000			03122nab|a22003737a|4500
001			64322
003			MX-TxCIM
005			20240919020953.0
008			202101s2021||||xxk|||p|op||||00||0|eng|d
022			_a0018-067X
022			_a1365-2540 (Online)
024	8		_ahttps://doi.org/10.1038/s41437-021-00474-1
040			_aMX-TxCIM
041			_aeng
100	1		_aLopez-Cruz, M. _92348
245	1	0	_aMulti-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices
260			_aUnited Kingdom : _bSpringer Nature, _c2021.
500			_aPeer review
500			_aOpen Access
520			_aGenomic prediction models are often calibrated using multi-generation data. Over time, as data accumulates, training data sets become increasingly heterogeneous. Differences in allele frequency and linkage disequilibrium patterns between the training and prediction genotypes may limit prediction accuracy. This leads to the question of whether all available data or a subset of it should be used to calibrate genomic prediction models. Previous research on training set optimization has focused on identifying a subset of the available data that is optimal for a given prediction set. However, this approach does not contemplate the possibility that different training sets may be optimal for different prediction genotypes. To address this problem, we recently introduced a sparse selection index (SSI) that identifies an optimal training set for each individual in a prediction set. Using additive genomic relationships, the SSI can provide increased accuracy relative to genomic-BLUP (GBLUP). Non-parametric genomic models using Gaussian kernels (KBLUP) have, in some cases, yielded higher prediction accuracies than standard additive models. Therefore, here we studied whether combining SSIs and kernel methods could further improve prediction accuracy when training genomic models using multi-generation data. Using four years of doubled haploid maize data from the International Maize and Wheat Improvement Center (CIMMYT), we found that when predicting grain yield the KBLUP outperformed the GBLUP, and that using SSI with additive relationships (GSSI) lead to 5–17% increases in accuracy, relative to the GBLUP. However, differences in prediction accuracy between the KBLUP and the kernel-based SSI were smaller and not always significant.
546			_aText in English
591			_aDe los Campos, G. : No CIMMYT Affiliation
650		7	_2AGROVOC _91132 _aGenomics
650		7	_2AGROVOC _94859 _aModels
650		7	_2AGROVOC _91130 _aGenetics
700	1		_aBeyene, Y. _9870 _8INT2891 _gGlobal Maize Program
700	1		_aGowda, M. _9795 _8I1705963 _gGlobal Maize Program
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_aPerez-Rodriguez, P. _92703
700	1		_aDe los Campos, G. _92349 _8CCAG01 _gGenetic Resources Program
773	0		_tHeredity _dUnited Kingdom : Springer Nature, 2021. _x0018-067X _gv. 127, no. 5, p. 423-432
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/21694
942			_cJA _n0 _2ddc
999			_c64322 _d64314