000			03251nab|a22003857a|4500
001			65754
003			MX-TxCIM
005			20240919020954.0
008			20229s2022||||mx |||p|op||||00||0|eng|d
022			_a1664-8021 (Online)
024	8		_ahttps://doi.org/10.3389/fgene.2022.966775
040			_aMX-TxCIM
041			_aeng
100	1		_aMontesinos-Lopez, O.A. _8I1706800 _92700 _gGenetic Resources Program
245	1	0	_aMulti-trait genome prediction of new environments with partial least squares
260			_bFrontiers, _c2022. _aSwitzerland :
500			_aPeer review
500			_aOpen Access
520			_aThe genomic selection (GS) methodology proposed over 20 years ago by Meuwissen et al. (Genetics, 2001) has revolutionized plant breeding. A predictive methodology that trains statistical machine learning algorithms with phenotypic and genotypic data of a reference population and makes predictions for genotyped candidate lines, GS saves significant resources in the selection of candidate individuals. However, its practical implementation is still challenging when the plant breeder is interested in the prediction of future seasons or new locations and/or environments, which is called the “leave one environment out” issue. Furthermore, because the distributions of the training and testing set do not match, most statistical machine learning methods struggle to produce moderate or reasonable prediction accuracies. For this reason, the main objective of this study was to explore the use of the multi-trait partial least square (MT-PLS) regression methodology for this specific task, benchmarking its performance with the Bayesian Multi-trait Genomic Best Linear Unbiased Predictor (MT-GBLUP) method. The benchmarking process was performed with five actual data sets. We found that in all data sets the MT-PLS method outperformed the popular MT-GBLUP method by 349.8% (under predictor E + G), 484.4% (under predictor E + G + GE; where E denotes environments, G genotypes and GE the genotype by environment interaction) and 15.9% (under predictor G + GE) across traits. Our results provide empirical evidence of the power of the MT-PLS methodology for the prediction of future seasons or new environments. Furthermore, the comparison between single univariate-trait (UT) versus MT for GBLUP and PLS gave an increase in prediction accuracy of MT-GBLUP versus UT-GBLUP, but not for MT-PLS versus UT-PLS.
546			_aText in English
591			_aMontesinos-Lopez, O.A. : No CIMMYT Affiliation
650		7	_aGenotypes _2AGROVOC _91134
650		7	_aGenotype environment interaction _2AGROVOC _91133
650		7	_aMachine learning _2AGROVOC _911127
650		7	_aForecasting _2AGROVOC _92701
650		7	_aMarker-assisted selection _2AGROVOC _910737
700	1		_aMontesinos-Lopez, A. _92702
700	1		_aBernal Sandoval, D.A. _929339
700	1		_aMosqueda-Gonzalez, B.A. _919441
700	1		_aValenzo-Jiménez, M.A. _929340
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
773	0		_tFrontiers in Genetics _gv. 13, art. 966775 _dSwitzerland : Frontiers, 2022. _w58093 _x1664-8021
856			_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/22290
942			_cJA _n0 _2ddc
999			_c65754 _d65746