A graph model for genomic prediction in the context of a linear mixed model framework
Genomic selection is revolutionizing both plant and animal breeding, with its practical application depending critically on high prediction accuracy. In this study, we aimed to enhance prediction accuracy by exploring the use of graph models within a linear mixed model framework. Our investigation r...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Wiley
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/159671 |
| _version_ | 1855538482790793216 |
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| author | Montesinos-Lopez, Osval A. Huerta Prado, Gloria Isabel Montesinos-Lopez, José Cricelio Montesinos-Lopez, Abelardo Crossa, José |
| author_browse | Crossa, José Huerta Prado, Gloria Isabel Montesinos-Lopez, Abelardo Montesinos-Lopez, José Cricelio Montesinos-Lopez, Osval A. |
| author_facet | Montesinos-Lopez, Osval A. Huerta Prado, Gloria Isabel Montesinos-Lopez, José Cricelio Montesinos-Lopez, Abelardo Crossa, José |
| author_sort | Montesinos-Lopez, Osval A. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Genomic selection is revolutionizing both plant and animal breeding, with its practical application depending critically on high prediction accuracy. In this study, we aimed to enhance prediction accuracy by exploring the use of graph models within a linear mixed model framework. Our investigation revealed that incorporating the graph constructed with line connections alone resulted in decreased prediction accuracy compared to conventional methods that consider only genotype effects. However, integrating both genotype effects and the graph structure led to slightly improved results over considering genotype effects alone. These findings were validated across 14 datasets commonly used in plant breeding research. |
| format | Journal Article |
| id | CGSpace159671 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Wiley |
| publisherStr | Wiley |
| record_format | dspace |
| spelling | CGSpace1596712025-12-08T09:54:28Z A graph model for genomic prediction in the context of a linear mixed model framework Montesinos-Lopez, Osval A. Huerta Prado, Gloria Isabel Montesinos-Lopez, José Cricelio Montesinos-Lopez, Abelardo Crossa, José linear models marker-assisted selection genotypes plant breeding Genomic selection is revolutionizing both plant and animal breeding, with its practical application depending critically on high prediction accuracy. In this study, we aimed to enhance prediction accuracy by exploring the use of graph models within a linear mixed model framework. Our investigation revealed that incorporating the graph constructed with line connections alone resulted in decreased prediction accuracy compared to conventional methods that consider only genotype effects. However, integrating both genotype effects and the graph structure led to slightly improved results over considering genotype effects alone. These findings were validated across 14 datasets commonly used in plant breeding research. 2024-12 2024-11-13T16:00:02Z 2024-11-13T16:00:02Z Journal Article https://hdl.handle.net/10568/159671 en Open Access application/pdf Wiley Montesinos‐López, O. A., Prado, G. I. H., Montesinos‐López, J. C., Montesinos‐López, A., & Crossa, J. (2024). A graph model for genomic prediction in the context of a linear mixed model framework. The Plant Genome, e20522. https://doi.org/10.1002/tpg2.20522 |
| spellingShingle | linear models marker-assisted selection genotypes plant breeding Montesinos-Lopez, Osval A. Huerta Prado, Gloria Isabel Montesinos-Lopez, José Cricelio Montesinos-Lopez, Abelardo Crossa, José A graph model for genomic prediction in the context of a linear mixed model framework |
| title | A graph model for genomic prediction in the context of a linear mixed model framework |
| title_full | A graph model for genomic prediction in the context of a linear mixed model framework |
| title_fullStr | A graph model for genomic prediction in the context of a linear mixed model framework |
| title_full_unstemmed | A graph model for genomic prediction in the context of a linear mixed model framework |
| title_short | A graph model for genomic prediction in the context of a linear mixed model framework |
| title_sort | graph model for genomic prediction in the context of a linear mixed model framework |
| topic | linear models marker-assisted selection genotypes plant breeding |
| url | https://hdl.handle.net/10568/159671 |
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