000			03089nab a22004217a 4500
999			_c57119 _d57111
001			57119
003			MX-TxCIM
005			20240919021002.0
008			151112s2015 xxu|||p|op||| 00| 0 eng d
022			_a1940-3372
024	8		_ahttps://doi.org/10.3835/plantgenome2014.09.0046
040			_aMX-TxCIM
041			_aeng
100	1		_91933 _aRutkoski, J. _gGlobal Wheat Program _8I1706399
245	1	0	_aEfficient use of historical data for genomic selection : _b a case study of stem rust resistance in wheat
260			_aUSA : _bCSSA, _c2015.
500			_aOpen Access
500			_aPeer review
520			_aGenomic selection (GS) is a methodology that can improve crop breeding efficiency. To implement GS, a training population (TP) with phenotypic and genotypic data is required to train a statistical model used to predict genotyped selection candidates (SCs). A key factor impacting prediction accuracy is the relationship between the TP and the SCs. This study used empirical data for quantitative adult plant resistance to stem rust of wheat (Triticum aestivum L.) to investigate the utility of a historical TP (TPH) compared with a population-specific TP (TPPS), the potential for TPH optimization, and the utility of TPH data when close relative data is available for training. We found that, depending on the population size, a TPPS was 1.5 to 4.4 times more accurate than a TPH, and TPH optimization based on the mean of the generalized coefficient of determination or prediction error variance enabled the selection of subsets that led to significantly higher accuracy than randomly selected subsets. Retaining historical data when data on close relatives were available lead to a 11.9% increase in accuracy, at best, and a 12% decrease in accuracy, at worst, depending on the heritability. We conclude that historical data could be used successfully to initiate a GS program, especially if the dataset is very large and of high heritability. Training population optimization would be useful for the identification of TPH subsets to phenotype additional traits. However, after model updating, discarding historical data may be warranted. More studies are needed to determine if these observations represent general trends.
536			_aGlobal Wheat Program
546			_aText in english
591			_bCIMMYT Informa No. 1956
594			_aINT0610
594			_aINT2843
594			_aI1706399
650		7	_aRusts _gAGROVOC _2 _91251
650		7	_aWheat _gAGROVOC _2 _91310
650		7	_91132 _aGenomics _2AGROVOC
700	1		_aSingh, R.P. _gGlobal Wheat Program _8INT0610 _9825
700	1		_aHuerta-Espino, J. _gGlobal Wheat Program _8CHUE01 _9397
700	1		_9867 _aBhavani, S. _8INT2843 _gGlobal Wheat Program
700	1		_92092 _aPoland, J.A.
700	1		_92093 _aJannink, J.L.
700	1		_92094 _aSorrells, M.E.
773	0		_wu94757 _aCrop Science Society of America _x1940-3372 _dMadison, WI (USA) : CSSA, 2015. _tThe Plant Genome _gv. 8, no. 1, p. 1-10
856	4		_yOpen Access through DSpace _uhttp://hdl.handle.net/10883/16815
942			_2ddc _cJA _n0