University of Worcester Worcester Research and Publications
 
  USER PANEL:
  ABOUT THE COLLECTION:
  CONTACT DETAILS:

The genetics of the mood disorder spectrum:genome-wide association analyses of over 185,000 cases and 439,000 controls

Coleman, J., Gaspar, H., Bryois, J., Bipolar Disorder Working Group, of the Psychiatric Genomics Consortium, Major Depressive Disorder Working Group of the, Psychiatric Genomics Consortium, Breen, G., Gordon-Smith, Katherine ORCID: https://orcid.org/0000-0003-4083-1143, Jones, Lisa ORCID: https://orcid.org/0000-0002-5122-8334 and Perry, Amy ORCID: https://orcid.org/0000-0002-9381-6636 (2020) The genetics of the mood disorder spectrum:genome-wide association analyses of over 185,000 cases and 439,000 controls. Biological Psychiatry, 88 (2). pp. 169-184. ISSN Print: 0006-3223 Online: 1873-2402

[img] Text (Pre-print (not peer-reviewed))
383331.full.pdf - Submitted Version
Restricted to Repository staff only

Download (612kB) | Request a copy
[img]
Preview
Text
KGS-8892-genetics-of-mood-disorder-spectrum.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (18MB) | Preview

Abstract

Background
Mood disorders (including major depressive disorder and bipolar disorder) affect 10-20% of the population. They range from brief, mild episodes to severe, incapacitating conditions that markedly impact lives. Despite their diagnostic distinction, multiple approaches have shown considerable sharing of risk factors across the mood disorders.

Methods
To clarify their shared molecular genetic basis, and to highlight disorder-specific associations, we meta-analysed data from the latest Psychiatric Genomics Consortium (PGC) genome-wide association studies of major depression (including data from 23andMe) and bipolar disorder, and an additional major depressive disorder cohort from UK Biobank (total: 185,285 cases, 439,741 controls; non-overlapping N = 609,424).

Results
Seventy-three loci reached genome-wide significance in the meta-analysis, including 15 that are novel for mood disorders. More genome-wide significant loci from the PGC analysis of major depression than bipolar disorder reached genome-wide significance. Genetic correlations revealed that type 2 bipolar disorder correlates strongly with recurrent and single episode major depressive disorder. Systems biology analyses highlight both similarities and differences between the mood disorders, particularly in the mouse brain cell-types implicated by the expression patterns of associated genes. The mood disorders also differ in their genetic correlation with educational attainment – positive in bipolar disorder but negative in major depressive disorder.

Conclusions
The mood disorders share several genetic associations, and can be combined effectively to increase variant discovery. However, we demonstrate several differences between these disorders. Analysing subtypes of major depressive disorder and bipolar disorder provides evidence for a genetic mood disorders spectrum.

Item Type: Article
Additional Information:

Staff and students at the University of Worcester can access the full-text of the online published article via the official URL. External users should check availability with their local library or Interlibrary Requests Service.

Uncontrolled Discrete Keywords: major depressive disorder, bipolar disorder, mood disorders, affective disorders, genome-wide association study, genetic correlation
Subjects: B Philosophy. Psychology. Religion > BF Psychology
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: College of Health, Life and Environmental Sciences > School of Allied Health and Community
Related URLs:
Depositing User: Katherine Gordon-Smith
Date Deposited: 11 Dec 2019 16:37
Last Modified: 17 Dec 2021 11:36
URI: https://eprints.worc.ac.uk/id/eprint/8982

Actions (login required)

View Item View Item
 
     
Worcester Research and Publications is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.