A graph model for genomic prediction in the context of a linear mixed model framework.
Journal
The plant genome
ISSN: 1940-3372
Titre abrégé: Plant Genome
Pays: United States
ID NLM: 101273919
Informations de publication
Date de publication:
07 Oct 2024
07 Oct 2024
Historique:
revised:
22
08
2024
received:
10
05
2024
accepted:
11
09
2024
medline:
7
10
2024
pubmed:
7
10
2024
entrez:
7
10
2024
Statut:
aheadofprint
Résumé
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.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e20522Subventions
Organisme : Bill & Melinda Gates Foundation
ID : INV-003439
Pays : United States
Informations de copyright
© 2024 International maize and Wheat Improvement Center(CIMMYT). The Plant Genome published by Wiley Periodicals LLC on behalf of Crop Science Society of America.
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