000			03264nab|a22004457a|4500
001			67478
003			MX-TxCIM
005			20240919020956.0
008			240515s2024 xxu||||| |||| 00| 0 eng d
022			_a1674-2052
022			_a1752-9867 (Online)
024	8		_ahttps://doi.org/10.1016/j.molp.2024.03.007
040			_aMX-TxCIM
041			_aeng
100	1		_aAlemu, A. _911491
245	1	0	_aGenomic selection in plant breeding : _bkey factors shaping two decades of progress
260			_aUSA : _bCell Press, _c2024.
500			_aPeer review
500			_aOpen Access
520			_aGenomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP—theoretically reaching one when using the Pearson's correlation as a metric—is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.
546			_aText in English
650		7	_aMarker-assisted selection _2AGROVOC _910737
650		7	_aGenetic gain _2AGROVOC _92091
650		7	_aAlgorithms _2AGROVOC _932603
700	1		_aÅstrand, J. _933984
700	1		_aMontesinos-Lopez, O.A. _8I1706800 _92700 _gGenetic Resources Program
700	1		_aIsidro y Sánchez, J. _933985
700	1		_aFernández-Gónzalez, J. _933986
700	1		_aTadesse, W. _91989
700	1		_aVetukuri, R.R. _930706
700	1		_aCarlsson, A.S. _933987
700	1		_aCeplitis, A. _930681
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_aOrtiz, R. _95322
700	1		_aChawade, A. _97735
773	0		_tMolecular Plant _gv. 17, no. 4, p. 552-578 _dUSA : Cell Press, 2024. _x1674-2052
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/23156
942			_cJA _n0 _2ddc
999			_c67478 _d67470