000			00595nab|a22002177a|4500
999			_c64145 _d64137
001			64145
003			MX-TxCIM
005			20210902155733.0
008			201209s2021||||xxk|||p|op||||00||0|eng|d
022			_a2045-2322
024	8		_ahttps://doi.org/10.1038/s41598-021-83567-5
040			_aMX-TxCIM
041			_aeng
100	1		_aAmini, F. _922615
245	1	4	_aThe look ahead trace back optimizer for genomic selection under transparent and opaque simulators
260			_aLondon (United Kingdom) : _bNature Publishing Group, _c2021.
500			_aPeer review
500			_aOpen Access
520			_aRecent advances in genomic selection (GS) have demonstrated the importance of not only the accuracy of genomic prediction but also the intelligence of selection strategies. The look ahead selection algorithm, for example, has been found to significantly outperform the widely used truncation selection approach in terms of genetic gain, thanks to its strategy of selecting breeding parents that may not necessarily be elite themselves but have the best chance of producing elite progeny in the future. This paper presents the look ahead trace back algorithm as a new variant of the look ahead approach, which introduces several improvements to further accelerate genetic gain especially under imperfect genomic prediction. Perhaps an even more significant contribution of this paper is the design of opaque simulators for evaluating the performance of GS algorithms. These simulators are partially observable, explicitly capture both additive and non-additive genetic effects, and simulate uncertain recombination events more realistically. In contrast, most existing GS simulation settings are transparent, either explicitly or implicitly allowing the GS algorithm to exploit certain critical information that may not be possible in actual breeding programs. Comprehensive computational experiments were carried out using a maize data set to compare a variety of GS algorithms under four simulators with different levels of opacity. These results reveal how differently a same GS algorithm would interact with different simulators, suggesting the need for continued research in the design of more realistic simulators. As long as GS algorithms continue to be trained in silico rather than in planta, the best way to avoid disappointing discrepancy between their simulated and actual performances may be to make the simulator as akin to the complex and opaque nature as possible.
546			_aText in English
650		7	_aMarker-assisted selection _2AGROVOC _910737
650		7	_aSimulation _2AGROVOC _98687
650		7	_aBiotechnology _2AGROVOC _95143
650		7	_aBioinformatics _2AGROVOC _98703
700	1		_aFranco, F.R. _922616
700	0		_aGuiping Hu _922617
700	0		_aLizhi Wang _922618
773	0		_gv. 11, art. 4124 _dLondon (United Kingdom) : Nature Publishing Group, 2021. _x2045-2322 _tNature Scientific Reports _wa58025
856	4		_yClick here to access online _uhttps://doi.org/10.1038/s41598-021-83567-5
942			_cJA _n0 _2ddc