Symptom-level modelling unravels the shared genetic architecture of anxiety and depression

Jackson G. Thorp (), Adrian I. Campos, Andrew D. Grotzinger, Zachary F. Gerring, Jiyuan An, Jue-Sheng Ong, Wei Wang, Suyash Shringarpure, Enda M. Byrne, Stuart MacGregor, Nicholas G. Martin, Sarah E. Medland, Christel M. Middeldorp and Eske M. Derks ()
Additional contact information
Jackson G. Thorp: QIMR Berghofer Medical Research Institute
Adrian I. Campos: University of Queensland
Andrew D. Grotzinger: University of Texas at Austin
Zachary F. Gerring: QIMR Berghofer Medical Research Institute
Jiyuan An: QIMR Berghofer Medical Research Institute
Jue-Sheng Ong: QIMR Berghofer Medical Research Institute
Wei Wang: 23andMe
Suyash Shringarpure: 23andMe
Enda M. Byrne: University of Queensland
Stuart MacGregor: QIMR Berghofer Medical Research Institute
Nicholas G. Martin: QIMR Berghofer Medical Research Institute
Sarah E. Medland: QIMR Berghofer Medical Research Institute
Christel M. Middeldorp: University of Queensland
Eske M. Derks: QIMR Berghofer Medical Research Institute

Nature Human Behaviour, 2021, vol. 5, issue 10, 1432-1442

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41562-021-01094-9

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