Sparse kernel models provide optimization of training set design for genomic prediction in multiyear wheat breeding data
The success of genomic selection (GS) in breeding schemes relies on its ability to provide accurate predictions of unobserved lines at early stages. Multigeneration data provides opportunities to increase the training data size and thus, the likelihood of extracting useful information from ancestors...
| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Wiley
2022
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/126293 |
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