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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

Richard Karlsson LinnériD, Pietro BiroliiD, Edward L. KongiD, S. Fleur W. MeddensiD, Robbee WedowiD, Mark Alan Fontana, Maël P. LebretoniD, Abdel AbdellaouiiD, Anke R. HammerschlagiD, Michel Guillaume NivardiD, Aysu OkbayiD, Cornelius A. RietveldiD, Pascal N. TimsheliD, Stephen P. Tino, Maciej TrzaskowskiiD, Ronald de VlamingiD, Christian Lukas ZündiD, Yanchun BaoiD, Laura Buzdugan, Ann H. CapliniD, Chia‐Yen CheniD, Peter EibichiD, Pierre FontanillasiD, Juan R. GonzáleziD, Peter K. JoshiiD, , Jonathan P. BeauchampiD

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Abstract

Humans vary substantially in their willingness to take risks. In a combined sample of over one 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. We identified 611 approximately independent genetic loci associated with at least one of our phenotypes, including 124 with general risk tolerance. We report evidence of substantial shared genetic influences across general risk tolerance and risky behaviors: 72 of the 124 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is moderately to strongly genetically correlated ([Formula] to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near general-risk-tolerance-associated SNPs are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We find no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

Abstract summary

Claude Opus 5.5 · xhigh effort · 2 Nov 2026 · from the abstract · CC BY-NC-ND 4.0

GWAS in over one million individuals identify 611 loci across risk tolerance and risky behaviors, revealing shared genetic architecture and implicating glutamatergic and GABAergic neurotransmission.

Willingness to take risks varies widely across humans, and candidate genes have previously been hypothesized to underlie this variation. Large-scale genome-wide association analyses across general risk tolerance, adventurousness, and domain-specific risky behaviors map hundreds of loci and test prior candidate-gene hypotheses.

Key results

  1. ShownGenome-wide association analyses across risk tolerance, adventurousness, and risky behaviors in driving, drinking, smoking, and sexual domains identify hundreds of associated loci.

    611 approximately independent genetic loci associated with at least one phenotype, including 124 loci associated with general risk tolerance, in a combined sample of over one million individuals. · Abstract

  2. ShownGeneral risk tolerance shares substantial genetic architecture with specific risky behaviors.

    Among the 124 loci for general risk tolerance, 72 harbor a lead single-nucleotide polymorphism (SNP) from at least one of the other analyzed GWAS traits, and genetic correlations reach 0.50. · Abstract

  3. SuggestedBioinformatics analyses indicate high brain-tissue expression for genes proximal to general-risk-tolerance SNPs and implicate glutamatergic and GABAergic signaling, whereas prior candidate genes show no enrichment.

    Reported from bioinformatics analyses of genes near general-risk-tolerance SNPs and candidate-gene enrichment tests; specific p-values and effect sizes are not printed in the abstract. · Abstract

Methods, data and reproducibility

In a combined cohort exceeding one million people, genome-wide association studies (GWAS) were run for general risk tolerance, adventurousness, and domain-specific risky behaviors (driving, drinking, smoking, and sexual behavior), alongside genetic correlation and bioinformatics expression analyses.

  • Combined GWAS cohort profiled for general risk tolerance, adventurousness, and domain-specific risky behaviors (driving, drinking, smoking, and sexual domains) · human · n = over one million individuals
Methods & tools
GWAS
Reproducibility gaps
Abstract only; contributing biobanks/cohorts, summary statistics accessions, and specific bioinformatics software tools are not listed in the abstract.
Limitations (2)
  • The lower bound of the genetic correlation range is missing in the abstract text due to a '[Formula]' placeholder. · noted in this summary
  • Cohort composition, ancestry distribution, and specific bioinformatics tool names are not provided in the abstract. · noted in this summary

Read this if you study the genetic architecture of human behavioral traits or use GWAS summary statistics for neuropsychiatric and behavioral phenotypes.

Citations
82

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