Leveraging information between multiple population groups and traits improves fine-mapping resolution.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
10 11 2023
10 11 2023
Historique:
received:
31
05
2023
accepted:
01
11
2023
medline:
13
11
2023
pubmed:
11
11
2023
entrez:
10
11
2023
Statut:
epublish
Résumé
Statistical fine-mapping helps to pinpoint likely causal variants underlying genetic association signals. Its resolution can be improved by (i) leveraging information between traits; and (ii) exploiting differences in linkage disequilibrium structure between diverse population groups. Using association summary statistics, MGflashfm jointly fine-maps signals from multiple traits and population groups; MGfm uses an analogous framework to analyse each trait separately. We also provide a practical approach to fine-mapping with out-of-sample reference panels. In simulation studies we show that MGflashfm and MGfm are well-calibrated and that the mean proportion of causal variants with PP > 0.80 is above 0.75 (MGflashfm) and 0.70 (MGfm). In our analysis of four lipids traits across five population groups, MGflashfm gives a median 99% credible set reduction of 10.5% over MGfm. MGflashfm and MGfm only require summary level data, making them very useful fine-mapping tools in consortia efforts where individual-level data cannot be shared.
Identifiants
pubmed: 37949886
doi: 10.1038/s41467-023-43159-5
pii: 10.1038/s41467-023-43159-5
pmc: PMC10638399
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
7279Subventions
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/W029626/1
Pays : United Kingdom
Informations de copyright
© 2023. The Author(s).
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