| 000 | 03625nab|a22004457a|4500 | ||
|---|---|---|---|
| 001 | 69549 | ||
| 003 | MX-TxCIM | ||
| 005 | 20251219160656.0 | ||
| 008 | 251120s2025||||sz |||p|op||||00||0|eng|d | ||
| 022 | _a2160-1836 (Online) | ||
| 024 | 8 | _ahttps://doi.org/10.1093/g3journal/jkaf183 | |
| 040 | _aMX-TxCIM | ||
| 041 | _aeng | ||
| 100 | 1 |
_aVillar-Hernandez, B.d.J. _97952 |
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| 245 | 1 | 0 | _aBayesian divergence-based approach for genomic multitrait ordinal selection |
| 260 |
_aBethesda, MD (United States of America) : _bOxford University Press, _c2025. |
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| 500 | _aPeer review | ||
| 500 | _aOpen Access | ||
| 520 | _aEffective genomic selection for ordinal traits, such as disease resistance scores, is a persistent challenge in plant breeding due to the discrete, ordered nature of these phenotypes. This study presents a novel Bayesian divergence-based framework for multitrait ordinal selection, implemented in the extended Multitrait Parental Selection R package (MPS-R). By leveraging decision-theoretic loss functions, including the Kullback–Leibler (KL) divergence, Bhattacharyya distance, and Hellinger distance, our approach quantifies the distance between candidate distributions and breeder-defined target distributions. Through extensive simulations under 6 scenarios combining different genetic correlation structures and heritability levels, we demonstrate the comparative performance of each loss function. KL divergence consistently yielded superior genetic gains, especially in moderate heritability settings. Additionally, random sampling validation using real wheat disease resistance data confirmed the utility of these methods in practical breeding contexts. The MPS-R package implements this methodology through user-friendly functions tailored for ordinal trait selection in breeding applications. Our results demonstrate that this toolset provides a flexible, robust, and biologically grounded framework to enhance selection efficiency in breeding programs targeting complex, multitrait ordinal phenotypes. A couple of limitations employed by the simulation scheme used on the study are also discussed. | ||
| 546 | _aText in English | ||
| 591 | _aVillar-Hernandez, B.d.J. : Not in IRS staff list but CIMMYT Affiliation | ||
| 591 | _aLozano-Ramirez, N. : Not in IRS staff list but CIMMYT Affiliation | ||
| 597 |
_dBill & Melinda Gates Foundation (BMGF) _dAccelerating Genetic Gains in Maize and Wheat (AGG) _dUnited States Agency for International Development (USAID) _dCentro Internacional de Mejoramiento de Maíz y Trigo (CIMMYT) _fBreeding for Tomorrow _uhttps://hdl.handle.net/10568/179091 |
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| 650 | 7 |
_aBayesian theory _2AGROVOC _94013 |
|
| 650 | 7 |
_aMarker-assisted selection _2AGROVOC _910737 |
|
| 650 | 7 |
_aWheat _2AGROVOC _91310 |
|
| 650 | 7 |
_aBreeding _2AGROVOC _91029 |
|
| 650 | 7 |
_aGenomics _2AGROVOC _91132 |
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| 650 | 7 |
_aForecasting _2AGROVOC _92701 |
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| 700 | 1 |
_aPawan Kumar Singh _gGlobal Wheat Program _8INT2868 _9868 |
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| 700 | 1 |
_aLozano-Ramirez, N. _917230 |
|
| 700 | 1 |
_8001713327 _aVitale, P. _gGenetic Resources Program _931497 |
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| 700 | 1 |
_aGerard, G.S. _81713398 _gGlobal Wheat Program _911490 |
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| 700 | 1 |
_aBreseghello, F. _8001715727 _gGlobal Wheat Program _920853 |
|
| 700 | 1 |
_aDreisigacker, S. _gGlobal Wheat Program _8INT2692 _9851 |
|
| 700 | 1 |
_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959 |
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| 773 | 0 |
_tG3: Genes, Genomes, Genetics _gv. 15, no. 10, p. jkaf183 _dBethesda, MD (United States of America) : Oxford University Press, 2025. _x2160-1836 _w56922 |
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| 856 | 4 |
_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/36139 |
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| 942 |
_cJA _n0 _2ddc |
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| 999 |
_c69549 _d69541 |
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