Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways

Abstract

Neuroticism is an important risk factor for psychiatric traits, including depression1, anxiety2,3, and schizophrenia4,5,6. At the time of analysis, previous genome-wide association studies7,8,9,10,11,12 (GWAS) reported 16 genomic loci associated to neuroticism10,11,12. Here we conducted a large GWAS meta-analysis (n = 449,484) of neuroticism and identified 136 independent genome-wide significant loci (124 new at the time of analysis), which implicate 599 genes. Functional follow-up analyses showed enrichment in several brain regions and involvement of specific cell types, including dopaminergic neuroblasts (P = 3.49 × 10−8), medium spiny neurons (P = 4.23 × 10−8), and serotonergic neurons (P = 1.37 × 10−7). Gene set analyses implicated three specific pathways: neurogenesis (P = 4.43 × 10−9), behavioral response to cocaine processes (P = 1.84 × 10−7), and axon part (P = 5.26 × 10−8). We show that neuroticism’s genetic signal partly originates in two genetically distinguishable subclusters13 (‘depressed affect’ and ‘worry’), suggesting distinct causal mechanisms for subtypes of individuals. Mendelian randomization analysis showed unidirectional and bidirectional effects between neuroticism and multiple psychiatric traits. These results enhance neurobiological understanding of neuroticism and provide specific leads for functional follow-up experiments.

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: SNP-based associations with neuroticism in the GWAS meta-analysis.
Fig. 2: Mapping of genes and tissue expression and cell expression profiles.
Fig. 3: Genomic risk loci, eQTL associations, and chromatin interactions for chromosomes 6 and 8, containing cross-locus interactions.
Fig. 4: Genetic correlations between neuroticism and other traits.

References

  1. 1.

    Kendler, K. S. & Myers, J. The genetic and environmental relationship between major depression and the five-factor model of personality. Psychol. Med. 40, 801–806 (2010).

    Article  PubMed  CAS  Google Scholar 

  2. 2.

    Middeldorp, C. M. et al. in Biology of Personal and Individual Differences (ed. Canli, T.) Ch. 12, 251–272 (Guilford Press, New York and London, 2006).

  3. 3.

    Hettema, J. M., Neale, M. C., Myers, J. M., Prescott, C. A. & Kendler, K. S. A population-based twin study of the relationship between neuroticism and internalizing disorders. Am. J. Psychiatry 163, 857–864 (2006).

    Article  PubMed  Google Scholar 

  4. 4.

    Hayes, J. F., Osborn, D. P. J., Lewis, G., Dalman, C. & Lundin, A. Association of late adolescent personality with risk for subsequent serious mental illness among men in a Swedish nationwide cohort study. JAMA Psychiatry 74, 703–711 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Smeland, O. B. et al. Identification of genetic loci shared between schizophrenia and the Big Five personality traits. Sci. Rep. 7, 2222 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. 6.

    Van Os, J. & Jones, P. B. Neuroticism as a risk factor for schizophrenia. Psychol. Med 31, 1129–1134 (2001).

    PubMed  Google Scholar 

  7. 7.

    Genetics of Personality Consortium. Meta-analysis of genome-wide association studies for neuroticism, and the polygenic association with major depressive disorder. JAMA Psychiatry 72, 642–650 (2015).

  8. 8.

    Terracciano, A. et al. Genome-wide association scan for five major dimensions of personality. Mol. Psychiatry 15, 647–656 (2010).

    Article  PubMed  CAS  Google Scholar 

  9. 9.

    de Moor, M. H. M. et al. Meta-analysis of genome-wide association studies for personality. Mol. Psychiatry 17, 337–349 (2012).

    Article  PubMed  CAS  Google Scholar 

  10. 10.

    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).

    Article  PubMed  CAS  Google Scholar 

  11. 11.

    Smith, D. J. et al. Genome-wide analysis of over 106,000 individuals identifies 9 neuroticism-associated loci. Mol. Psychiatry 21, 1–9 (2016).

    Article  CAS  Google Scholar 

  12. 12.

    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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. 13.

    Nagel, M., Watanabe, K., Stringer, S., Posthuma, D. & van der Sluis, S. Item-level analyses reveal genetic heterogeneity in neuroticism. Nat. Commun. 9, 905 (2018).

    Article  PubMed  PubMed Central  CAS  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.

    Eriksson, N. et al. Web-based, participant-driven studies yield novel genetic associations for common traits. PLoS Genet. 6, e1000993 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. 16.

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. 17.

    Skol, A. D., Scott, L. J., Abecasis, G. R. & Boehnke, M. Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat. Genet. 38, 209–213 (2006).

    Article  PubMed  CAS  Google Scholar 

  18. 18.

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. 19.

    Yang, J. et al. Genomic inflation factors under polygenic inheritance. Eur. J. Hum. Genet. 19, 807–812 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. 21.

    Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. 22.

    Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. 23.

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

    Article  PubMed  CAS  Google Scholar 

  24. 24.

    Vilhjálmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. 25.

    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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. 26.

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  PubMed  CAS  Google Scholar 

  27. 27.

    GTEx Consortium. The genotype–tissue expression (GTEx) pilot analysis: multi-tissue gene regulation in humans. Science 348, 648–660 (2015).

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  28. 28.

    Skene, N. G. et al. Genetic identification of brain cell types underlying schizophrenia. Nat. Genet. 50, 825–833 (2018).

    Article  PubMed  CAS  Google Scholar 

  29. 29.

    Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium. A mega-analysis of genome-wide association studies for major depressive disorder. Mol. Psychiatry 18, 497–511 (2013).

    Article  CAS  Google Scholar 

  30. 30.

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. 31.

    Zhu, Z. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9, 224 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. 32.

    Griffith, M. et al. DGIdb: mining the druggable genome. Nat. Methods 10, 1209–1210 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. 33.

    Cotto, K. C. et al. DGIdb 3.0: a redesign and expansion of the drug–gene interaction database. Nucleic Acids Res. 46, D1068–D1073 (2017).

    Article  PubMed Central  Google Scholar 

  34. 34.

    Luciano, M. et al. Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism. Nat. Genet. 50, 6–11 (2018).

    Article  PubMed  CAS  Google Scholar 

  35. 35.

    Hyde, C. L. et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat. Genet. 48, 1031–1036 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. 36.

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

    Article  Google Scholar 

  37. 37.

    John, O. P. & Srivastava, S. The Big Five trait taxonomy: history, measurement and theoretical perspectives. Handb. Personal. Theory Res. 2, 102–138 (1999).

    Google Scholar 

  38. 38.

    Soto, C. J. & John, O. P. Ten facet scales for the Big Five Inventory: convergence with NEO PI-R facets, self-peer agreement and discriminant validity. J. Res. Pers. 43, 84–90 (2009).

    Article  Google Scholar 

  39. 39.

    Costa, P. & McCrae, R. R. Professional Manual: Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor-Inventory (NEO-FFI). (Psychological Assessment Resources, Odessa, FL, USA, 1992).

    Google Scholar 

  40. 40.

    Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  PubMed  CAS  Google Scholar 

  41. 41.

    Webb, B. T. et al. Molecular genetic influences on normative and problematic alcohol use in a population-based sample of college students. Front. Genet. 8, 30 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Abraham, G. & Inouye, M. Fast principal component analysis of large-scale genome-wide data. PLoS One 9, e93766 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. 43.

    Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. 44.

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. 45.

    Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. 46.

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. 47.

    Schmitt, A. D. et al. A compendium of chromatin contact maps reveals spatially active regions in the human genome. Cell Rep. 17, 2042–2059 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. 48.

    Roadmap Epigenomics Consortium. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  49. 49.

    Croft, D. et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 42, D472–D477 (2014).

    Article  PubMed  CAS  Google Scholar 

  50. 50.

    Coleman, J. R. I. et al. Functional consequences of genetic loci associated with intelligence in a meta-analysis of 87,740 individuals. Mol. Psychiatry https://doi.org/10.1038/s41380-018-0040-6 (2018).

Download references

Acknowledgements

We would like to thank the participants, including the 23andMe customers who consented to participate in research, and the researchers who collected and contributed to the data. This work was funded by the Netherlands Organization for Scientific Research through the following grants: NWO Brain and Cognition 433-09-228 (D.P.), NWO MagW VIDI 452-12-014 (S.v.d.S.), NWO VICI 435-13-005 (D.P.) and 645-000-003) (D.P.). P.R.J. was funded by the Sophia Foundation for Scientific Research (SSWO, grant no. S14-27). J.H.-L. was funded by the Swedish Research Council (Vetenskapsrådet, award 2014-3863), StratNeuro, the Wellcome Trust (108726/Z/15/Z), and the Swedish Brain Foundation (Hjärnfonden). N.G.S. was supported by the Wellcome Trust (108726/Z/15/Z). J.B. was funded by the Swiss National Science Foundation. The work of H.T. was supported by a NWO–VICI grant (NWO-ZonMW 016.VICI.170.200). Analyses were carried out on the Genetic Cluster computer, which is financed by the Netherlands Scientific Organization (NWO award 480-05-003 to D.P.), VU University (Amsterdam, The Netherlands), and the Dutch Brain Foundation and is hosted by the Dutch National Computing and Networking Services, SurfSARA. This research has been conducted using the UK Biobank resource (application 16406).

Author information

Affiliations

Authors

Consortia

Contributions

S.v.d.S. and D.P. conceived the study; M.N. and P.R.J. performed the analyses; S.S. performed the quality control on the UKB data and wrote a pipeline to facilitate data processing; K.W. constructed the tool for biological annotation and ran the analyses; H.T. and T.W. read and commented on the pre-final version of the manuscript; A.R.H., C.A.d.L., J.E.S., and T.J.C.P. wrote part of the analysis pipeline and assisted in interpreting results; N.G.S., A.B.M.-M., S.L., and J.H.-L. provided single-cell RNA-seq data for mouse brain cell types; J.B. and P.F.S. performed the single-cell gene expression analysis; and M.N., P.R.J., S.v.d.S., and D.P. wrote the paper. All authors discussed the results and commented on the paper.

Corresponding author

Correspondence to Danielle Posthuma.

Ethics declarations

Competing interests

J.H.-L. is a scientific advisor at Cartana and has received a grant from Roche. P.F. has received a grant from Lundbeck and is currently a member of the advisory committee. Over the last 3 years, P.F. has been on the scientific advisory board at Pfizer, received a consultation fee from Element Genomics, and received speaker reimbursement fees from Roche.

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 Note, Supplementary Results and Supplementary Figures 1–17

Reporting Summary

Supplementary Tables 1–42

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Nagel, M., Jansen, P.R., Stringer, S. et al. Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways. Nat Genet 50, 920–927 (2018). https://doi.org/10.1038/s41588-018-0151-7

Download citation

Further reading

Search

Quick links

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