Boosting Genomic Prediction Transferability with Sparse Testing
These findings emphasize that enriching training data with relevant, temporally proximate information is key to enhancing genomic prediction performance; conversely, incorporating unrelated data can reduce prediction accuracy.
Authors
- Iván Delgado‐Enciso (ORCID: https://orcid.org/0000-0001-9848-862X)
- Carolina Saint Pierre (ORCID: https://orcid.org/0000-0003-1291-7468)
- Abelardo Montesinos‐López (ORCID: https://orcid.org/0000-0002-0525-9973)
- Leonardo Crespo‐Herrera (ORCID: https://orcid.org/0000-0003-0506-4700)
- Susanne Dreisigacker (ORCID: https://orcid.org/0000-0002-3546-5989)
- Réka Howard (ORCID: https://orcid.org/0000-0002-8837-3770)
- Guillermo Gerard (ORCID: https://orcid.org/0000-0002-9112-3588)
- Paolo Vitale (ORCID: https://orcid.org/0000-0002-4353-5828)
- Jose Crossa
- Osval A. Montesinos-López
Institutions
- University of Nebraska–Lincoln (US)
- Universidad de Guadalajara (MX)
- Centro Internacional de Mejoramiento de Maíz Y Trigo (MX)
- Colegio de Postgraduados (MX)
- Universidad de Colima (MX)
Publication Details
- Journal
- Genes
- Published
- 2025-07-16
- DOI
- https://doi.org/10.3390/genes16070827
- Primary Topic
- Genetic and phenotypic traits in livestock
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- United States Agency for International Development
- Bill and Melinda Gates Foundation
- Consortium of International Agricultural Research Centers
- Foreign, Commonwealth and Development Office