| Title: | Whole-exome sequencing in UK Biobank reveals rare genetic architecture for depression |
| Journal: | Nature Communications |
| Published: | 26 Feb 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38409228/ |
| DOI: | https://doi.org/10.1038/s41467-024-45774-2 |
| URL: | https://escholarship.org/uc/item/5jx3j9q3 |
| Citations: | 3 (3 in last 2 years) as of 8 Aug 2024 |
Publication 8652
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Abstract
Nearly two hundred common-variant depression risk loci have been identified by genome-wide association studies (GWAS). However, the impact of rare coding variants on depression remains poorly understood. Here, we present whole-exome sequencing analyses of depression with seven different definitions based on survey, questionnaire, and electronic health records in 320,356 UK Biobank participants. We showed that the burden of rare damaging coding variants in loss-of-function intolerant genes is significantly associated with risk of depression with various definitions. We compared the rare and common genetic architecture across depression definitions by genetic correlation and showed different genetic relationships between definitions across common and rare variants. In addition, we demonstrated that the effects of rare damaging coding variant burden and polygenic risk score on depression risk are additive. The gene set burden analyses revealed overlapping rare genetic variant components with developmental disorder, autism, and schizophrenia. Our study provides insights into the contribution of rare coding variants, separately and in conjunction with common variants, on depression with various definitions and their genetic relationships with neurodevelopmental disorders.</p>
7 Keywords
- Biological Specimen Banks
- Depression
- Exome Sequencing
- Genetic Predisposition to Disease
- Genome-Wide Association Study
- Humans
- UK Biobank
16 Authors
- Ruoyu Tian
- Tian Ge
- Hyeokmoon Kweon
- Daniel B. Rocha
- Max Lam
- Jimmy Z. Liu
- Kritika Singh
- Daniel F. Levey
- Joel Gelernter
- Murray B. Stein
- Ellen A. Tsai
- Hailiang Huang
- Christopher F. Chabris
- Todd Lencz
- Heiko Runz
- Chia-Yen Chen
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