000			03221nab a22004937a 4500
999			_c59663 _d59655
001			59663
003			MX-TxCIM
005			20250211020919.0
008			180814s2018||||xxk|||p|op||||00||0|eng|d
024	8		_ahttps://doi.org/10.1038/s41598-018-30027-2
040			_aMX-TxCIM
041			_aeng
100	1		_96147 _aRoorkiwal, M.
245	1	0	_aGenomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea _h[Electronic Resource]
260			_aLondon : _bNature Publishing Group, _c2018.
500			_aPeer review
500			_aOpen Access
520			_aGenomic selection (GS) by selecting lines prior to field phenotyping using genotyping data has the potential to enhance the rate of genetic gains. Genotype × environment (G × E) interaction inclusion in GS models can improve prediction accuracy hence aid in selection of lines across target environments. Phenotypic data on 320 chickpea breeding lines for eight traits for three seasons at two locations were recorded. These lines were genotyped using DArTseq (1.6 K SNPs) and Genotyping-by-Sequencing (GBS; 89 K SNPs). Thirteen models were fitted including main effects of environment and lines, markers, and/or naïve and informed interactions to estimate prediction accuracies. Three cross-validation schemes mimicking real scenarios that breeders might encounter in the fields were considered to assess prediction accuracy of the models (CV2: incomplete field trials or sparse testing; CV1: newly developed lines; and CV0: untested environments). Maximum prediction accuracies for different traits and different models were observed with CV2. DArTseq performed better than GBS and the combined genotyping set (DArTseq and GBS) regardless of the cross validation scheme with most of the main effect marker and interaction models. Improvement of GS models and application of various genotyping platforms are key factors for obtaining accurate and precise prediction accuracies, leading to more precise selection of candidates.
546			_aText in English
591			_bCIMMYT Informa : 2020 (September 27, 2018)
650		7	_91133 _aGenotype environment interaction _2AGROVOC
650		7	_2AGROVOC _92701 _aForecasting
650		7	_2AGROVOC _92846 _aChickpeas
650		7	_2AGROVOC _91132 _aGenomics
700	1		_91934 _aJarquín, D.
700	0		_aMuneendra K. Singh _97894
700	0		_97895 _aPooran M. Gaur
700	0		_aChellapilla Bharadwaj _97896
700	1		_aRathore, A. _8001712937 _gExcellence in Breeding _gGenetic Resources Program _97897
700	1		_aHoward, R. _97898
700	0		_aSamineni Srinivasan _97899
700	0		_aAnkit Jain _97900
700	0		_aVanika Garg _97901
700	0		_aSandip Kale _97902
700	0		_aAnnapurna Chitikineni _97903
700	0		_aShailesh Tripathi _97904
700	1		_aJones, E. _97905
700	1		_95987 _aRobbins, K.
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_95901 _aVarshney, R.K.
773	0		_tScientific Reports _gv. 8, art. 11701 _x2045-2322 _wa58025
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/19600
942			_cJA _n0 _2ddc