Mejoramiento de Maiz y Trigo (CIMMYT)
650		7	_94674 _aPedigree livestock _2AGROVOC
650		7	_91134 _aGenotypes _2AGROVOC
650		7	_aWheat _gAGROVOC _2 _91310
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_91934 _aJarquín, D.
700	1		_aReynolds, M.P. _gGlobal Wheat Program _8INT1511 _9831
773	0		_wu444244 _x0011-183X _dMadison, WI (USA) : CSSA, 2017. _tCrop Science _gv. 57, no. 4, p. 1865-1880
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/18619
942			_2ddc _cJA _n0