000			05337nab a22005537a 4500
999			_c30447 _d30447
001			G98583
003			MX-TxCIM
005			20240919020947.0
008			121211b |||p||p||||||| |z||| |
022			_a1365-2540 (Revista en electrónico)
022	0		_a0018-067X
024	8		_ahttps://doi.org/10.1038/hdy.2013.144
040			_aMX-TxCIM
041	0		_aEn
090			_aCIS-7464
100	1		_aOrnella, L.
245	1	0	_aGenomic-enabled prediction with classification algorithms
260			_c2014
500			_aPeer-review: Yes - Open Access: Yes |http://science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&ISSN=0018-067X
500			_aPeer review
500			_aOpen Access
520			_aPearson?s correlation coefficient (ρ) is the most commonly reported metric of the success of prediction in genomic selection (GS). However, in real breeding ρ may not be very useful for assessing the quality of the regression in the tails of the distribution, where individuals are chosen for selection. This research used 14 maize and 16 wheat data sets with different trait?environment combinations. Six different models were evaluated by means of a cross-validation scheme (50 random partitions each, with 90% of the individuals in the training set and 10% in the testing set). The predictive accuracy of these algorithms for selecting individuals belonging to the best α=10, 15, 20, 25, 30, 35, 40% of the distribution was estimated using Cohen?s kappa coefficient (κ) and an ad hoc measure, which we call relative efficiency (RE), which indicates the expected genetic gain due to selection when individuals are selected based on GS exclusively. We put special emphasis on the analysis for α=15%, because it is a percentile commonly used in plant breeding programmes (for example, at CIMMYT). We also used ρ as a criterion for overall success. The algorithms used were: Bayesian LASSO (BL), Ridge Regression (RR), Reproducing Kernel Hilbert Spaces (RHKS), Random Forest Regression (RFR), and Support Vector Regression (SVR) with linear (lin) and Gaussian kernels (rbf). The performance of regression methods for selecting the best individuals was compared with that of three supervised classification algorithms: Random Forest Classification (RFC) and Support Vector Classification (SVC) with linear (lin) and Gaussian (rbf) kernels. Classification methods were evaluated using the same cross-validation scheme but with the response vector of the original training sets dichotomised using a given threshold. For α=15%, SVC-lin presented the highest κ coefficients in 13 of the 14 maize data sets, with best values ranging from 0.131 to 0.722 (statistically significant in 9 data sets) and the best RE in the same 13 data sets, with values ranging from 0.393 to 0.948 (statistically significant in 12 data sets). RR produced the best mean for both κ and RE in one data set (0.148 and 0.381, respectively). Regarding the wheat data sets, SVC-lin presented the best κ in 12 of the 16 data sets, with outcomes ranging from 0.280 to 0.580 (statistically significant in 4 data sets) and the best RE in 9 data sets ranging from 0.484 to 0.821 (statistically significant in 5 data sets). SVC-rbf (0.235), RR (0.265) and RHKS (0.422) gave the best κ in one data set each, while RHKS and BL tied for the last one (0.234). Finally, BL presented the best RE in two data sets (0.738 and 0.750), RFR (0.636) and SVC-rbf (0.617) in one and RHKS in the remaining three (0.502, 0.458 and 0.586). The difference between the performance of SVC-lin and that of the rest of the models was not so pronounced at higher percentiles of the distribution. The behaviour of regression and classification algorithms varied markedly when selection was done at different thresholds, that is, κ and RE for each algorithm depended strongly on the selection percentile. Based on the results, we propose classification method as a promising alternative for GS in plant breeding.
536			_aGlobal Maize Program|Genetic Resources Program|Global Wheat Program
546			_aEnglish
591			_aCIMMYT Informa No. 1876|Nature Publishing Group
594			_aINT3239|INT3400|INT3098|INT3035|INT2902|INT2692|INT0610|CCJL01
595			_aCSC
650	1	0	_aGenomic selection _91513
650		7	_aMaize _gAGROVOC _2 _91173
650	1	0	_asupport vector machines
650		7	_aWheat _gAGROVOC _2 _91310
650		7	_aArtificial Selection _98685 _2AGROVOC
650		7	_aStatistical methods _92624 _2AGROVOC
700	1		_aGonzlez-Camacho, J.M., _ecoaut.
700	1		_aLong, N., _ecoaut. _9576
700	1		_aPerez, P., _ecoaut.
700	1		_aTapia, E., _ecoaut.
700	1		_aVicente, F.S., _ecoaut.
700	1		_9892 _aSukhwinder-Singh _gGenetic Resources Program _8INT3098 _ecoaut.
700	1		_9907 _aBurgueño, J. _gGenetic Resources Program _8INT3239
700	0		_aXuecai Zhang _gGlobal Maize Program _8INT3400 _9951
700	1		_aSingh, R.P. _gGlobal Wheat Program _8INT0610 _9825
700	1		_9851 _aDreisigacker, S. _gGlobal Wheat Program _8INT2692
700	1		_9871 _aBonnett, D.G. _gGlobal Wheat Program _8INT2902 _ecoaut.
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
773	0		_tHeredity _gv. 112, p. 616-626
856	4		_uhttps://hdl.handle.net/10883/19766 _yOpen Access through DSpace
942			_cJA _2ddc _n0