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Symptom-level modelling unravels the shared genetic architecture of anxiety and depression

Nature Human Behaviour volume 5, pages 1432–1442 (2021)Cite this article

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Abstract

Depression and anxiety are highly prevalent and comorbid psychiatric traits that cause considerable burden worldwide. Here we use factor analysis and genomic structural equation modelling to investigate the genetic factor structure underlying 28 items assessing depression, anxiety and neuroticism, a closely related personality trait. Symptoms of depression and anxiety loaded on two distinct, although highly genetically correlated factors, and neuroticism items were partitioned between them. We used this factor structure to conduct genome-wide association analyses on latent factors of depressive symptoms (89 independent variants, 61 genomic loci) and anxiety symptoms (102 variants, 73 loci) in the UK Biobank. Of these associated variants, 72% and 78%, respectively, replicated in an independent cohort of approximately 1.9 million individuals with self-reported diagnosis of depression and anxiety. We use these results to characterize shared and trait-specific genetic associations. Our findings provide insight into the genetic architecture of depression and anxiety and comorbidity between them.

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Fig. 1: Genetic EFA of depression, anxiety and neuroticism.
Fig. 2: SNP-based associations of the DEP and ANX latent factors.
Fig. 3: Polygenic risk prediction of depressive and anxiety symptoms.
Fig. 4: Genetic correlations with other complex traits.
Fig. 5: Shared and trait-specific genetic associations of depressive and anxiety symptoms.

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Data availability

All GWAS summary statistics generated from UK Biobank data are available from the authors upon request. Individual-level data for UK Biobank participants are available to eligible researchers through the UK Biobank (www.biobank.ac.uk). Access to 23andMe data is available upon request to 23andMe (further information is available from https://research.23andme.com/collaborate/).

Code availability

Code used to conduct analyses presented in this manuscript is available from the authors upon reasonable request.

References

  1. Vigo, D., Thornicroft, G. & Atun, R. Estimating the true global burden of mental illness. Lancet Psychiatry 3, 171–178 (2016).

    Article  PubMed  Google Scholar 

  2. Depression and Other Common Mental Disorders: Global Health Estimates (World Health Organization, 2017).

  3. Lamers, F. et al. Comorbidity patterns of anxiety and depressive disorders in a large cohort study: the Netherlands study of depression and anxiety (NESDA). J. Clin. Psychiatry 72, 341–348 (2011).

    Article  PubMed  Google Scholar 

  4. Hettema, J. M., Neale, M. C. & Kendler, K. S. A review and meta-analysis of the genetic epidemiology of anxiety disorders. Am. J. Psychiatry 158, 1568–1578 (2001).

    Article  CAS  PubMed  Google Scholar 

  5. Sullivan, P. F., Neale, M. C. & Kendler, K. S. Genetic epidemiology of major depression: review and meta-analysis. Am. J. Psychiatry 157, 1552–1562 (2000).

    Article  CAS  PubMed  Google Scholar 

  6. Middeldorp, C. M., Cath, D. C., Van Dyck, R. & Boomsma, D. I. The co-morbidity of anxiety and depression in the perspective of genetic epidemiology. A review of twin and family studies. Psychol. Med. 35, 611–624 (2005).

    Article  CAS  PubMed  Google Scholar 

  7. McCrae, R. R. & Costa, P. T. Updating Norman’s “adequacy taxonomy”: intelligence and personality dimensions in natural language and in questionnaires. J. Pers. Soc. Psychol. 49, 710–721 (1985).

    Article  CAS  PubMed  Google Scholar 

  8. Eysenck, H. J. & Eysenck, M. W. Personality and Individual Differences: A Natural Science Approach (Plenum, New York, NY, 1985).

    Book  Google Scholar 

  9. Kotov, R., Gamez, W., Schmidt, F. & Watson, D. Linking “big” personality traits to anxiety, depressive, and substance use disorders: a meta-analysis. Psychol. Bull. 136, 768–821 (2010).

    Article  PubMed  Google Scholar 

  10. Gray, J. A. & McNaughton, N. The Neuropsychology of Anxiety. An Enquiry into the Functions of the Septo-Hippocampal System (Oxford Univ. Press, Oxford, 2000).

    Google Scholar 

  11. Ormel, J. et al. Neuroticism and common mental disorders: meaning and utility of a complex relationship. Clin. Psychol. Rev. 33, 686–697 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Zinbarg, R. E. et al. Testing a hierarchical model of neuroticism and its cognitive facets: latent structure and prospective prediction of first onsets of anxiety and unipolar mood disorders during 3 years in late adolescence. Clin. Psychol. Sci. 4, 805–824 (2016).

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

  14. Hettema, J. M., Prescott, C. A. & Kendler, K. S. Genetic and environmental sources of covariation between generalized anxiety disorder and neuroticism. Am. J. Psychiatry 161, 1581–1587 (2004).

    Article  PubMed  Google Scholar 

  15. Jardine, R., Martin, N. G. & Henderson, A. S. Genetic covariation between neuroticism and the symptoms of anxiety and depression. Genet. Epidemiol. 1, 89–107 (1984).

    Article  CAS  PubMed  Google Scholar 

  16. Fanous, A., Gardner, C. O., Prescott, C. A., Cancro, R. & Kendler, K. S. Neuroticism, major depression and gender: a population-based twin study. Psychol. Med. 32, 719–728 (2002).

    Article  CAS  PubMed  Google Scholar 

  17. 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 

  18. Purves, K. L. et al. A major role for common genetic variation in anxiety disorders. Mol. Psychiatry 25, 3292–3303 (2020).

    Article  CAS  PubMed  Google Scholar 

  19. Meier, S. M. et al. Genetic variants associated with anxiety and stress-related disorders: a genome-wide association study and mouse-model study. JAMA Psychiatry 76, 924–932 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Howard, D. M. et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat. Neurosci. 22, 343–352 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Nagel, M. 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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  24. Levey, D. F. et al. Reproducible genetic risk loci for anxiety: results from ∼200,000 participants in the Million Veteran Program. Am. J. Psychiatry 177, 223–232 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Adams, M. J. et al. Genetic stratification of depression by neuroticism: revisiting a diagnostic tradition. Psychol. Med. 50, 2526–2535 (2020).

    Article  PubMed  Google Scholar 

  27. Ormel, J., Riese, H. & Rosmalen, J. G. M. Interpreting neuroticism scores across the adult life course: immutable or experience-dependent set points of negative affect? Clin. Psychol. Rev. 32, 71–79 (2012).

    Article  PubMed  Google Scholar 

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

    Article  Google Scholar 

  29. 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  Google Scholar 

  30. Thorp, J. G. et al. Genetic heterogeneity in self-reported depressive symptoms identified through genetic analyses of the PHQ-9. Psychol. Med. 50, 2585–2396 (2020).

    Article  Google Scholar 

  31. Grotzinger, A. D. et al. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat. Hum. Behav. 3, 513–525 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Pickrell, J. K. et al. Detection and interpretation of shared genetic influences on 42 human traits. Nat. Genet. 48, 709–717 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Buniello, A. et al. The NHGRI-EBI GWAS catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019).

    Article  CAS  PubMed  Google Scholar 

  34. Beard, C. et al. Network analysis of depression and anxiety symptom relationships in a psychiatric sample. Psychol. Med. 46, 3359–3369 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Kendler, K. S., Heath, A. C., Martin, N. G. & Eaves, L. J. Symptoms of anxiety and symptoms of depression. Same genes, different environments? Arch. Gen. Psychiatry 44, 451–457 (1987).

    Article  CAS  PubMed  Google Scholar 

  36. Andrews, F. M. Construct validity and error components of survey measures: A structural modeling approach. Public Opin. Q. 48, 409–442 (1984).

    Article  Google Scholar 

  37. Franić, S., Dolan, C. V., Borsboom, D., van Beijsterveldt, C. E. & Boomsma, D. I. Three-and-a-half-factor model? The genetic and environmental structure of the CBCL/6-18 internalizing grouping. Behav. Genet. 44, 254–268 (2014).

    PubMed  Google Scholar 

  38. Fergusson, D. M., Horwood, L. J. & Boden, J. M. Structure of internalising symptoms in early adulthood. Br. J. Psychiatry 189, 540–546 (2006).

    Article  PubMed  Google Scholar 

  39. Waszczuk, M. A. et al. Redefining phenotypes to advance psychiatric genetics: Implications from hierarchical taxonomy of psychopathology. J. Abnorm. Psychol. 129, 143–161 (2020).

    Article  PubMed  Google Scholar 

  40. 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  CAS  PubMed  PubMed Central  Google Scholar 

  41. Howard, D. M. et al. Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nat. Commun. 9, 1470 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  42. 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  CAS  PubMed  PubMed Central  Google Scholar 

  43. Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229–237 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Baselmans, B. M. L. et al. Multivariate genome-wide analyses of the well-being spectrum. Nat. Genet. 51, 445–451 (2019).

    Article  CAS  PubMed  Google Scholar 

  45. Hill, W. D. et al. Genetic contributions to two special factors of neuroticism are associated with affluence, higher intelligence, better health, and longer life. Mol. Psychiatry 25, 3034–3052 (2020).

    Article  PubMed  Google Scholar 

  46. Igna, C. V., Julkunen, J. & Vanhanen, H. Vital exhaustion, depressive symptoms and serum triglyceride levels in high-risk middle-aged men. Psychiatry Res. 187, 363–369 (2011).

    Article  CAS  PubMed  Google Scholar 

  47. Richter, N., Juckel, G. & Assion, H. J. Metabolic syndrome: a follow-up study of acute depressive inpatients. Eur. Arch. Psychiatry Clin. Neurosci. 260, 41–49 (2010).

    Article  CAS  PubMed  Google Scholar 

  48. Akbaraly, T. N. et al. Association between metabolic syndrome and depressive symptoms in middle-aged adults. Diabetes Care 32, 499–504 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Glueck, C. J. et al. Improvement in symptoms of depression and in an index of life stressors accompany treatment of severe hypertriglyceridemia. Biol. Psychiatry 34, 240–252 (1993).

    Article  CAS  PubMed  Google Scholar 

  50. Pan, Y. et al. Association between anxiety and hypertension: a systematic review and meta-analysis of epidemiological studies. Neuropsychiatr. Dis. Treat. 11, 1121–1130 (2015).

    PubMed  PubMed Central  Google Scholar 

  51. Rayner, C. et al. A genome-wide association meta-analysis of prognostic outcomes following cognitive behavioural therapy in individuals with anxiety and depressive disorders. Transl. Psychiatry 9, 150 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Young, J. F., Mufson, L. & Davies, M. Impact of comorbid anxiety in an effectiveness study of interpersonal psychotherapy for depressed adolescents. J. Am. Acad. Child Adolesc. Psychiatry 45, 904–912 (2006).

    Article  PubMed  Google Scholar 

  53. Kessler, R. C. et al. Co-morbid major depression and generalized anxiety disorders in the national comorbidity survey follow-up. Psychol. Med. 38, 365–374 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Emmanuel, J., Simmonds, S. & Tyrer, P. Systematic review of the outcome of anxiety and depressive disorders. Br. J. Psychiatry 173, 35–41 (1998).

    Article  Google Scholar 

  55. Walker, E. A. et al. Predictors of outcome in a primary care depression trial. J. Gen. Intern. Med. 15, 859–867 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Altamura, A. C., Montresor, C., Salvadori, D. & Mundo, E. Does comorbid subthreshold anxiety affect clinical presentation and treatment response in depression? A preliminary 12-month naturalistic study. Int. J. Neuropsychopharmacol. 7, 481–487 (2004).

    Article  PubMed  Google Scholar 

  57. Achim, A. M. et al. How prevalent are anxiety disorders in schizophrenia? A meta-analysis and critical review on a significant association. Schizophr. Bull. 37, 811–821 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Emsley, R. A., Oosthuizen, P. P., Joubert, A. F., Roberts, M. C. & Stein, D. J. Depressive and anxiety symptoms in patients with schizophrenia and schizophreniform disorder. J. Clin. Psychiatry 60, 747–751 (1999).

    Article  CAS  PubMed  Google Scholar 

  59. Fluharty, M., Taylor, A. E., Grabski, M. & Munafò, M. R. The association of cigarette smoking with depression and anxiety: A systematic review. Nicotine Tob. Res. 19, 3–13 (2017).

    Article  PubMed  Google Scholar 

  60. Schwabe, I. et al. Unraveling the genetic architecture of major depressive disorder: merits and pitfalls of the approaches used in genome-wide association studies. Psychol. Med. 49, 2646–2656 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Kendler, K. S. et al. Shared and specific genetic risk factors for lifetime major depression, depressive symptoms and neuroticism in three population-based twin samples. Psychol. Med. 49, 2745–2753 (2019).

    Article  PubMed  Google Scholar 

  62. Cai, N. et al. Minimal phenotyping yields genome-wide association signals of low specificity for major depression. Nat. Genet. 52, 437–447 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Smoller, J. W. et al. Psychiatric genetics and the structure of psychopathology. Mol. Psychiatry 24, 409–420 (2019).

    Article  PubMed  Google Scholar 

  64. Lee, P. H. et al. Genomic relationships, Novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cell 179, 1469–1482 (2019).

    Article  Google Scholar 

  65. Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51, 1339–1348 (2019).

    Article  CAS  PubMed  Google Scholar 

  66. Sanchez-Roige, S. Emerging phenotyping strategies will advance our understanding of psychiatric genetics. Nat. Neurosci. 23, 475–480 (2020).

    Article  CAS  PubMed  Google Scholar 

  67. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. MacGregor, S. et al. Genome-wide association study of intraocular pressure uncovers new pathways to glaucoma. Nat. Genet. 50, 1067–1071 (2018).

    Article  CAS  PubMed  Google Scholar 

  69. Kroenke, K., Spitzer, R. L. & Williams, J. B. W. The PHQ‐9. J. Gen. Intern. Med. 16, 606–613 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Spitzer, R. L., Kroenke, K., Williams, J. B. W. & Löwe, B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch. Intern. Med. 166, 1092–1097 (2006).

    Article  PubMed  Google Scholar 

  71. Davis, K. A. S. et al. Mental health in UK Biobank—development, implementation and results from an online questionnaire completed by 157 366 participants: a reanalysis. BJPsych Open 6, e18 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  72. 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  Google Scholar 

  73. Velicer, W. F. Determining the number of components from the matrix of partial correlations. Psychometrika 41, 321–327 (1976).

    Article  Google Scholar 

  74. Kaiser, H. F. The application of electronic computers to factor analysis. Educ. Psychol. Meas. 20, 141–151 (1960).

    Article  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  76. Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Bigdeli, T. B. et al. A simple yet accurate correction for winner’s curse can predict signals discovered in much larger genome scans. Bioinformatics 32, 2598–2603 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Bedford, A., Foulds, G. A. & Sheffield, B. F. A new personal disturbance scale (DSSI/sAD). Br. J. Soc. Clin. Psychol. 15, 387–394 (1976).

    Article  CAS  PubMed  Google Scholar 

  79. Lloyd-Jones, L. R. et al. Improved polygenic prediction by Bayesian multiple regression on summary statistics. Nat. Commun. 10, 5086 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  80. 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  CAS  PubMed  PubMed Central  Google Scholar 

  81. Campos, A. I. et al. Genetic aetiology of self-harm ideation and behaviour. Sci. Rep. 10, 9713 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Chang, L.-H. et al. Association between polygenic risk for tobacco or alcohol consumption and liability to licit and illicit substance use in young Australian adults. Drug Alcohol Depend. 197, 271–279 (2019).

    Article  PubMed  Google Scholar 

  83. Yang, J., Zaitlen, N. A., Goddard, M. E., Visscher, P. M. & Price, A. L. Advantages and pitfalls in the application of mixed-model association methods. Nat. Genet. 46, 100–106 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  84. 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  Google Scholar 

  85. Berisa, T. & Pickrell, J. K. Approximately independent linkage disequilibrium blocks in human populations. Bioinformatics 32, 283–285 (2016).

    CAS  PubMed  Google Scholar 

  86. Pickrell, J. K. Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am. J. Hum. Genet. 94, 559–573 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Cuéllar-Partida, G., et al. Complex-Traits Genetics Virtual Lab: a community-driven web platform for post-GWAS analyses. Preprint at bioRxiv https://doi.org/10.1101/518027 (2019).

  88. Lonsdale, J. et al. The Genotype–Tissue expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

    Article  CAS  Google Scholar 

  89. Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Ramasamy, A. et al. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat. Neurosci. 17, 1418–1428 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. 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  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the research participants of all cohorts for making this study possible. This work was conducted using the UK Biobank Resource (application number 25331). J.G.T. and A.I.C. are supported by a University of Queensland Research Training Scholarship. N.G.M. received funding from the Australian National Health and Medical Research Council (NHMRC) to conduct surveys in the QIMR Adult Twin Study. S.M. is supported by an NHMRC Fellowship.

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  1. A list of members and their affiliations appears in the Supplementary Information.

Authors and Affiliations

  1. Translational Neurogenomics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

    Jackson G. Thorp, Zachary F. Gerring & Eske M. Derks

  2. Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia

    Jackson G. Thorp & Adrian I. Campos

  3. Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

    Adrian I. Campos & Nicholas G. Martin

  4. Department of Psychology, University of Texas at Austin, Austin, TX, USA

    Andrew D. Grotzinger

  5. Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

    Jiyuan An, Jue-Sheng Ong & Stuart MacGregor

  6. 23andMe, Sunnyvale, CA, USA

    Wei Wang & Suyash Shringarpure

  7. Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia

    Enda M. Byrne

  8. Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

    Sarah E. Medland

  9. Child Health Research Centre, University of Queensland, Brisbane, Queensland, Australia

    Christel M. Middeldorp

  10. Child and Youth Mental Health Service, Children’s Health Queensland Hospital and Health Service, Brisbane, Queensland, Australia

    Christel M. Middeldorp

  11. Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands

    Christel M. Middeldorp

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Contributions

J.G.T. and E.M.D. conceived and directed the study. J.G.T. performed most of the statistical and bioinformatics analyses with the UK Biobank data, with support from A.I.C., A.D.G., Z.F.G., J.A., J.-S.O. and E.M.D. W.W., S.S. and the 23andMe Research Team conducted the replication analyses in the 23andMe cohort. A.I.C. conducted the polygenic risk prediction analyses, with support from J.G.T. N.G.M. collected and contributed data from the QIMR Adult Twin Study. Z.F.G., E.M.B., S.M., N.G.M., S.E.M., C.M.M. and E.M.D. provided methodological and psychiatric expertise. J.G.T. and E.M.D. wrote the manuscript, with all authors providing comments and suggestions.

Corresponding authors

Correspondence to Jackson G. Thorp or Eske M. Derks.

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Competing interests

W.W., S.S. and members of the 23andMe Research Team are employees of 23andMe Inc. The other authors declare no competing interests.

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Peer review information Nature Human Behaviour thanks Evangelos Evangelou and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Thorp, J.G., Campos, A.I., Grotzinger, A.D. et al. Symptom-level modelling unravels the shared genetic architecture of anxiety and depression. Nat Hum Behav 5, 1432–1442 (2021). https://doi.org/10.1038/s41562-021-01094-9

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