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001			65042
003			MX-TxCIM
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008			202202s2022||||xxk|||p|op||||00||0|eng|d
022			_a1297-9686
024	8		_ahttps://doi.org/10.1186/s12711-022-00705-x
040			_aMX-TxCIM
041			_aeng
100	1		_aLegarra, A. _96003
245	1	0	_aComputing strategies for multi-population genomic evaluation
260			_aLondon (United Kingdom) : _bBioMed Central, _c2022.
500			_aPeer review
500			_aOpen Access
520			_aBACKGROUND: Multiple breed evaluation using genomic prediction includes the use of data from multiple populations, or from parental breeds and crosses, and is expected to lead to better genomic predictions. Increased complexity comes from the need to fit non-additive effects such as dominance and/or genotype-by-environment interactions. In these models, marker effects (and breeding values) are modelled as correlated between breeds, which leads to multiple trait formulations that are based either on markers [single nucleotide polymorphism best linear unbiased prediction (SNP-BLUP)] or on individuals [genomic(G)BLUP)]. As an alternative, we propose the use of generalized least squares (GLS) followed by backsolving of marker effects using selection index (SI) theory. RESULTS: All investigated options have advantages and inconveniences. The SNP-BLUP yields marker effects directly, which are useful for indirect prediction and for planned matings, but is very large in number of equations and is structured in dense and sparse blocks that do not allow for simple solving. GBLUP uses a multiple trait formulation and is very general, but results in many equations that are not used, which increase memory needs, and is also structured in dense and sparse blocks. An alternative formulation of GBLUP is more compact but requires tailored programming. The alternative of solving by GLS + SI is the least consuming, both in number of operations and in memory, and it uses only single dense blocks. However, it requires dedicated programming. Computational complexity problems are exacerbated when more than additive effects are fitted, e.g. dominance effects or genotype x environment interactions. CONCLUSIONS: As multi-breed predictions become more frequent and non-additive effects are more often included, standard equations for genomic prediction based on Henderson's mixed model equations become less practical and may need to be replaced by more efficient (although less general) approaches such as the GLS + SI approach proposed here.
546			_aText in English
591			_aGonzález-Diéguez, D. : Not in IRS Staff list but CIMMYT Affiliation
650		7	_aGenomics _91132 _2AGROVOC
650		7	_aEvaluation _97749 _2AGROVOC
650		7	_aBreeding _91029 _2AGROVOC
650		7	_aGenetic markers _91848 _2AGROVOC
700	1		_aGonzález-Diéguez, D.O. _81707522 _gGlobal Wheat Program _926628
700	1		_aVitezica, Z.G. _926629
773	0		_tGenetics Selection Evolution _gv. 54, art. 10 _dLondon (United Kingdom) : BioMed Central, 2022. _x1297-9686
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/21976
942			_cJA _n0 _2ddc
999			_c65042 _d65034