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Multivariate genome-wide analysis of education, socioeconomic status and brain phenome

Abstract

Socioeconomic status (SES) and education (EDU) are phenotypically associated with psychiatric disorders and behaviours. It remains unclear how these associations influence genetic risk for psychopathology, psychosocial factors and EDU and/or SES (EDU/SES) individually. Using information from >1 million individuals, we conditioned the genetic risk for psychiatric disorders, personality traits, brain imaging phenotypes and externalizing behaviours with genome-wide data for EDU/SES. Accounting for EDU/SES significantly affected the observed heritability of psychiatric traits, ranging from 2.44% h2 decrease for bipolar disorder to 14.2% h2 decrease for Tourette syndrome. Neuroticism h2 significantly increased by 20.23% after conditioning with SES. After EDU/SES conditioning, neuronal cell types were identified for risky behaviour (excitatory), major depression (inhibitory), schizophrenia (excitatory and γ-aminobutyric acid (GABA) mediated) and bipolar disorder (excitatory). Conditioning with EDU/SES also revealed unidirectional causality between brain morphology, psychopathology and psychosocial factors. Our results indicate that genetic discoveries related to psychopathology and psychosocial factors may be limited by genetic overlap with EDU/SES.

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Fig. 1: Trait inclusion genetic correlations.
Fig. 2: Heritability (h2) changes.
Fig. 3: Cell-type transcriptomic profile enrichments underlying psychopathology and psychosocial factors.
Fig. 4: Trait loading onto latent factors.
Fig. 5: Causal relationships masked by EDU and SES effects.
Fig. 6: LCV relationship network.

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Data availability

All GWAS association data and analysis materials used in this study are publicly available for download by qualified researchers. All data used to make conclusions discussed in this study are provided as Supplementary Material.

Social Science Genetic Association Consortium: https://www.thessgac.org

Psychiatric Genomics Consortium: https://www.med.unc.edu/pgc/download-results/

UK Biobank: https://www.ukbiobank.ac.uk/register-apply/

23andMe: https://research.23andme.com/research-innovation-collaborations/

Brain Imaging Genetics: http://big.stats.ox.ac.uk

Code availability

Previously developed pipelines were used to produce the results for this study. No custom code was developed.

References

  1. Keyes, K. M., Platt, J., Kaufman, A. S. & McLaughlin, K. A. Association of fluid intelligence and psychiatric disorders in a population-representative sample of US adolescents. JAMA Psychiatry 74, 179–188 (2017).

    PubMed  PubMed Central  Google Scholar 

  2. McLaughlin, K. A., Costello, E. J., Leblanc, W., Sampson, N. A. & Kessler, R. C. Socioeconomic status and adolescent mental disorders. Am. J. Public Health 102, 1742–1750 (2012).

    PubMed  PubMed Central  Google Scholar 

  3. d’Errico, A. et al. Socioeconomic indicators in epidemiologic research: a practical example from the LIFEPATH study. PLoS ONE 12, e0178071 (2017).

    PubMed  PubMed Central  Google Scholar 

  4. 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).

    CAS  PubMed  Google Scholar 

  5. 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 

  6. Morris, T. T., Davies, N. M., Hemani, G. & Smith, G. D. Why are education, socioeconomic position and intelligence genetically correlated? Preprint at bioRxiv https://doi.org/10.1101/630426 (2019).

  7. Trampush, J. W. et al. GWAS meta-analysis reveals novel loci and genetic correlates for general cognitive function: a report from the COGENT consortium. Mol. Psychiatry 22, 336–345 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Marioni, R. E. et al. Molecular genetic contributions to socioeconomic status and intelligence. Intelligence 44, 26–32 (2014).

    PubMed  PubMed Central  Google Scholar 

  9. Hill, W. D. et al. Molecular genetic contributions to social deprivation and household income in UK biobank. Curr. Biol. 26, 3083–3089 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Anttila, V. et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).

    PubMed  Google Scholar 

  11. Zhang, Y., Qi, G., Park, J. H. & Chatterjee, N. Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits. Nat. Genet. 50, 1318–1326 (2018).

    CAS  PubMed  Google Scholar 

  12. Choi, S. W. & O’Reilly, P. F. PRSice-2: polygenic risk score software for biobank-scale data. Gigascience https://doi.org/10.1093/gigascience/giz082 (2019).

  13. Choi, S. W., Mak, T. S. & O’Reilly, P. F. Tutorial: a guide to performing polygenic risk score analyses. Nat. Protoc. 15, 2759–2772 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Avinun, R. Educational attainment polygenic score is associated with depressive symptoms via socioeconomic status: a gene–environment–trait correlation. Preprint at bioRxiv https://doi.org/10.1101/727552 (2019).

  15. Krapohl, E. & Plomin, R. Genetic link between family socioeconomic status and children’s educational achievement estimated from genome-wide SNPs. Mol. Psychiatry 21, 437–443 (2016).

    CAS  PubMed  Google Scholar 

  16. Richards, A. L. et al. The relationship between polygenic risk scores and cognition in schizophrenia. Schizophr. Bull. 46, 336–344 (2019).

    PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  19. Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40, 304–314 (2016).

    PubMed  PubMed Central  Google Scholar 

  20. Bowden, J. et al. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat. Med. 36, 1783–1802 (2017).

    PubMed  PubMed Central  Google Scholar 

  21. 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 

  22. Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

    CAS  PubMed  Google Scholar 

  23. Eroglu, C. et al. Gabapentin receptor alpha2delta-1 is a neuronal thrombospondin receptor responsible for excitatory CNS synaptogenesis. Cell 139, 380–392 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Vergult, S. et al. Genomic aberrations of the CACNA2D1 gene in three patients with epilepsy and intellectual disability. Eur. J. Hum. Genet. 23, 628–632 (2015).

    CAS  PubMed  Google Scholar 

  25. Battle, A., Brown, C. D., Engelhardt, B. E. & Montgomery, S. B. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    PubMed  Google Scholar 

  26. Crayton, J. W. & Meltzer, H. Y. Degeneration and regeneration of motor neurons in psychotic patients. Biol. Psychiatry 14, 803–819 (1979).

    CAS  PubMed  Google Scholar 

  27. Strassnig, M., Signorile, J., Gonzalez, C. & Harvey, P. D. Physical performance and disability in schizophrenia. Schizophr. Res. Cogn. 1, 112–121 (2014).

    CAS  PubMed  Google Scholar 

  28. Watanabe, K., Umicevic Mirkov, M., de Leeuw, C. A., van den Heuvel, M. P. & Posthuma, D. Genetic mapping of cell type specificity for complex traits. Nat. Commun. 10, 3222 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018).

    CAS  PubMed  Google Scholar 

  30. O’Connor, L. J. & Price, A. L. Distinguishing genetic correlation from causation across 52 diseases and complex traits. Nat. Genet. 50, 1728–1734 (2018).

    PubMed  PubMed Central  Google Scholar 

  31. 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).

    PubMed  Google Scholar 

  32. Duman, R. S., Sanacora, G. & Krystal, J. H. Altered connectivity in depression: GABA and glutamate neurotransmitter deficits and reversal by novel treatments. Neuron 102, 75–90 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Gerhard, D. M. et al. GABA interneurons are the cellular trigger for ketamine’s rapid antidepressant actions. J. Clin. Invest. 130, 1336–1349 (2019).

    Google Scholar 

  34. Andersen, A. M. et al. Polygenic scores for major depressive disorder and risk of alcohol dependence. JAMA Psychiatry 74, 1153–1160 (2017).

    PubMed  PubMed Central  Google Scholar 

  35. Burgess, S., Bowden, J., Fall, T., Ingelsson, E. & Thompson, S. G. Sensitivity analyses for robust causal inference from mendelian randomization analyses with multiple genetic variants. Epidemiology 28, 30–42 (2017).

    PubMed  Google Scholar 

  36. Hartwig, F. P., Davey Smith, G. & Bowden, J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J. Epidemiol. 46, 1985–1998 (2017).

    PubMed  PubMed Central  Google Scholar 

  37. Zhao, Q., Wang, J., Hemani, G., Bowden, J. & Small, D. S. Statistical inference in two-sample summary data Mendelian randomization using robust adjusted profile score. Ann. Statist. 48, 1742–1769 (2020).

    Google Scholar 

  38. Clarke, T. K. et al. Common polygenic risk for autism spectrum disorder (ASD) is associated with cognitive ability in the general population. Mol. Psychiatry 21, 419–425 (2016).

    PubMed  Google Scholar 

  39. Klein, M. et al. Genetic markers of ADHD-related variations in intracranial volume. Am. J. Psychiatry 176, 228–238 (2019).

    PubMed  PubMed Central  Google Scholar 

  40. Abu-Akel, A., Allison, C., Baron-Cohen, S. & Heinke, D. The distribution of autistic traits across the autism spectrum: evidence for discontinuous dimensional subpopulations underlying the autism continuum. Mol. Autism 10, 24 (2019).

    PubMed  PubMed Central  Google Scholar 

  41. Larsson, H., Anckarsater, H., Rastam, M., Chang, Z. & Lichtenstein, P. Childhood attention-deficit hyperactivity disorder as an extreme of a continuous trait: a quantitative genetic study of 8,500 twin pairs. J. Child Psychol. Psychiatry 53, 73–80 (2012).

    PubMed  Google Scholar 

  42. Gandal, M. J. et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science 359, 693–697 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Romer, A. L. et al. Structural alterations within cerebellar circuitry are associated with general liability for common mental disorders. Mol. Psychiatry 23, 1084–1090 (2018).

    CAS  PubMed  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  45. Ge, S. X., Jung, D. & Yao, R. ShinyGO: a graphical enrichment tool for animals and plants. Bioinformatics 36, 2628–2629 (2020).

    CAS  PubMed  Google Scholar 

  46. 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).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank the research participants and employees of 23andMe Inc for making this work possible. This study was supported by the Simons Foundation Autism Research Initiative (SFARI Explorer Award 534858 (R.P.)), the American Foundation for Suicide Prevention (YIG-1-109-16 (R.P.)), the National Institutes of Health (R21 DC018098 (R.P.), R21 DA047527 (R.P.), F32 MH122058 (F.R.W.) and R01 MH117646 (T.L.)) and the National Center for PTSD of the U.S. Department of Veterans Affairs. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

Authors

Contributions

F.R.W and R.P. conceived the study design; F.R.W. generated and analysed all data; F.R.W., G.A.P., T.L., J.H.K., J.G. and R.P. contributed to data interpretation; F.R.W., G.A.P. and R.P. contributed to data visualization and presentation; F.R.W. drafted the original manuscript; F.R.W., G.A.P., T.L., J.H.K., J.G. and R.P. critically evaluated and revised the manuscript.

Corresponding author

Correspondence to Renato Polimanti.

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Competing interests

J.H.K. reports compensation as the editor of Biological Psychiatry and also serves on the Scientific Advisory Boards for Bioasis Technologies, Inc., Biohaven Pharmaceuticals, BioXcel Therapeutics, Inc. (Clinical Advisory Board), Cadent Therapeutics (Clinical Advisory Board), PsychoGenics, Inc, Stanley Center for Psychiatric Research at the Broad Institute of MIT and Harvard, and the Lohocla Research Corporation. He owns stock in ArRETT Neuroscience, Inc., Biohaven Pharmaceuticals, Sage Pharmaceuticals and Spring Care, Inc. and stock options in Biohaven Pharmaceuticals Medical Sciences, BlackThorn Therapeutics, Inc. and Storm Biosciences, Inc. He is co-inventor on multiple patents as listed below: (1) Seibyl J. P., Krystal J. H., Charney D. S. Dopamine and noradrenergic reuptake inhibitors in treatment of schizophrenia. US patent 5,447,948 (1995); (2) Vladimir, C., Krystal, J. H., Sanacora, G. Glutamate modulating agents in the treatment of mental disorders, US patent 8,778,979 (2014); (3) Charney D., Krystal J. H., Manji H., Matthew S., Zarate C. Intranasal administration of ketamine to treat depression US patent application 14/197,767 (2014); (4) Zarate, C., Charney, D. S., Manji, H. K., Mathew, S. J., Krystal, J. H., Department of Veterans Affairs. Methods for treating suicidal ideation, US patent application no. 14/197,767 (2014); (5) Arias A., Petrakis I., Krystal J. H. Composition and methods to treat addiction. US patent application no. 61/973/961 (2014); (6) Chekroud, A., Gueorguieva, R., Krystal, J. H. Treatment selection for major depressive disorder (filing date 2016, USPTO docket number Y0087.70116US00). Provisional patent submission by Yale University: (7) Gihyun, Y., Petrakis I., Krystal, J. H. Compounds, compositions and methods for treating or preventing depression and other diseases. US provisional patent application no. 62/444,552 (filed 10 January 2017) by Yale University Office of Cooperative Research OCR 7088 US01: (8) Abdallah, C., Krystal, J. H., Duman, R., Sanacora, G. Combination therapy for treating or preventing depression or other mood diseases. US provisional patent application no. 047162-7177P1 (00754) filed on 20 August 2018 by Yale University Office of Cooperative Research OCR 7451 US01. J.G. is named as an inventor on PCT patent application 15/878,640 Genotype-guided dosing of opioid agonists, filed 24 January 2018. J.G. and R.P. are paid for their editorial work on the journal Complex Psychiatry. The other authors declare no competing interests.

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Wendt, F.R., Pathak, G.A., Lencz, T. et al. Multivariate genome-wide analysis of education, socioeconomic status and brain phenome. Nat Hum Behav 5, 482–496 (2021). https://doi.org/10.1038/s41562-020-00980-y

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