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Symptom-level modelling unravels the shared genetic architecture of anxiety and depression.
- Source :
-
Nature human behaviour [Nat Hum Behav] 2021 Oct; Vol. 5 (10), pp. 1432-1442. Date of Electronic Publication: 2021 Apr 15. - Publication Year :
- 2021
-
Abstract
- Depression and anxiety are highly prevalent and comorbid psychiatric traits that cause considerable burden worldwide. Here we use factor analysis and genomic structural equation modelling to investigate the genetic factor structure underlying 28 items assessing depression, anxiety and neuroticism, a closely related personality trait. Symptoms of depression and anxiety loaded on two distinct, although highly genetically correlated factors, and neuroticism items were partitioned between them. We used this factor structure to conduct genome-wide association analyses on latent factors of depressive symptoms (89 independent variants, 61 genomic loci) and anxiety symptoms (102 variants, 73 loci) in the UK Biobank. Of these associated variants, 72% and 78%, respectively, replicated in an independent cohort of approximately 1.9 million individuals with self-reported diagnosis of depression and anxiety. We use these results to characterize shared and trait-specific genetic associations. Our findings provide insight into the genetic architecture of depression and anxiety and comorbidity between them.<br /> (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)
- Subjects :
- Comorbidity
Factor Analysis, Statistical
Genetic Predisposition to Disease
Genome-Wide Association Study
Humans
Latent Class Analysis
Symptom Assessment methods
Symptom Assessment statistics & numerical data
Anxiety diagnosis
Anxiety epidemiology
Anxiety genetics
Behavioral Symptoms diagnosis
Behavioral Symptoms psychology
Depression diagnosis
Depression epidemiology
Depression genetics
Neuroticism physiology
Subjects
Details
- Language :
- English
- ISSN :
- 2397-3374
- Volume :
- 5
- Issue :
- 10
- Database :
- MEDLINE
- Journal :
- Nature human behaviour
- Publication Type :
- Academic Journal
- Accession number :
- 33859377
- Full Text :
- https://doi.org/10.1038/s41562-021-01094-9