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Genetic underpinnings of risky behaviour relate to altered neuroanatomy


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.

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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

Code availability

The analysis code used in this study is publicly available at


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This research was carried out under the auspices of the Brain Imaging and Genetics in Behavioural Research ( 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 (

Author information

Authors and Affiliations




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.

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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

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

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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).

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