| Title: | Genetic network structure of 13 psychiatric disorders in the general population |
| Journal: | European Archives of Psychiatry and Clinical Neuroscience |
| Published: | 19 Apr 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37074466/ |
| DOI: | https://doi.org/10.1007/s00406-023-01601-1 |
Publication 7868
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Abstract
Psychiatric disorders frequently co-occur and share common symptoms and genetic backgrounds. Previous research has used genome-wide association studies to identify the interrelationships among psychiatric disorders and identify clusters of disorders; however, these methods have limitations in terms of their ability to examine the relationships among disorders as a network structure and their generalizability to the general population. In this study, we explored the network structure of the polygenic risk score (PRS) for 13 psychiatric disorders in a general population (276,249 participants of European ancestry from the UK Biobank) and identified communities and the centrality of the network. In this network, the nodes represented a PRS for each psychiatric disorder and the edges represented the connections between nodes. The psychiatric disorders comprised four robust communities. The first community included attention-deficit hyperactivity disorder, autism spectrum disorder, major depressive disorder, and anxiety disorder. The second community consisted of bipolar I and II disorders, schizophrenia, and anorexia nervosa. The third group included Tourette's syndrome and obsessive-compulsive disorder. Cannabis use disorder, alcohol use disorder, and post-traumatic stress disorder make up the fourth community. The PRS of schizophrenia had the highest values for the three metrics (strength, betweenness, and closeness) in the network. Our findings provide a comprehensive genetic network of psychiatric disorders and biological evidence for the classification of psychiatric disorders.8 Authors
- Hong Kyu Ihm
- Hyejin Kim
- Jinho Kim
- Woong-Yang Park
- Hyo Shin Kang
- Jungkyu Park
- Hong-Hee Won
- Woojae Myung
1 Application
| Application ID | Title |
|---|---|
| 33002 | Development of a computational method to predict disease risk |
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