000			03261nab|a22004697a|4500
001			65424
003			MX-TxCIM
005			20240919020954.0
008			20221s2022||||mx |||p|op||||00||0|eng|d
022			_a0040-5752
022			_a1432-2242 (Online)
024	8		_ahttps://doi.org/10.1007/s00122-022-04147-3
040			_aMX-TxCIM
041			_aeng
100	1		_aFentaye Kassa Semagn _8INT2869 _9869 _gGlobal Maize Program
245	1		_aComparison of single-trait and multi-trait genomic predictions on agronomic and disease resistance traits in spring wheat
260			_bSpringer, _c2022. _aBerlin (Germany) :
500			_aPeer review
520			_aThe predictive ability of multi-trait and single-trait prediction models has not been investigated on diverse traits evaluated under organic and conventional management systems. Here, we compared the predictive abilities of 25% of a testing set that has not been evaluated for a single trait (ST), not evaluated for multi-traits (MT1), and evaluated for some traits but not others (MT2) in three spring wheat populations genotyped either with the wheat 90K single nucleotide polymorphisms array or DArTseq. Analyses were performed on seven agronomic traits evaluated under conventional and organic management systems, four to seven disease resistance traits, and all agronomic and disease resistance traits simultaneously. The average prediction accuracies of the ST, MT1, and MT2 models varied from 0.03 to 0.78 (mean 0.41), from 0.05 to 0.82 (mean 0.47), and from 0.05 to 0.92 (mean 0.67), respectively. The predictive ability of the MT2 model was significantly greater than the ST model in all traits and populations except common bunt with the MT1 model being intermediate between them. The MT2 model increased prediction accuracies over the ST and MT1 models in all traits by 9.0–82.4% (mean 37.3%) and 2.9–82.5% (mean 25.7%), respectively, except common bunt that showed up to 7.7% smaller accuracies in two populations. A joint analysis of all agronomic and disease resistance traits further improved accuracies within the MT1 and MT2 models on average by 21.4% and 17.4%, respectively, as compared to either the agronomic or disease resistance traits, demonstrating the high potential of the multi-traits models in improving prediction accuracies.
546			_aText in English
650		7	_aAgronomy _2AGROVOC _96289
650		7	_aForecasting _2AGROVOC _92701
650		7	_aDisease resistance _2AGROVOC _91077
650		7	_aModelling _2AGROVOC _911710
650		7	_aSpring wheat _2AGROVOC _91806
650		7	_aGenes _2AGROVOC _93563
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_aCuevas, J. _94437
700	1		_aIqbal, M. _911300
700	1		_aCiechanowska, I. _927368
700	1		_aHenríquez, M.A. _921986
700	1		_aRandhawa, H. S. _95865
700	1		_aBeres, B.L. _98802
700	1		_aAboukhaddour, R. _911627
700	1		_aMcCallum, B.D. _911628
700	1		_aBrûlé-Babel, A.L. _927370
700	1		_aN’Diaye, A. _93087
700	1		_aPozniak, C.J. _93065
700	1		_aSpaner, D. _917724
773	0		_tTheoretical and Applied Genetics _dBerlin (Germany) : Springer, 2022 _x0040-5752 _gv 135, p. 2747–2767 _wG444762
942			_cJA _n0 _2ddc
999			_c65424 _d65416