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Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences

Abstract

Humans vary substantially in their willingness to take risks. In a combined sample of over 1 million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. Across all GWAS, we identified hundreds of associated loci, including 99 loci associated with general risk tolerance. We report evidence of substantial shared genetic influences across risk tolerance and the risky behaviors: 46 of the 99 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is genetically correlated (\(\left| {\hat r_{\mathrm{g}}} \right|\) ~ 0.25 to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near SNPs associated with general risk tolerance are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We found no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

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Fig. 1: Manhattan plots.
Fig. 2: Genetic correlations with general risk tolerance.
Fig. 3: Results from selected biological analyses.

Data availability

GWAS summary statistics can be downloaded from http://www.thessgac.org/data. SNP-level summary statistics from analyses based entirely or in part on 23andMe data can only be reported for up to 10,000 SNPs. For general risk tolerance, we provide association results for all SNPs that passed quality-control filters in a GWAS meta-analysis of general risk tolerance that excludes the research participants from 23andMe; we also provide association results from the complete GWAS (which includes data from 23andMe) for all lead SNPs identified in our discovery GWAS and MTAG analysis of general risk tolerance and for the 4,000 most significant SNPs in the meta-analysis of the discovery and replication GWAS of risk tolerance. For adventurousness, we provide association results from the complete GWAS (which includes only data from 23andMe) for all lead SNPs and for the next 4,000 most significant SNPs. For automobile speeding propensity, drinks per week, ever smoker, number of sexual partners, and the first PC of the four risky behaviors, we provide association results from the complete GWAS for all SNPs that passed quality-control filters. Contact information for the cohorts included in this paper can be found in the Supplementary Note.

References

  1. Dohmen, T. et al. Individual risk attitudes: measurement, determinants, and behavioral consequences. J. Eur. Econ. Assoc. 9, 522–550 (2011).

    Article  Google Scholar 

  2. Falk, A., et al. The Nature and Predictive Power of Preferences: Global Evidence (IZA Institute of Labor Economics, 2015).

  3. Beauchamp, J. P., Cesarini, D. & Johannesson, M. The psychometric and empirical properties of measures of risk preferences. J. Risk Uncertain. 54, 203–237 (2017).

    Article  Google Scholar 

  4. Cesarini, D., Dawes, C. T., Johannesson, M., Lichtenstein, P. & Wallace, B. Genetic variation in preferences for giving and risk taking. Q. J. Econ. 124, 809–842 (2009).

    Article  Google Scholar 

  5. Harden, K. P. et al. Beyond dual systems: a genetically-informed, latent factor model of behavioral and self-report measures related to adolescent risk-taking. Dev. Cogn. Neurosci. 25, 221–234 (2017).

    Article  Google Scholar 

  6. Hewitt, J. K. Editorial policy on candidate gene association and candidate gene-by-environment interaction studies of complex traits. Behav. Genet. 42, 1–2 (2012).

    Article  Google Scholar 

  7. Day, F. R. et al. Physical and neurobehavioral determinants of reproductive onset and success. Nat. Genet. 48, 617–623 (2016).

    Article  CAS  Google Scholar 

  8. Strawbridge, R. J. et al. Genome-wide analysis of self-reported risk-taking behaviour and cross-disorder genetic correlations in the UK Biobank cohort. Transl. Psychiatry 8, 1–11 (2018).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  10. 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  Google Scholar 

  11. 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  CAS  Google Scholar 

  12. Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).

    Article  CAS  Google Scholar 

  13. Byrnes, J. P., Miller, D. C. & Schafer, W. D. Gender differences in risk taking: a meta-analysis. Psychol. Bull. 125, 367–383 (1999).

    Article  Google Scholar 

  14. Croson, R. & Gneezy, U. Gender differences in preferences. J. Econ. Lit. 47, 448–474 (2009).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  16. Shi, H., Kichaev, G. & Pasaniuc, B. Contrasting the genetic architecture of 30 complex traits from summary association data. Am. J. Hum. Genet. 99, 139–153 (2016).

    Article  CAS  Google Scholar 

  17. Price, A. L. et al. Long-range LD can confound genome scans in admixed populations. Am. J. Hum. Genet. 83, 132–139 (2008).

    Article  CAS  Google Scholar 

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

    CAS  Google Scholar 

  19. Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).

    Article  CAS  Google Scholar 

  20. Einav, B. L., Finkelstein, A., Pascu, I. & Cullen, M. R. How general are risk preferences? Choices under uncertainty in different domains. Am. Econ. Rev. 102, 2606–2638 (2016).

    Article  Google Scholar 

  21. Frey, R., Pedroni, A., Mata, R., Rieskamp, J. & Hertwig, R. Risk preference shares the psychometric structure of major psychological traits. Sci. Adv. 3, e1701381 (2017).

    Article  Google Scholar 

  22. Weber, E. U., Blais, A. E. & Betz, N. E. A domain-specific risk-attitude scale: measuring risk perceptions and risk behaviors. J. Behav. Decis. Mak. 15, 263–290 (2002).

    Article  Google Scholar 

  23. Hanoch, Y., Johnson, J. G. & Wilke, A. Domain specificity in experimental measures and participant recruitment: an application to risk-taking behavior. Psychol. Sci. 17, 300–304 (2006).

    Article  Google Scholar 

  24. Stringer, S. et al. Genome-wide association study of lifetime cannabis use based on a large meta-analytic sample of 32,330 subjects from the International Cannabis Consortium. Transl. Psychiatry 6, e769 (2016).

    Article  CAS  Google Scholar 

  25. Becker, A., Deckers, T., Dohmen, T., Falk, A. & Kosse, F. The relationship between economic preferences and psychological personality measures. Annu. Rev. Econ. 4, 453–478 (2012).

    Article  Google Scholar 

  26. Krueger, R. F. et al. Etiologic connections among substance dependence, antisocial behavior and personality: modeling the externalizing spectrum. J. Abnorm. Psychol. 111, 411–424 (2002).

    Article  Google Scholar 

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

    Article  Google Scholar 

  28. Daetwyler, H. D., Villanueva, B. & Woolliams, J. A. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS ONE 3, e3395 (2008).

    Article  Google Scholar 

  29. de Vlaming, R. et al. Meta-GWAS Accuracy and Power (MetaGAP) calculator shows that hiding heritability is partially due to imperfect genetic correlations across studies. PLoS Genet. 13, e1006495 (2017).

    Article  Google Scholar 

  30. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, 1–19 (2015).

    Article  Google Scholar 

  31. Fehrmann, R. S. N. et al. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat. Genet. 47, 115–125 (2015).

    Article  CAS  Google Scholar 

  32. Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).

    Article  CAS  Google Scholar 

  33. Petroff, O. A. C. GABA and glutamate in the human brain. Neurosci. 8, 562–573 (2002).

    CAS  Google Scholar 

  34. Lee, J. et al. Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment. Nat. Genet. 50, 1112–1121 (2018).

    Article  CAS  Google Scholar 

  35. Ripke, S. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

    Article  CAS  Google Scholar 

  36. 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  Google Scholar 

  37. Haber, S. N. & Knutson, B. The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology 35, 4–26 (2010).

    Article  Google Scholar 

  38. Tobler, P. N. & Weber, E. U. in Neuroeconomics 149–172 (Elsevier, Amsterdam, 2014).

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

    Article  CAS  Google Scholar 

  40. Sahm, C. R. How much does risk tolerance change? Q. J. Finance 2, 1250020 (2012).

    Article  Google Scholar 

  41. Malmendier, U. & Nagel, S. Depression babies: do macroeconomic experiences affect risk taking? Q. J. Econ. 126, 373–416 (2011).

    Article  Google Scholar 

  42. Frey, B. J. & Dueck, D. Clustering by passing messages between data points. Science 315, 972–976 (2007).

  43. Furberg, H. et al. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. 42, 441–447 (2010).

    Article  CAS  Google Scholar 

  44. Winkler, T. W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 9, 1192–1212 (2014).

    Article  Google Scholar 

  45. Ripke, S. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  47. Shi, H., Kichaev, G. & Pasaniuc, B. Contrasting the genetic architecture of 30 complex traits from summary association data. Am. J. Hum. Genet. 99, 139–153 (2016).

    Article  CAS  Google Scholar 

  48. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    Article  CAS  Google Scholar 

  49. Finucane, H. K. et al. Partitioning heritability by functional category using GWAS summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    Article  CAS  Google Scholar 

  50. Demontis, D. et al. Discovery of the first genome-wide significant risk loci for ADHD. Preprint at https://doi.org/10.1101/145581 (2017).

  51. Stringer, S. et al. Genome-wide association study of lifetime cannabis use based on a large meta-analytic sample of 32 330 subjects from the International Cannabis Consortium. Transl. Psychiatry 6, e769 (2016).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  53. Dudbridge, F. Power and predictive accuracy of polygenic risk scores. PLoS Genet. 9, e1003348 (2013).

    Article  CAS  Google Scholar 

  54. Purcell, S. M. et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009).

    Article  CAS  Google Scholar 

  55. Buchanan, C. C., Torstenson, E. S., Bush, W. S. & Ritchie, M. D. A comparison of cataloged variation between International HapMap Consortium and 1000 Genomes Project data. J. Am. Med. Informatics Assoc. 19, 289–294 (2012).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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Acknowledgements

This research was carried out under the auspices of the Social Science Genetic Association Consortium. The research was also conducted using the UK Biobank Resource under application number 11425. The study was supported by funding from the Ragnar Söderberg Foundation (E9/11 and E42/15); the Swedish Research Council (421-2013-1061); the Jan Wallander and Tom Hedelius Foundation; an ERC Consolidator Grant to Philipp Koellinger (647648 EdGe); the Pershing Square Fund of the Foundations of Human Behavior; the Open Philanthropy Project; the National Institute on Aging, National Institutes of Health through grants P01-AG005842, P01-AG005842-20S2, P30-AG012810, and T32-AG000186-23 to the National Bureau of Economic Research and R01-AG042568-02 to the University of Southern California; the government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-152); and the Social Sciences and Humanities Research Council of Canada. We thank the International Cannabis Consortium, the eQTLgen Consortium, and the Psychiatric Genomics Consortium for sharing summary statistics from the GWAS of lifetime cannabis use, eQTL summary statistics, and summary statistics from the GWAS of ADHD, respectively. A full list of acknowledgments is provided in the Supplementary Note.

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J.P.B., P.D.K., and D.J.B. designed and oversaw the study. J.P.B. closely supervised the analyses and led the writing of the manuscript. R.K.L. was the lead analyst, responsible for quality control, the meta-analyses, summarizing the overlap across the results of the various GWAS, and the SNP heritability analyses. E.K. conducted the population stratification, replication, and proxy-phenotype analyses. R.W. led the genetic correlation analyses, and M.A.F. contributed to those analyses. P.B. led the polygenic score prediction analyses, and R.K.L., E.K., R.W., A.A., R.d.V., M.A.F., and M.G.N. contributed to those analyses. P.B. and C.L.Z. conducted the MTAG analyses. The bioinformatics analyses were led by S.F.W.M. Analysts who assisted S.F.W.M. include J.G. and M.T. (SMR), A.R.H. (MAGMA), G.A.M. (Gene Network), and P.N.T. (DEPICT). M.L. conducted the review of the literature attempting to link risk tolerance to biological pathways. R.K.L. prepared the majority of figures; E.K. and S.P.T. also prepared some figures. A.O., C.A.R., and S.P.T. helped with several additional analyses. D.C., J.G., and J.J.L. provided helpful advice and feedback on various aspects of the study design. All authors contributed to and critically reviewed the manuscript. P.B., R.K.L., E.K., S.F.W.M., and R.W. made especially major contributions to the writing and editing; these authors contributed equally, and lead authors 2–5 are listed aphabetically in the author list.

Corresponding authors

Correspondence to Richard Karlsson Linnér or Jonathan P. Beauchamp.

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Competing interests

A. Auton, P.F., D.A.H., and A.K. are employees of 23andMe. R.C.K., in the past three years, received support for his epidemiological studies from Sanofi Aventis; was a consultant for Johnson & Johnson Wellness and Prevention, Sage Pharmaceuticals, Shire, and Takeda; and served on an advisory board for the Johnson & Johnson Services Inc. Lake Nona Life Project. R.C.K. is a co-owner of DataStat Inc., a market research firm that carries out healthcare research. J.M. is a principal in BEAM Diagnostics Inc. The authors declare no other competing interests.

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Karlsson Linnér, R., Biroli, P., Kong, E. et al. Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nat Genet 51, 245–257 (2019). https://doi.org/10.1038/s41588-018-0309-3

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