Bayesian divergence-based approach for genomic multitrait ordinal selection Article Swipe
Bartolo de Jesús Villar‐Hernández
,
P. K. Singh
,
Nérida Lozano-Ramírez
,
Paolo Vitale
,
Guillermo Gerard
,
F. Breseghello
,
Susanne Dreisigacker
,
José Crossa
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/g3journal/jkaf183
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/g3journal/jkaf183
Effective 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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/g3journal/jkaf183
- OA Status
- gold
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413905740
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413905740Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/g3journal/jkaf183Digital Object Identifier
- Title
-
Bayesian divergence-based approach for genomic multitrait ordinal selectionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-09-02Full publication date if available
- Authors
-
Bartolo de Jesús Villar‐Hernández, P. K. Singh, Nérida Lozano-Ramírez, Paolo Vitale, Guillermo Gerard, F. Breseghello, Susanne Dreisigacker, José CrossaList of authors in order
- Landing page
-
https://doi.org/10.1093/g3journal/jkaf183Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1093/g3journal/jkaf183Direct OA link when available
- Concepts
-
Trait, Heritability, Selection (genetic algorithm), Biology, Bayesian probability, Divergence (linguistics), Ordinal data, Statistics, Computer science, Machine learning, Evolutionary biology, Mathematics, Programming language, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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19Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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