Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Genetic underpinnings of risky behaviour relate to altered neuroanatomy

Nature Human Behaviour volume 5, pages 787–794 (2021)Cite this article

Subjects

Abstract

Previous research points to the heritability of risk-taking behaviour. However, evidence on how genetic dispositions are translated into risky behaviour is scarce. Here, we report a genetically informed neuroimaging study of real-world risky behaviour across the domains of drinking, smoking, driving and sexual behaviour in a European sample from the UK Biobank (N = 12,675). We find negative associations between risky behaviour and grey-matter volume in distinct brain regions, including amygdala, ventral striatum, hypothalamus and dorsolateral prefrontal cortex (dlPFC). These effects are replicated in an independent sample recruited from the same population (N = 13,004). Polygenic risk scores for risky behaviour, derived from a genome-wide association study in an independent sample (N = 297,025), are inversely associated with grey-matter volume in dlPFC, putamen and hypothalamus. This relation mediates roughly 2.2% of the association between genes and behaviour. Our results highlight distinct heritable neuroanatomical features as manifestations of the genetic propensity for risk taking.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Main sample characteristics (N = 12,675).
Fig. 2: Association between risky behaviour and IDPs of GMV.
Fig. 3: Voxel-level GMV associated with risky behaviour in the replication sample (N = 13,004).
Fig. 4: Conjunction between the GMV differences associated with risky behaviour identified in the current study and the results of a meta-analysis of 101 fMRI studies, on the basis of the keyword ‘risky’.
Fig. 5: Association of PRS for risky behaviour and GMV.

Data availability

Data and materials are available via UKB at http://www.ukbiobank.ac.uk/.

Code availability

The analysis code used in this study is publicly available at https://osf.io/qkp4g/.

References

  1. Knight, F. H. Risk, Uncertainty, and Profit (Houghton Mifflin, 1921).

  2. Eckel, C. C. & Füllbrunn, S. C. Thar SHE blows? gender, competition, and bubbles in experimental asset markets. Am. Economic Rev. 105, 906–920 (2015).

    Article  Google Scholar 

  3. Incidence, Prevalence, and Cost of Sexually Transmitted Infections in the United States (Centers for Disease Control and Prevention, 2013); https://www.cdc.gov/std/stats/sti-estimates-fact-sheet-feb-2013.pdf

  4. Sacks, J. J., Gonzales, K. R., Bouchery, E. E., Tomedi, L. E. & Brewer, R. D. 2010 national and state costs of excessive alcohol consumption. Am. J. Prev. Med. 49, e73–e79 (2015).

  5. Blincoe, L., Miller, T. R., Zaloshnja, E. & Lawrence, B. A. The Economic and Societal Impact of Motor Vehicle Crashes, 2010 (revised) (Transportation Research Board, 2015); https://trid.trb.org/view/1311862

  6. The Health Consequences of Smoking: 50 Years of Progress. A Report of the Surgeon General. Atlanta, GA (US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2014).

  7. Karlsson Linnér, R. 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).

    Article  PubMed  Google Scholar 

  8. Elliott, L. T. et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 562, 210–216 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Thompson, P. M. et al. Genetic influences on brain structure. Nat. Neurosci. 4, 1253–1258 (2001).

    Article  CAS  PubMed  Google Scholar 

  10. Grubb, M. A., Tymula, A., Gilaie-Dotan, S., Glimcher, P. W. & Levy, I. Neuroanatomy accounts for age-related changes in risk preferences. Nat. Commun. 7, 13822 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Jung, W. H., Lee, S., Lerman, C. & Kable, J. W. Amygdala functional and structural connectivity predicts individual risk tolerance. Neuron 98, 394–404.e4 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Nasiriavanaki, Z. et al. Prediction of individual differences in risky behavior in young adults via variations in local brain structure. Front. Neurosci. 9, 359 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Button, K. S. et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14, 365–376 (2013).

    Article  CAS  PubMed  Google Scholar 

  14. Marek, S. et al. Towards reproducible brain-wide association studies. Preprint at bioRxiv https://doi.org/10.1101/2020.08.21.257758 (2020).

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

    Article  Google Scholar 

  16. Cardon, L. R. & Palmer, L. J. Population stratification and spurious allelic association. Lancet 361, 598–604 (2003).

    Article  PubMed  Google Scholar 

  17. Nave, G., Jung, W. H., Karlsson Linnér, R., Kable, J. W. & Koellinger, P. D. Are bigger brains smarter? Evidence from a large-scale preregistered study. Psychol. Sci. 30, 43–54 (2019).

    Article  PubMed  Google Scholar 

  18. Romer, D. Adolescent risk taking, impulsivity, and brain development: implications for prevention. Dev. Psychobiol. 52, 263–276 (2010).

    PubMed  PubMed Central  Google Scholar 

  19. Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19, 1523–1536 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Harper, C. The neurotoxicity of alcohol. Hum. Exp. Toxicol. 26, 251–257 (2007).

    Article  CAS  PubMed  Google Scholar 

  22. Daviet, R. et al. Multimodal brain imaging study of 36,678 participants reveals adverse effects of moderate drinking. Preprint at bioRxiv https://doi.org/10.1101/2020.03.27.011791 (2020).

  23. Kranzler, H. R. et al. Topiramate treatment for heavy drinkers: moderation by a GRIK1 polymorphism. Am. J. Psychiatry 171, 445–452 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Ashburner, J. & Friston, K. J. Voxel-based morphometry—the methods. Neuroimage 11, 805–821 (2000).

    Article  CAS  PubMed  Google Scholar 

  25. Botvinik-Nezer, R. et al. Variability in the analysis of a single neuroimaging dataset by many teams. Nature 582, 84–88 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Alfaro-Almagro, F. et al. Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166, 400–424 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  27. 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 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Masouleh, S. K., Eickhoff, S. B., Hoffstaedter, F., Genon, S. & The Alzheimer’s Disease NeuroImaging Initiative. Empirical examination of the replicability of associations between brain structure and psychological variables. eLife 8, e43464 (2019).

    Article  Google Scholar 

  30. Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C. & Wager, T. D. Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 8, 665–670 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Mohr, P. N. C., Biele, G. & Heekeren, H. R. Neural processing of risk. J. Neurosci. 30, 6613–6619 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Wu, C. C., Sacchet, M. D. & Knutson, B. Toward an affective neuroscience account of financial risk taking. Front. Neurosci. 6, 159 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Schonberg, T., Fox, C. R. & Poldrack, R. A. Mind the gap: bridging economic and naturalistic risk-taking with cognitive neuroscience. Trends Cogn. Sci. 15, 11–19 (2011).

    Article  PubMed  Google Scholar 

  34. Kable, J. W. & Levy, I. Neural markers of individual differences in decision-making. Curr. Opin. Behav. Sci. 5, 100–107 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Chang, L. J., Yarkoni, T., Khaw, M. W. & Sanfey, A. G. Decoding the role of the insula in human cognition: functional parcellation and large-scale reverse inference. Cereb. Cortex 23, 739–749 (2013).

    Article  PubMed  Google Scholar 

  36. Evans, B. E., Greaves-Lord, K., Euser, A. S., Franken, I. H. A. & Huizink, A. C. The relation between hypothalamic-pituitary-adrenal (HPA) axis activity and age of onset of alcohol use. Addiction 107, 312–322 (2012).

    Article  PubMed  Google Scholar 

  37. Kreek, M. J., Nielsen, D. A., Butelman, E. R. & LaForge, K. S. Genetic influences on impulsivity, risk taking, stress responsivity and vulnerability to drug abuse and addiction. Nat. Neurosci. 8, 1450–1457 (2005).

    Article  CAS  Google Scholar 

  38. O’Doherty, J. et al. Dissociable roles of ventral and dorsal striatum in instrumental conditioning. Science 304, 452–454 (2004).

    Article  PubMed  Google Scholar 

  39. Dosenbach, N. U. F., Fair, D. A., Cohen, A. L., Schlaggar, B. L. & Petersen, S. E. A dual-networks architecture of top-down control. Trends Cogn. Sci. 12, 99–105 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Ivanov, I., Schulz, K. P., London, E. D. & Newcorn, J. H. Inhibitory control deficits in childhood and risk for substance use disorders: a review. Am. J. Drug Alcohol Abus. 34, 239–258 (2008).

    Article  Google Scholar 

  41. Heilman, R. M., Crişan, L. G., Houser, D., Miclea, M. & Miu, A. C. Emotion regulation and decision making under risk and uncertainty. Emotion 10, 257–265 (2010).

    Article  PubMed  Google Scholar 

  42. Tobler, P. N., Christopoulos, G. I., O’Doherty, J. P., Dolan, R. J. & Schultz, W. Risk-dependent reward value signal in human prefrontal cortex. Proc. Natl Acad. Sci. USA 106, 7185–7190 (2009).

    Article  CAS  PubMed  Google Scholar 

  43. Huizink, A. C., Ferdinand, R. F., Ormel, J. & Verhulst, F. C. Hypothalamic-pituitary-adrenal axis activity and early onset of cannabis use. Addiction 101, 1581–1588 (2006).

    Article  PubMed  Google Scholar 

  44. Margittai, Z. et al. Combined effects of glucocorticoid and noradrenergic activity on loss aversion. Neuropsychopharmacology 43, 334–341 (2018).

    Article  CAS  PubMed  Google Scholar 

  45. Grotzinger, A. D. et al. Hair and salivary testosterone, hair cortisol, and externalizing behaviors in adolescents. Psychol. Sci. 29, 688–699 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Buckner, R. L. The cerebellum and cognitive function: 25 years of insight from anatomy and neuroimaging. Neuron 80, 807–815 (2013).

    Article  CAS  PubMed  Google Scholar 

  47. Mechelli, A., Price, C. J., Friston, K. J. & Ashburner, J. Voxel-based morphometry of the human brain: methods and applications. Curr. Med. Imaging Rev. 1, 105–113 (2005).

    Article  Google Scholar 

  48. Knoch, D. et al. Disruption of right prefrontal cortex by low-frequency repetitive transcranial magnetic stimulation induces risk-taking behavior. J. Neurosci. 26, 6469–6472 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. BrainSpan Atlas of the Developing Human Brain (Allen Institute for Brain Science, 2015); http://www.brainspan.org/

  50. Loh, P. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Camerer, C. F. et al. Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015. Nat. Hum. Behav. 2, 637–644 (2018).

    Article  Google Scholar 

  52. Dickie, D. A. et al. Permutation and parametric tests for effect sizes in voxel-based morphometry of gray matter volume in brain structural MRI. Magn. Reson. Imaging 33, 1299–1305 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This research was carried out under the auspices of the Brain Imaging and Genetics in Behavioural Research (https://big-bear-research.org/) consortium. We thank N.C. Furtner for helpful comments and D. Manfredi for research assistance. The research was conducted using UKB resources under application no. 40830. The study was supported by funding from an National Science Foundation Early Career Development Program grant (no. 1942917) and The Wharton School Dean’s Research fund to G.N., a European Research Council Consolidator grant to P.D.K. (no. 647648 EdGe), and a European Research Council Consolidator grant (no. 725355 BRAINCODES) and a Swiss National Science Foundation grant (no. 100019L_173248) to C.C.R . R.R.W. was financially supported by NIAAA K23 grant (no. K23 AA023894) and H.R.K. was supported by National Institute of Drug Abuse grant no. P30 DA046345. G.N. thanks C. and R. de la Cruz for ongoing support. The work was carried out on the Dutch national e-infrastructure with the support of the SURF Cooperative. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Data can be accessed via the UK Biobank, and data analysis scripts are available on the Open Science Framework (https://osf.io/qkp4g/).

Author information

Authors and Affiliations

  1. Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland

    Gökhan Aydogan, Todd A. Hare & Christian C. Ruff

  2. Marketing Department, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA

    Remi Daviet, Joseph W. Kable & Gideon Nave

  3. Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands

    Richard Karlsson Linnér & Philipp D. Koellinger

  4. Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA

    Joseph W. Kable

  5. Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Henry R. Kranzler & Reagan R. Wetherill

  6. Veterans Integrated Service Network 4, Mental Illness Research, Education and Clinical Center, Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA

    Henry R. Kranzler

  7. La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI, USA

    Philipp D. Koellinger

Authors
  1. Gökhan Aydogan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Remi Daviet
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Richard Karlsson Linnér
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Todd A. Hare
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Joseph W. Kable
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Henry R. Kranzler
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Reagan R. Wetherill
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Christian C. Ruff
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Philipp D. Koellinger
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Gideon Nave
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

BIG BEAR Consortium

Contributions

G.A., R.D., J.W.K., P.D.K. and G.N. designed the research plan. G.N., P.D.K. and C.C.R. oversaw the study. G.A., R.D. and R.K.L. analysed the data with critical input from P.D.K., G.N. and C.C.R. G.A., G.N. and P.D.K. wrote the paper and Supplementary Materials. T.A.H., H.R.K. and R.R.W. contributed to and critically reviewed the manuscript.

Corresponding author

Correspondence to Gideon Nave.

Ethics declarations

Competing interests

H.R.K. is a member of an advisory board for Dicerna Pharmaceuticals; a member of the American Society of Clinical Psychopharmacology’s Alcohol Clinical Trials Initiative, which was sponsored in the past three years by AbbVie, Alkermes, Amygdala Neurosciences, Arbor Pharmaceuticals, Ethypharm, Indivior, Lilly, Lundbeck, Otsuka and Pfizer; and is named as an inventor on PCT patent application no. 15/878,640, entitled ‘Genotype-guided dosing of opioid agonists’ and filed 24 January 2018. All other authors declare no competing interests.

Additional information

Peer review information Nature Human Behaviour thanks Michelle Luciano, Agnieszka Tymula and the other, anonymous, reviewers for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Marike Schiffer.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1

Bivariate correlations between variables used in the main study sample (N = 12,675).

Extended Data Fig. 2

Empirical distributions of variables in the replication sample (N = 13,004).

Extended Data Fig. 3 Effect sizes (standardized betas) of associations between risky behaviour and grey matter volume (GMV) in voxel clusters showing significant associations at P < .01 (FWE-corrected) (N = 12,675).

Coordinates of peak association for each cluster are reported in parentheses (in mm). Standard errors denote uncorrected 95% confidence intervals. See Extended Data Table 4 for further details.

Extended Data Fig. 4 Effect sizes (standardized betas) of association between risky behaviour and IDPs of grey matter volume (GMV) showing significant associations at P<0.01 level (FWE-corrected) (N = 12,675).

Standard errors denote uncorrected 95% confidence intervals.

Extended Data Fig. 5 Associations (p-values) between risky behaviour and 148 ROI-level imaging-derived phenotypes (IDPs) of grey matter volume (GMV), controlling for cognitive and socioeconomic outcomes (N = 11,864).

Control variables include education years, fluid IQ, zip-code level social deprivation, household income, number of household members, birth location, and all standard controls.

Extended Data Fig. 6

Associations (p-values) between risky behaviour and 148 ROI-level imaging-derived phenotypes (IDPs) of grey matter volume (GMV), controlling for current drinking levels (binned in deciles) and smoking levels (binned in 3 categories) in addition to all standard controls (N = 12,675).

Extended Data Fig. 7 Meta-analysis of functional MRI studies of risky behaviours, provided by Neurosynth (N = 4,717 participants and K = 101 studies).

Conjunction with areas showing negative GMV association with risky behaviour (including thalamus, vmPFC, amygdala and dlPFC) is marked in magenta (see Supplementary Table 1). Additional meta-analytic functional activation areas are marked in red.

Extended Data Fig. 8 Mediation analysis of the association between PRS and risky behaviour with GMV in dlPFC, putamen and hypothalamus (N = 12,675).

The sum of all GMV differences in right dlPFC, putamen and hypothalamus (based on the activation masks from Fig 5A) mediated ~2.07% of the association between the PRS and risky behaviour. Arrows depict the direction of the structural equation modelling and do not imply causality.

Supplementary information

Supplementary Information

Supplementary Methods, Supplementary Discussion, Supplementary Tables 1–9, Supplementary References and Supplementary Notes.

Reporting Summary

Peer Review Information

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aydogan, G., Daviet, R., Karlsson Linnér, R. et al. Genetic underpinnings of risky behaviour relate to altered neuroanatomy. Nat Hum Behav 5, 787–794 (2021). https://doi.org/10.1038/s41562-020-01027-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41562-020-01027-y

This article is cited by

Search

Advanced 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