| 000 | 02828nab|a22004697a|4500 | ||
|---|---|---|---|
| 001 | 68771 | ||
| 003 | MX-TxCIM | ||
| 005 | 20251217110912.0 | ||
| 008 | 20254s2025|||||sz ||p|op||||00||0|eng|dd | ||
| 022 | _a1422-0067 (Online) | ||
| 024 | 8 | _ahttps://doi.org/10.3390/ijms26083620 | |
| 040 | _aMX-TxCIM | ||
| 041 | _aeng | ||
| 100 | 1 |
_aMontesinos-Lopez, O.A. _gGenetic Resources Program _8I1706800 _92700 |
|
| 245 | 1 | 0 | _aGBLUP outperforms quantile mapping and outlier detection for enhanced genomic prediction |
| 260 |
_aSwitzerland : _bMDPI, _c2025. |
||
| 500 | _aPeer review | ||
| 500 | _aOpen Access | ||
| 520 | _aGenomic selection (GS) accelerates plant breeding by predicting complex traits using genomic data. This study compares genomic best linear unbiased prediction (GBLUP), quantile mapping (QM)-an adjustment to GBLUP predictions-and four outlier detection methods. Using 14 real datasets, predictive accuracy was evaluated with Pearson's correlation (COR) and normalized root mean square error (NRMSE). GBLUP consistently outperformed all other methods, achieving an average COR of 0.65 and an NRMSE reduction of up to 10% compared to alternative approaches. The proportion of detected outliers was low (<7%), and their removal had minimal impact on GBLUP's predictive performance. QM provided slight improvements in datasets with skewed distributions but showed no significant advantage in well-distributed data. These findings confirm GBLUP's robustness and reliability, suggesting limited utility for QM when data deviations are minimal. | ||
| 546 | _aText in English | ||
| 591 | _aMontesinos-Lopez, O.A. : No CIMMYT Affiliation | ||
| 597 |
_dSLU Grogrund _fBreeding for Tomorrow _uhttps://hdl.handle.net/10568/175487 |
||
| 650 | 7 |
_aMarker-assisted selection _2AGROVOC _910737 |
|
| 650 | 7 |
_aPlant breeding _2AGROVOC _91203 |
|
| 650 | 7 |
_aForecasting _2AGROVOC _92701 |
|
| 650 | 7 |
_aWheat _2AGROVOC _91310 |
|
| 700 | 1 |
_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959 |
|
| 700 | 1 |
_8001713327 _aVitale, P. _gGlobal Wheat Program _gGenetic Resources Program _931497 |
|
| 700 | 1 |
_aGerard, G.S. _81713398 _gGlobal Wheat Program _911490 |
|
| 700 | 1 |
_aCrespo-Herrera, L.A. _gGlobal Wheat Program _8I1706538 _92608 |
|
| 700 | 1 |
_aDreisigacker, S. _gGlobal Wheat Program _8INT2692 _9851 |
|
| 700 | 1 |
_aSaint Pierre, C. _gGlobal Wheat Program _8INT2731 _9855 |
|
| 700 | 1 |
_aPosadas, L.G. _938790 |
|
| 700 | 1 |
_aAgbona, A. _916134 |
|
| 700 | 1 |
_aBuenrostro-Mariscal, R. _922062 |
|
| 700 | 1 |
_aMontesinos-Lopez, A. _92702 |
|
| 700 | 1 |
_aChawade, A. _97735 |
|
| 773 | 0 |
_tInternational Journal of Molecular Sciences _gv. 26, no. 8, art. 3620 _dSwitzerland : MDPI, 2025. _x1661-6596 _w57216 |
|
| 856 | 4 |
_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/35640 |
|
| 942 |
_cJA _n0 _2ddc |
||
| 999 |
_c68771 _d68763 |
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