Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism

An Author Correction to this article was published on 29 January 2019

This article has been updated

Abstract

Neuroticism is a relatively stable personality trait characterized by negative emotionality (for example, worry and guilt)1; heritability estimated from twin studies ranges from 30 to 50%2, and SNP-based heritability ranges from 6 to 15%3,4,5,6. Increased neuroticism is associated with poorer mental and physical health7,8, translating to high economic burden9. Genome-wide association studies (GWAS) of neuroticism have identified up to 11 associated genetic loci3,4. Here we report 116 significant independent loci from a GWAS of neuroticism in 329,821 UK Biobank participants; 15 of these loci replicated at P < 0.00045 in an unrelated cohort (N = 122,867). Genetic signals were enriched in neuronal genesis and differentiation pathways, and substantial genetic correlations were found between neuroticism and depressive symptoms (rg = 0.82, standard error (s.e.) = 0.03), major depressive disorder (MDD; rg = 0.69, s.e. = 0.07) and subjective well-being (rg = –0.68, s.e. = 0.03) alongside other mental health traits. These discoveries significantly advance understanding of neuroticism and its association with MDD.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1
Fig. 2: Regional association plots for suggestive and significant signals in UK Biobank.

Change history

  • 29 January 2019

    In the version of this article initially published, in Table 2, the descriptions of pathways and definitions in the first and last columns did not correctly correspond to the values in the other columns. The error has been corrected in the HTML and PDF versions of the article.

References

  1. 1.

    Matthews, G., Deary, I. J. & Whiteman, M. C. Personality Traits (Cambridge University Press, Cambridge, UK, 2009).

    Google Scholar 

  2. 2.

    Vukasovic, T. & Bratko, D. Heritability of personality: a meta-analysis of behavior genetic studies. Psychol. Bull. 141, 769–785 (2015).

    PubMed  Google Scholar 

  3. 3.

    Smith, D. J. et al. Genome-wide analysis of over 106 000 individuals identifies 9 neuroticism-associated loci. Mol. Psychiatry 21, 749–757 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Okbay, A. et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet. 48, 624–633 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Power, R. A. & Pluess, M. Heritability estimates of the Big Five personality traits based on common genetic variants. Transl. Psychiatry 5, e604 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Vinkhuyzen, A. A. et al. Common SNPs explain some of the variation in the personality dimensions of neuroticism and extraversion. Transl. Psychiatry 2, e102 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Kubzansky, L. D., Martin, L. T. & Buka, S. L. Early manifestations of personality and adult health: a life course perspective. Health. Psychol. 28, 364–372 (2009).

    PubMed  Google Scholar 

  8. 8.

    Strickhouser, J. E., Zell, E. & Krizan, Z. Does personality predict health and well-being? A metasynthesis. Health. Psychol. 36, 797–810 (2017).

    PubMed  Google Scholar 

  9. 9.

    Cuijpers, P. et al. Economic costs of neuroticism: a population-based study. Arch. Gen. Psychiatry 67, 1086–1093 (2010).

    PubMed  Google Scholar 

  10. 10.

    Few, L. R. et al. Genetic variation in personality traits explains genetic overlap between borderline personality features and substance use disorders. Addiction 109, 2118–2127 (2014).

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Kendler, K. S., Gatz, M., Gardner, C. O. & Pedersen, N. L. Personality and major depression: a Swedish longitudinal, population-based twin study. Arch. Gen. Psychiatry 63, 1113–1120 (2006).

    PubMed  Google Scholar 

  12. 12.

    Wray, N. R. & Sullivan, P. F. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Preprint at bioRxiv https://doi.org/10.1101/167577 (2017).

  13. 13.

    Sudlow, C. 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. 12, e1001779 (2015).

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    Bycroft, C. et al. Genome-wide genetic data on ~500,000 UK Biobank participants. Preprint at bioRxiv https://doi.org/10.1101/166298 (2017).

  15. 15.

    Eysenck, S. B., Eysenck, H. J. & Barrett, P. A revised version of the psychoticism scale. Pers. Individ. Dif. 6, 21–29 (1985).

    Google Scholar 

  16. 16.

    McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379 (2013).

    PubMed Central  Google Scholar 

  19. 19.

    Sekar, A. et al. Schizophrenia risk from complex variation of complement component 4. Nature 530, 177–183 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Hegyi, H. GABBR1 has a HERV-W LTR in its regulatory region—a possible implication for schizophrenia. Biol. Direct 8, 5 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Wei, J. & Hemmings, G. P. TNXB locus may be a candidate gene predisposing to schizophrenia. Am. J. Med. Genet. B Neuropsychiatr. Genet. 125B, 43–49 (2004).

    CAS  PubMed  Google Scholar 

  22. 22.

    Lo, M.-T. et al. Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders. Nat. Genet. 49, 152–156 (2017).

    CAS  PubMed  Google Scholar 

  23. 23.

    de Moor, M. H. et al. Meta-analysis of genome-wide association studies for neuroticism, and the polygenic association with major depressive disorder. JAMA Psychiatry 72, 642–650 (2015).

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Hu, Y. et al. GWAS of 89,283 individuals identifies genetic variants associated with self-reporting of being a morning person. Nat. Commun. 7, 10448 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Duggan, K. A., Friedman, H. S., McDevitt, E. A. & Mednick, S. C. Personality and healthy sleep: the importance of conscientiousness and neuroticism. PLoS One 9, e90628 (2014).

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Mi, H., Muruganujan, A. & Thomas, P. D. PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees. Nucleic Acids Res. 41, D377–D386 (2013).

    CAS  PubMed  Google Scholar 

  27. 27.

    Sulser, F. The role of CREB and other transcription factors in the pharmacotherapy and etiology of depression. Ann. Med. 34, 348–356 (2002).

    CAS  PubMed  Google Scholar 

  28. 28.

    Wang, H. et al. Forkhead box O transcription factors as possible mediators in the development of major depression. Neuropharmacology 99, 527–537 (2015).

    CAS  PubMed  Google Scholar 

  29. 29.

    Malan-Müller, S., Hemmings, S. M. J. & Seedat, S. Big effects of small RNAs: a review of microRNAs in anxiety. Mol. Neurobiol. 47, 726–739 (2013).

    PubMed  Google Scholar 

  30. 30.

    Dwivedi, Y. Emerging role of microRNAs in major depressive disorder: diagnosis and therapeutic implications. Dialogues Clin. Neurosci. 16, 43–61 (2014).

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Ambrosini, A. et al. Possible involvement of the CACNA1E gene in migraine: a search for single nucleotide polymorphism in different clinical phenotypes. Headache 57, 1136–1144 (2017).

    PubMed  Google Scholar 

  32. 32.

    Schraa-Tam, C. K. L. et al. fMRI activities in the emotional cerebellum: a preference for negative stimuli and goal-directed behavior. Cerebellum 11, 233–245 (2012).

    PubMed  Google Scholar 

  33. 33.

    Schutter, D. J. L. G., Koolschijn, P. C. M. P., Peper, J. S. & Crone, E. A. The cerebellum link to neuroticism: a volumetric MRI association study in healthy volunteers. PLoS One 7, e37252 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Grinberg, M. et al. Mitochondrial carrier homolog 2 is a target of tBID in cells signaled to die by tumor necrosis factor α. Mol. Cell. Biol. 25, 4579–4590 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Lucassen, P. J., Oomen, C. A., Schouten, M., Encinas, J. M. & Fitzsimons, C. P. in Adult Neurogenesis in the Hippocampus (ed. Canales, J. J.) 177–206 (Academic Press, San Diego, CA, 2016).

    Google Scholar 

  38. 38.

    Schoenfeld, T. J. & Cameron, H. A. Adult neurogenesis and mental illness. Neuropsychopharmacology 40, 113–128 (2015).

    PubMed  Google Scholar 

  39. 39.

    Wray, N. R. et al. Anxiety and comorbid measures associated with PLXNA2. Arch. Gen. Psychiatry 64, 318–326 (2007).

    CAS  PubMed  Google Scholar 

  40. 40.

    Redies, C., Hertel, N. & Hübner, C. A. Cadherins and neuropsychiatric disorders. Brain Res. 1470, 130–144 (2012).

    CAS  PubMed  Google Scholar 

  41. 41.

    Chang, H. et al. The protocadherin 17 gene affects cognition, personality, amygdala structure and function, synapse development and risk of major mood disorders. Mol. Psychiatry http://dx.doi.org/10.1038/mp.2016.231 (2017).

  42. 42.

    DeYoung, C. G., Cicchetti, D. & Rogosch, F. A. Moderation of the association between childhood maltreatment and neuroticism by the corticotropin-releasing hormone receptor 1 gene. J. Child Psychol. Psychiatry 52, 898–906 (2011).

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Binder, E. B. & Nemeroff, C. B. The CRF system, stress, depression and anxiety—insights from human genetic studies. Mol. Psychiatry 15, 574–588 (2010).

    CAS  PubMed  Google Scholar 

  44. 44.

    Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Smith, B. H. et al. Cohort profile: Generation Scotland: Scottish Family Health Study (GS:SFHS). The study, its participants and their potential for genetic research on health and illness. Int. J. Epidemiol. 42, 689–700 (2013).

    PubMed  Google Scholar 

  46. 46.

    Burton, R. The Anatomy of Melancholy (eds. Faulkner, T. C., Kiessling, N. K. & Blair, R. L.) (Oxford University Press, Oxford, UK, 1989).

  47. 47.

    Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Boyle, A. P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790–1797 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Mi, H. et al. PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements. Nucleic Acids Res. 45 (D1), D183–D189 (2017).

    CAS  Google Scholar 

  51. 51.

    Gene Ontology Consortium. Expansion of the Gene Ontology knowledgebase and resources. Nucleic Acids Res. 45 (D1), D331–D338 (2017).

    Google Scholar 

  52. 52.

    Hemani, G. et al. MR-Base: a platform for systematic causal inference across the phenome using billions of genetic associations. Preprint at bioRxiv https://doi.org/10.1101/078972 (2016).

  53. 53.

    Tobacco and Genetics Consortium. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. 42, 441–447 (2010).

    Google Scholar 

  54. 54.

    Rietveld, C. A. et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340, 1467–1471 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Euesden, J., Lewis, C. M. & O’Reilly, P. F. PRSice: polygenic risk score software. Bioinformatics 31, 1466–1468 (2015).

    CAS  PubMed  Google Scholar 

  56. 56.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

Download references

Acknowledgements

This research has been conducted using the UK Biobank Resource (application numbers 10279 and 4844). Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). Ethical approval for the GS:SFHS study was obtained from the Tayside Committee on Medical Research Ethics (05/S1401/89 Tayside Committee on Medical Research Ethics A). We are grateful to all the families who took part, the general practitioners and the Scottish School of Primary Care for their help in recruiting them, and the whole Generation Scotland team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, healthcare assistants and nurses. This work was supported by the University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross-council Lifelong Health and Wellbeing Initiative (MR/K026992/1); funding from the Biotechnology and Biological Sciences Research Council (BBSRC) and the Medical Research Council (MRC) is gratefully acknowledged. This report represents independent research funded in part by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and the Maudsley NHS Foundation Trust and King’s College London. W.D.H. is supported by a grant from Age UK (Disconnected Mind Project). A.M.M. and I.J.D. are supported by funding from a Wellcome Trust Strategic Award (104036/Z/14/Z).

Author information

Affiliations

Authors

Contributions

M.L. drafted the manuscript with contributions from W.D.H. and I.J.D. G.D., D.C.L., R.E.M., M.J.A. and D.M.H. performed quality control of UK Biobank and/or Generation Scotland data. M.L., G.D., S.P.H. and M.S. analyzed the data. T.-K.C., C.F.-R., W.D.H. and S.E.H. performed or assisted with downstream analysis. C.R.G., C.M.L. and A.M.M. provided critical comments on the manuscript draft and analysis. M.L. and I.J.D. coordinated the work. All authors commented on and approved the manuscript.

Corresponding author

Correspondence to Michelle Luciano.

Ethics declarations

Competing interests

I.J.D. was a participant in UK Biobank. The other authors declare 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

Supplementary Text and Figures

Supplementary Figures 1–5, Supplementary Tables 1 and 11–13, and Supplementary Note

Life Sciences Reporting Summary

Supplementary Tables 2–10

Supplementary Tables 2–10

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Luciano, M., Hagenaars, S.P., Davies, G. et al. Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism. Nat Genet 50, 6–11 (2018). https://doi.org/10.1038/s41588-017-0013-8

Download citation

Further reading

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing