Depression pathophysiology, risk prediction of recurrence and comorbid psychiatric disorders using genome-wide analyses.
Journal
Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015
Informations de publication
Date de publication:
07 2023
07 2023
Historique:
received:
30
06
2022
accepted:
17
04
2023
medline:
21
7
2023
pubmed:
19
7
2023
entrez:
18
7
2023
Statut:
ppublish
Résumé
Depression is a common psychiatric disorder and a leading cause of disability worldwide. Here we conducted a genome-wide association study meta-analysis of six datasets, including >1.3 million individuals (371,184 with depression) and identified 243 risk loci. Overall, 64 loci were new, including genes encoding glutamate and GABA receptors, which are targets for antidepressant drugs. Intersection with functional genomics data prioritized likely causal genes and revealed new enrichment of prenatal GABAergic neurons, astrocytes and oligodendrocyte lineages. We found depression to be highly polygenic, with ~11,700 variants explaining 90% of the single-nucleotide polymorphism heritability, estimating that >95% of risk variants for other psychiatric disorders (anxiety, schizophrenia, bipolar disorder and attention deficit hyperactivity disorder) were influencing depression risk when both concordant and discordant variants were considered, and nearly all depression risk variants influenced educational attainment. Additionally, depression genetic risk was associated with impaired complex cognition domains. We dissected the genetic and clinical heterogeneity, revealing distinct polygenic architectures across subgroups of depression and demonstrating significantly increased absolute risks for recurrence and psychiatric comorbidity among cases of depression with the highest polygenic burden, with considerable sex differences. The risks were up to 5- and 32-fold higher than cases with the lowest polygenic burden and the background population, respectively. These results deepen the understanding of the biology underlying depression, its disease progression and inform precision medicine approaches to treatment.
Identifiants
pubmed: 37464041
doi: 10.1038/s41591-023-02352-1
pii: 10.1038/s41591-023-02352-1
doi:
Banques de données
figshare
['10.6084/m9.figshare.22139849']
Types de publication
Meta-Analysis
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1832-1844Subventions
Organisme : NIMH NIH HHS
ID : U01 MH109514
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH124851
Pays : United States
Organisme : NIMH NIH HHS
ID : K08 MH122911
Pays : United States
Organisme : NIMH NIH HHS
ID : T32 MH087004
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK125246
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG067025
Pays : United States
Organisme : NIAID NIH HHS
ID : R01 AI116442
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK109677
Pays : United States
Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.
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