A computationally feasible multi-trait single-step genomic prediction model with trait-specific marker weights.
Journal
Genetics, selection, evolution : GSE
ISSN: 1297-9686
Titre abrégé: Genet Sel Evol
Pays: France
ID NLM: 9114088
Informations de publication
Date de publication:
16 Aug 2024
16 Aug 2024
Historique:
received:
11
01
2024
accepted:
26
07
2024
medline:
17
8
2024
pubmed:
17
8
2024
entrez:
16
8
2024
Statut:
epublish
Résumé
Regions of genome-wide marker data may have differing influences on the evaluated traits. This can be reflected in the genomic models by assigning different weights to the markers, which can enhance the accuracy of genomic prediction. However, the standard multi-trait single-step genomic evaluation model can be computationally infeasible when the traits are allowed to have different marker weights. In this study, we developed and implemented a multi-trait single-step single nucleotide polymorphism best linear unbiased prediction (SNPBLUP) model for large genomic data evaluations that allows for the use of precomputed trait-specific marker weights. The modifications to the standard single-step SNPBLUP model were minor and did not significantly increase the preprocessing workload. The model was tested using simulated data and marker weights precomputed using BayesA. Based on the results, memory requirements and computing time per iteration slightly increased compared to the standard single-step model without weights. Moreover, convergence of the model was slower when using marker weights, which resulted in longer total computing time. The use of marker weights, however, improved prediction accuracy. We investigated a single-step SNPBLUP model that can be used to accommodate trait-specific marker weights. The marker-weighted single-step model improved prediction accuracy. The approach can be used for large genomic data evaluations using precomputed marker weights.
Sections du résumé
BACKGROUND
BACKGROUND
Regions of genome-wide marker data may have differing influences on the evaluated traits. This can be reflected in the genomic models by assigning different weights to the markers, which can enhance the accuracy of genomic prediction. However, the standard multi-trait single-step genomic evaluation model can be computationally infeasible when the traits are allowed to have different marker weights.
RESULTS
RESULTS
In this study, we developed and implemented a multi-trait single-step single nucleotide polymorphism best linear unbiased prediction (SNPBLUP) model for large genomic data evaluations that allows for the use of precomputed trait-specific marker weights. The modifications to the standard single-step SNPBLUP model were minor and did not significantly increase the preprocessing workload. The model was tested using simulated data and marker weights precomputed using BayesA. Based on the results, memory requirements and computing time per iteration slightly increased compared to the standard single-step model without weights. Moreover, convergence of the model was slower when using marker weights, which resulted in longer total computing time. The use of marker weights, however, improved prediction accuracy.
CONCLUSIONS
CONCLUSIONS
We investigated a single-step SNPBLUP model that can be used to accommodate trait-specific marker weights. The marker-weighted single-step model improved prediction accuracy. The approach can be used for large genomic data evaluations using precomputed marker weights.
Identifiants
pubmed: 39152403
doi: 10.1186/s12711-024-00926-2
pii: 10.1186/s12711-024-00926-2
doi:
Substances chimiques
Genetic Markers
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
58Subventions
Organisme : Norwegian Research Council
ID : 309611
Informations de copyright
© 2024. The Author(s).
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