000			02925nab a22003617a 4500
999			_c58201 _d58193
001			58201
003			MX-TxCIM
005			20240919020949.0
008			151020s2016 xxu|||p|op||| 00| 0 eng d
024	8		_ahttps://doi.org/10.2135/cropsci2015.11.0718
040			_aMX-TxCIM
041			_aeng
100	1		_91932 _aCeron Rojas, J.J.
245	1	0	_aA predetermined proportional gains eigen selection index method _h[Electronic Resource]
260			_aUSA : _bCSSA, _c2016.
500			_aPeer review
500			_aOpen Access
520			_aThe most general linear phenotypic selection index (PSI) is the predetermined proportional gains phenotypic selection index (PPG-PSI) that allows imposing restrictions on the trait expected genetic gain values to make some traits change their mean values based on a predetermined level, while the rest of the traits remain without restrictions. However, due to the increasing number of restricted traits: (i) PPG-PSI accuracy decreases; (ii) the proportional constant associated with this index can be negative, in which case, its results have no meaning in practice; and (iii) the PPG-PSI can shift the population means in the opposite direction to the predetermined desired direction. Based on the eigen selection index method (ESIM), we propose a PPG-ESIM that does not require a proportional constant, and due to the properties associated with eigen analysis, it is possible to use the theory of similar matrices to change the direction of the eigenvector values without affecting PPG-ESIM accuracy, which helps to eliminate the problem indicated in the third point above, associated with the standard PPG-PSI. The PPG-ESIM uses the first eigenvector as its vector of coefficients, and the first eigenvalue in the selection response. Two simulated and one real data set, each with four traits, were used to validate PPG-ESIM efficiency vs. PPG-PSI efficiency; the simulated and real results indicated that PPG-ESIM efficiency was higher than PPG-PSI efficiency. We concluded that PPG-ESIM is an efficient selection index that can be used in any selection program as a good alternative to PPG-PSI.
526			_aWC _cFP3
546			_aText in English
591			_bCIMMYT Informa: 1988 (April 6, 2017)
650		7	_93634 _aPhenotypes _2AGROVOC
650		7	_aWheat _gAGROVOC _2 _91310
650		7	_96025 _aLinear models _2AGROVOC
650		7	_92091 _aGenetic gain _2AGROVOC
650		7	_98831 _aGenetic engineering _gAGROVOC
700	1		_91999 _aToledo, F.H. _8I1706676 _gGenetic Resources Program
700	1		_94103 _aSahagún-Castellanos, J.
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
773	0		_wu444244 _aCrop Science Society of America _x0011-183X _dMadison, WI (USA) : Crop Science Society of America - CSSA _tCrop Science _gv. 56, no. 5, p. 2436-2447
856	4		_uhttp://hdl.handle.net/10883/18869 _yOpen Access through DSpace
942			_2ddc _cJA _n0