Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

The genomic basis of mood instability: identification of 46 loci in 363,705 UK Biobank participants, genetic correlation with psychiatric disorders, and association with gene expression and function

Molecular Psychiatry volume 25, pages 3091–3099 (2020)Cite this article

Subjects

A Correction to this article was published on 30 July 2019

This article has been updated

Abstract

Genome-wide association studies (GWAS) of psychiatric phenotypes have tended to focus on categorical diagnoses, but to understand the biology of mental illness it may be more useful to study traits which cut across traditional boundaries. Here, we report the results of a GWAS of mood instability as a trait in a large population cohort (UK Biobank, n = 363,705). We also assess the clinical and biological relevance of the findings, including whether genetic associations show enrichment for nervous system pathways. Forty six unique loci associated with mood instability were identified with a SNP heritability estimate of 9%. Linkage Disequilibrium Score Regression (LDSR) analyses identified genetic correlations with Major Depressive Disorder (MDD), Bipolar Disorder (BD), Schizophrenia, anxiety, and Post Traumatic Stress Disorder (PTSD). Gene-level and gene set analyses identified 244 significant genes and 6 enriched gene sets. Tissue expression analysis of the SNP-level data found enrichment in multiple brain regions, and eQTL analyses highlighted an inversion on chromosome 17 plus two brain-specific eQTLs. In addition, we used a Phenotype Linkage Network (PLN) analysis and community analysis to assess for enrichment of nervous system gene sets using mouse orthologue databases. The PLN analysis found enrichment in nervous system PLNs for a community containing serotonin and melatonin receptors. In summary, this work has identified novel loci, tissues and gene sets contributing to mood instability. These findings may be relevant for the identification of novel trans-diagnostic drug targets and could help to inform future stratified medicine innovations in mental health.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2

Similar content being viewed by others

Change history

References

  1. Marwaha S, He Z, Broome M, Singh SP, Scott J, Eyden J, et al. How is affective instability defined and measured? A systematic review. Psychol Med. 2014;44:1793–808.

    CAS  PubMed  Google Scholar 

  2. Cuthbert BN, Insel TR. Toward the future of psychiatric diagnosis: the seven pillars of RDoC. BMC Med. 2013;11:126.

    PubMed  PubMed Central  Google Scholar 

  3. Broome MR, Saunders KEA, Harrison PJ, Marwaha S. Mood instability: significance, definition and measurement. Br J Psychiatry. 2015;207:283–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Ward J, Strawbridge RJ, Bailey MES, Graham N, Ferguson A, Lyall DM, et al. Genome-wide analysis in UK Biobank identifies four loci associated with mood instability and genetic correlation with major depressive disorder, anxiety disorder and schizophrenia. Transl Psychiatry. 2017;7:1264.

    PubMed  PubMed Central  Google Scholar 

  5. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779.

    PubMed  PubMed Central  Google Scholar 

  6. Biobank U. Genotyping of 500,000 UK Biobank participants. Description of sample processing workflow and preparation of DNA for genotyping. 2015.

  7. Loh PR, Tucker G, Bulik-Sullivan BK, Vilhjalmsson BJ, Finucane HK, Salem RM, et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat Genet. 2015;47:284–90.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Loh P-R, Kichaev G, Gazal S, Schoech AP, Price AL. Mixed-model association for biobank-scale datasets. Nat Genet. 2018;50:906–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP, et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics. 2010;26:2336–7

    PubMed  PubMed Central  Google Scholar 

  10. Watanabe K, Taskesen E, van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. Nat Commun. 2017;8:1826.

    PubMed  PubMed Central  Google Scholar 

  11. de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: generalized gene-set analysis of GWAS data. PLOS Comput Biol. 2015;11:e1004219.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci. 2005;102:15545.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Bulik-Sullivan BK, Loh P-R, Finucane HK, Ripke S, Yang J, Schizophrenia Working Group of the Psychiatric Genomics C, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47:291–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet. 2018;50:668–81.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell 2018;173:1705–15. e1716.

  16. Duncan LE, Ratanatharathorn A, Aiello AE, Almli LM, Amstadter AB, Ashley-Koch AE, et al. Largest GWAS of PTSD (N = 20 070) yields genetic overlap with schizophrenia and sex differences in heritability. Mol Psychiatry. 2018;23:666–73.

    CAS  PubMed  Google Scholar 

  17. Otowa T, Hek K, Lee M, Byrne EM, Mirza SS, Nivard MG, et al. Meta-analysis of genome-wide association studies of anxiety disorders. Mol Psychiatry. 2016;21:1391–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Consortium GT. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45:580–5.

    Google Scholar 

  19. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira Manuel AR, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Human Genet. 2007;81:559–75.

    CAS  Google Scholar 

  20. Honti F, Meader S, Webber C. Unbiased functional clustering of gene variants with a phenotypic-linkage network. PLoS Comput Biol. 2014;10:e1003815.

    PubMed  PubMed Central  Google Scholar 

  21. Sandor C, Beer NL, Webber C. Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network. PLoS Comput Biol. 2017;13:e1005816.

    PubMed  PubMed Central  Google Scholar 

  22. Stefansson H, Helgason A, Thorleifsson G, Steinthorsdottir V, Masson G, Barnard J, et al. A common inversion under selection in Europeans. Nat Genet. 2005;37:129.

    CAS  PubMed  Google Scholar 

  23. de Jong S, Chepelev I, Janson E, Strengman E, van den Berg LH, Veldink JH, et al. Common inversion polymorphism at 17q21.31 affects expression of multiple genes in tissue-specific manner. BMC Genomics. 2012;13:458.

    PubMed  PubMed Central  Google Scholar 

  24. Kanematsu T, Jang I-S, Yamaguchi T, Nagahama H, Yoshimura K, Hidaka K, et al. Role of the PLC-related, catalytically inactive protein p130 in GABA(A) receptor function. EMBO J. 2002;21:1004–11.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Muller JS, Baumeister SK, Rasic VM, Krause S, Todorovic S, Kugler K, et al. Impaired receptor clustering in congenital myasthenic syndrome with novel RAPSN mutations. Neurology. 2006;67:1159–64.

    CAS  PubMed  Google Scholar 

  26. Schizophrenia Working Group of the Psychiatric Genomics C. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421–7.

    Google Scholar 

  27. Nakatani N, Hattori E, Ohnishi T, Dean B, Iwayama Y, Matsumoto I, et al. Genome-wide expression analysis detects eight genes with robust alterations specific to bipolar I disorder: relevance to neuronal network perturbation. Human Mol Genet. 2006;15:1949–62.

    CAS  Google Scholar 

  28. Camp AJ, Wijesinghe R. Calretinin: modulator of neuronal excitability. Int J Biochem Cell Biol. 2009;41:2118–21.

    CAS  PubMed  Google Scholar 

  29. Winter C, tom Dieck S, Boeckers TM, Bockmann J, Kampf U, Sanmarti-Vila L, et al. The presynaptic cytomatrix protein Bassoon: sequence and chromosomal localization of the human BSN gene. Genomics. 1999;57:389–97.

    CAS  PubMed  Google Scholar 

  30. Hamshere ML, Green EK, Jones IR, Jones L, Moskvina V, Kirov G, et al. Genetic utility of broadly defined bipolar schizoaffective disorder as a diagnostic concept. Br J Psychiatry. 2009;195:23–29.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhuang B, Su YS, Sockanathan S. FARP1 promotes the dendritic growth of spinal motor neuron subtypes through transmembrane Semaphorin6A and PlexinA4 signaling. Neuron. 2009;61:359–72.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Cheadle L, Biederer T. Activity-dependent regulation of dendritic complexity by semaphorin 3A through Farp1. J Neurosci. 2014;34:7999.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Broadbelt K, Byne W, Jones LB. Evidence for a decrease in basilar dendrites of pyramidal cells in schizophrenic medial prefrontal cortex. Schizophrenia Res. 2002;58:75–81.

    Google Scholar 

  34. Takita J, Chen Y, Okubo J, Sanada M, Adachi M, Ohki K, et al. Aberrations of NEGR1 on 1p31 and MYEOV on 11q13 in neuroblastoma. Cancer Sci. 2011;102:1645–50.

    CAS  PubMed  Google Scholar 

  35. van der Wees J, Schilthuis JG, Koster CH, Diesveld-Schipper H, Folkers GE, van der Saag PT, et al. Inhibition of retinoic acid receptor-mediated signalling alters positional identity in the developing hindbrain. Development. 1998;125:545–56.

    PubMed  Google Scholar 

  36. Wilkinson DG. Multiple roles of EPH receptors and ephrins in neural development. Nat Rev Neurosci. 2001;2:155–64.

    CAS  PubMed  Google Scholar 

  37. Srivastava S, Engels H, Schanze I, Cremer K, Wieland T, Menzel M, et al. Loss-of-function variants in HIVEP2 are a cause of intellectual disability. Eur J Human Genet. 2016;24:556–61.

    CAS  Google Scholar 

  38. Williams HJ, Moskvina V, Smith RL, Dwyer S, Russo G, Owen MJ, et al. Association between TCF4 and schizophrenia does not exert its effect by common nonsynonymous variation or by influencing cis-acting regulation of mRNA expression in adult human brain. Am J Med Genet B Neuropsychiatr Genet. 2011;156b:781–4.

    PubMed  Google Scholar 

  39. Hyde CL, Nagle MW, Tian C, Chen X, Paciga SA, Wendland JR, et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat Genet. 2016;48:1031–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Maccarrone G, Ditzen C, Yassouridis A, Rewerts C, Uhr M, Uhlen M, et al. Psychiatric patient stratification using biosignatures based on cerebrospinal fluid protein expression clusters. J Psychiat Res. 2013;47:1572–80.

    PubMed  Google Scholar 

  41. O’Reilly KC, Shumake J, Gonzalez-Lima F, Lane MA, Bailey SJ. Chronic administration of 13-cis-retinoic acid increases depression-related behavior in mice. Neuropsychopharmacology. 2006;31:1919.

    PubMed  Google Scholar 

  42. Tsai SY, Catts VS, Fullerton JM, Corley SM, Fillman SG, Weickert CS. Nuclear receptors and neuroinflammation in schizophrenia. Mol Neuropsychiatry. 2017;3:181–91.

    CAS  Google Scholar 

  43. Davies MN, Krause L, Bell JT, Gao F, Ward KJ, Wu H, et al. Hypermethylation in the ZBTB20 gene is associated with major depressive disorder. Genome Biol. 2014;15:R56–R56.

    PubMed  PubMed Central  Google Scholar 

  44. Su L, Ling W, Jiang J, Hu J, Fan J, Guo X, et al. Association of EPHB1rs11918092 and EFNB2 rs9520087 with psychopathological symptoms of schizophrenia in Chinese Zhuang and Han populations. Asia PacPsychiatry. 2016;8:306–8.

    Google Scholar 

  45. Dong X, Liao Z, Gritsch D, Hadzhiev Y, Bai Y, Locascio JJ, et al. Enhancers active in dopamine neurons are a primary link between genetic variation and neuropsychiatric disease. Nat Neurosci. 2018;21:1482–92.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Guo J, Zhang W, Zhang L, Ding H, Zhang J, Song C, et al. Probable involvement of p11 with interferon alpha induced depression. Sci Rep. 2016;6.

  47. Fontaine-Bisson B, Thorburn J, Gregory A, Zhang H, Sun G. Melanin-concentrating hormone receptor 1 polymorphisms are associated with components of energy balance in the Complex Diseases in the Newfoundland Population: Environment and Genetics (CODING) study. Am J Clin Nutr. 2014;99:384–91.

  48. Demontis D, Nyegaard M, Christensen JH, Severinsen J, Hedemand A, Hansen T, et al. The gene encoding the melanin-concentrating hormone receptor 1 is associated with schizophrenia in a Danish case-control sample. Psychiatr Genet. 2012;22:62–9.

  49. Chaturvedi M, Chander R. Development of emotional stability scale. Ind Psychiatry J. 2010;19:37.

  50. Marwaha S, Parsons N, Flanagan S, Broome M. The prevalence and clinical associations of mood instability in adults living in England: Results from the Adult Psychiatric Morbidity Survey 2007. Psychiatry Res. 2013;205:262–8.

Download references

Acknowledgements

JW is supported by the JMAS Sim Fellowship for depression research from the Royal College of Physicians of Edinburgh (173558). AF is supported by an MRC Doctoral Training Programme Studentship at the University of Glasgow (MR/K501335/1). RJS is supported by a UKRI Innovation–HDR-UK Fellowship (MR/S003061/1). KJAJ is supported by an MRC Doctoral Training Programme Studentship at the Universities of Glasgow and Edinburgh. DJS acknowledges the support of a Lister Prize Fellowship (173096) and the MRC Mental Health Data Pathfinder Award (MC_PC_17217). EMT and PJH are supported by the Oxford Health NIHR Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the National Health Service, NIHR or the Department of Health. Data were generated as part of the CommonMind Consortium supported by funding from Takeda Pharmaceuticals Company Limited, F. Hoffman-La Roche Ltd and NIH grants R01MH085542, R01MH093725, P50MH066392, P50MH080405, R01MH097276, RO1-MH-075916, P50M096891, P50MH084053S1, R37MH057881 and R37MH057881S1, HHSN271201300031C, AG02219, AG05138 and MH06692. Brain tissue for the study was obtained from the following brain bank collections: the Mount Sinai NIH Brain and Tissue Repository, the University of Pennsylvania Alzheimer’s Disease Core Center, the University of Pittsburgh NeuroBioBank and Brain and Tissue Repositories and the NIMH Human Brain Collection Core. CMC Leadership: Pamela Sklar, Joseph Buxbaum (Icahn School of Medicine at Mount Sinai), Bernie Devlin, David Lewis (University of Pittsburgh), Raquel Gur, Chang-Gyu Hahn (University of Pennsylvania), Keisuke Hirai, Hiroyoshi Toyoshiba (Takeda Pharmaceuticals Company Limited), Enrico Domenici, Laurent Essioux (F. Hoffman-La Roche Ltd), Lara Mangravite, Mette Peters (Sage Bionetworks), Thomas Lehner, Barbara Lipska (NIMH). We thank all participants in the UK Biobank study. UK Biobank was established by the Wellcome Trust, Medical Research Council, Department of Health, Scottish Government and Northwest Regional Development Agency. UK Biobank has also had funding from the Welsh Assembly Government and the British Heart Foundation. Data collection was funded by UK Biobank. The summary statistics of the GWAS are available upon request by contacting the corresponding author.

Author information

Authors and Affiliations

  1. Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK

    Joey Ward, Laura M. Lyall, Amy Ferguson, Rona J. Strawbridge, Donald M. Lyall, Breda Cullen, Nicholas Graham, Keira J. A. Johnston, Jill P. Pell & Daniel J. Smith

  2. Department of Psychiatry, University of Oxford, Oxford, UK

    Elizabeth M. Tunbridge & Paul J. Harrison

  3. Oxford Health NHS Foundation Trust, Oxford, UK

    Elizabeth M. Tunbridge & Paul J. Harrison

  4. UK Dementia Research Institute, Cardiff University, Cardiff, UK

    Cynthia Sandor & Caleb Webber

  5. Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden

    Rona J. Strawbridge

  6. Department of Physiology, Anatomy and Genetics, Oxford, UK

    Caleb Webber

  7. University of Cardiff, Cardiff, UK

    Valentina Escott-Price & Michael O’Donovan

  8. School of Life Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK

    Mark E. S. Bailey

Authors
  1. Joey Ward
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elizabeth M. Tunbridge
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cynthia Sandor
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Laura M. Lyall
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Amy Ferguson
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rona J. Strawbridge
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Donald M. Lyall
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Breda Cullen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Nicholas Graham
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Keira J. A. Johnston
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Caleb Webber
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Valentina Escott-Price
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Michael O’Donovan
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Jill P. Pell
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Mark E. S. Bailey
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Paul J. Harrison
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Daniel J. Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel J. Smith.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ward, J., Tunbridge, E.M., Sandor, C. et al. The genomic basis of mood instability: identification of 46 loci in 363,705 UK Biobank participants, genetic correlation with psychiatric disorders, and association with gene expression and function. Mol Psychiatry 25, 3091–3099 (2020). https://doi.org/10.1038/s41380-019-0439-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41380-019-0439-8

This article is cited by

Search

Advanced search

Quick links