000			02836nab|a22003737a|4500
999			_c63067 _d63059
001			63067
003			MX-TxCIM
005			20220413201809.0
008			200910s2020||||sz |||p|op||||00||0|eng|d
022			_a1664-462X (Online)
024	8		_ahttps://doi.org/10.3389/fpls.2020.00197
040			_aMX-TxCIM
041			_aeng
100	1		_aSehgal, D. _9922 _gGlobal Wheat Program _8INT3332
245	1	0	_aIncorporating genome-wide association mapping results into genomic prediction models for grain yield and yield stability in CIMMYT spring bread wheat
260			_aSwitzerland : _bFrontiers, _c2020.
500			_aPeer review
500			_aOpen Access
520			_aUntangling the genetic architecture of grain yield (GY) and yield stability is an important determining factor to optimize genomics-assisted selection strategies in wheat. We conducted in-depth investigation on the above using a large set of advanced bread wheat lines (4,302), which were genotyped with genotyping-by-sequencing markers and phenotyped under contrasting (irrigated and stress) environments. Haplotypes-based genome-wide-association study (GWAS) identified 58 associations with GY and 15 with superiority index Pi (measure of stability). Sixteen associations with GY were “environment-specific” with two on chromosomes 3B and 6B with the large effects and 8 associations were consistent across environments and trials. For Pi, 8 associations were from chromosomes 4B and 7B, indicating ‘hot spot’ regions for stability. Epistatic interactions contributed to an additional 5–9% variation on average. We further explored whether integrating consistent and robust associations identified in GWAS as fixed effects in prediction models improves prediction accuracy. For GY, the model accounting for the haplotype-based GWAS loci as fixed effects led to up to 9–10% increase in prediction accuracy, whereas for Pi this approach did not provide any advantage. This is the first report of integrating genetic architecture of GY and yield stability into prediction models in wheat.
526			_aWC _cFP3
546			_aText in English
650		7	_2AGROVOC _91296 _aTriticum aestivum
650		7	_aGenomes _gAGROVOC _2 _91131
650		7	_2AGROVOC _910737 _aMarker-assisted selection
650		7	_2AGROVOC _91132 _aGenomics
700	1		_94557 _aRosyara, U. _8I1707470 _gGlobal Wheat Program
700	1		_aMondal, S. _gFormerly Global Wheat Program _8INT3211 _9904
700	1		_aSingh, R.P. _gGlobal Wheat Program _8INT0610 _9825
700	1		_92092 _aPoland, J.A.
700	1		_9851 _aDreisigacker, S. _8INT2692 _gGlobal Wheat Program
773	0		_tFrontiers in Plant Science _gv. 11, art. 197 _dSwitzerland : Frontiers, 2020. _x1664-462X _wu56875
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/21102
942			_cJA _n0 _2ddc