Demographic characteristics are presented in Table S1. Risk takers were younger, more often men, more often current or ever-smokers, and more likely to report mood instability, a history of addiction or a history of mood disorders13. They were also more likely to have a university degree than controls13. As with our previous report13, test–retest reliability was 84.4% (inconsistent 13.2%, missing 2.4%, n = 14,551).

The risk-taking GWAS of white British participants identified 1162 genome-wide significant SNPs at 10 loci (Fig. 2a and Table S3), including the previously reported CADM2 and HLA loci13.

Fig. 2: Genome-wide associations with risk-taking behaviour. Manhattan plots of association with risk-taking behaviour (inset QQ plot) for a all White British individuals, b White British men and c White British women Full size image

Association of two previously reported loci with self-reported risk taking

The CADM2 locus on Chr3 (85 Mb) contained 812 GWAS-significant SNPs (Fig. 3a and Table S3), including a novel lead SNP (rs542809491) and the previously reported lead SNP for risk taking, rs1308453113. Conditional analysis including the previous lead SNP (rs13084531) as a covariate had limited impact on the effect size of the new lead SNP (rs542809491) and did not remove the significance of the association (Table S4). Including rs542809491 as a covariate rendered the previous lead SNP nonsignificant. Indeed, after conditioning on rs542809491, no SNPs in this locus reached even suggestive association (p < 1 × 10−5), indicating that this locus contains only one signal.

Fig. 3: Regional plots of known loci: a CADM2, b, c extended HLA region; novel loci d AKT3, e KHK, f SOX2, g FOXP2, h CYP17B1, i CASP12, j C15orf59, k NFAT5; and sex-specific loci l SOX5 and m Chr10 gene desert Full size image

On Chr6, significant SNPs were identified at 27 Mb and at 29 Mb (Figs. 3b, c and Table S3). As these fall within the extended HLA region, known to have extensive linkage disequilibrium (LD), conditional analysis was conducted on the two sets of SNPs together. The previous lead SNP in this region failed to meet the suggestive level of significance here (rs9379971, p = 4.94 × 10−5). Conditional analysis using this SNP increased the p of all SNPs in the region somewhat whereas conditioning on either the 27 Mb lead SNP (rs188973463) or the 29 Mb lead SNP (rs566858049) attenuated all associations (Table S5). Thus, this locus also appears to contain only one signal (index SNP rs188973463).

Eight novel loci associated with self-reported risk taking were identified

Eight novel risk-taking-associated loci were identified (Table S3 and Figs. 3d–k). Conditional analyses demonstrated that only the Chr3 (181 Mb) locus was suggestive of a second signal (Table S6). To aid data mining, rs727644 (instead of 7:114156758 _GT_G) and rs10895735 (instead of 11:104700736 _ACTTCAC_A) were used as the lead SNPs of the loci on Chr7 and Chr11, respectively, based on minor allele frequency (MAF) similarity and nonsignificance in the conditional analyses.

Two sex-specific loci were identified

Compared with the previous GWAS of risk-taking behaviour, the cohort size was more than doubled, allowing for well-powered sex-specific analyses. The characteristics of the sex-specific samples were comparable with the sex-combined samples (Table S7). In men, two GWAS-significant loci were identified (Fig. 2b, Table S3). The CADM2 locus was the same as was identified in the sex-combined analysis, albeit 100 kb away, whereas the Chr12 locus (Fig. 3l) was unique to men and did not reach even suggestive significance (p < 1 × 10−5) in the sex-combined analysis. The women-only analysis identified five loci (Fig. 2c and Table S3). The lead SNPs at the CADM2 and CYP7B1 loci were the same as for the sex-combined analysis. The lead SNPs for the Chr1 (AKT3), Chr15 (C15orf59) and Chr10 locus (specific to women) all reached suggestive evidence of association in the sex-combined analysis. The conditional analysis demonstrated that the women-only lead SNP of the C15orf59 locus represented the same signal as the sex-combined analysis (conditional p = 0.2234). However, for the AKT3 signal the results were inconclusive (conditional p = 3.5 × 10−4). The women-specific Chr10 locus lies within a gene desert (no coding genes within a flanking region of 500 kb up or downstream of the lead SNP) (Fig. 3m).

Genetic overlap with psychiatric, behavioural and cognitive traits

LDSR demonstrated significant genetic overlap between self-reported risk taking and ADHD, SCZ, BD, MDD, PTSD, smoking, alcohol consumption and cannabis use, as well as with IQ (fluid intelligence) and BMI (Table 1). This is consistent with our previous report13.

Table 1 Genetic correlations of self-reported risk taking with psychiatric disorders and relevant other traits Full size table

Consistency of associations in other ethnicities

The demographic characteristics of non-British White, South Asian, African-Caribbean and mixed ethnicities are presented in Table S8. Overall, when assessing consistency of effects across ethnicities, self-reported risk takers were more often men and more likely to be smokers and more often reported mood instability, history of addiction and mood disorders. The effect of the lead SNPs on risk taking in these ethnicities are presented in Table S9. In white non-British individuals, the CADM2 (rs542809491), FOXP2 (rs727644) and CYP7B1 (rs189335278) loci demonstrated nominal significance with risk-taking behaviour. In South Asians, the SOX2 (rs9841382) and FOXP2 (rs727644) loci demonstrated nominal significance. No evidence of effects was observed in African-Caribbean or mixed ethnicities. Meta-analysis of all ethnicities demonstrated GWAS significance (p < 5 × 10−8) for 8 of 11 loci (Table 2). Of these loci, six had low heterogeneity (I2 was < 25%) and two had moderate heterogeneity (I2 was 25% < 50%), consistent with failure to detect effects in the separate ethnicities possibly being due to sample size rather than lack of true effects.

Table 2 Trans-ethnic meta-analysis of lead SNPs Full size table

Risk-taking PRS and brain imaging phenotypes

Multiple strategies were employed to explore the impact of risk-taking SNPs on brain biology. One was to examine whether the genetic variants influenced the structure of brain regions previously implicated in risk-taking behaviours. Results of a secondary GWAS excluding the MRI subset were consistent with those for the discovery GWAS (Figure S2). Demographic characteristics of the MRI subset (Fig. 1) were generally comparable with the full cohort (Table S10), although there was enrichment for having a university degree and higher affluence.

Each SNP had only a small effect, therefore we also assessed the total genetic burden of all risk-taking loci using PRS. As it is likely that many variants (not only GWAS-significant SNPs) have effects on the anatomical ROIs, a variety of different p-value thresholds were employed31. Comparing the top versus bottom PRS quintile demonstrated that at some p-value thresholds, higher PRS was associated with lower volume of grey matter in the middle frontal gyrus and insular cortex (Table S11), but not with lower total grey matter volume or greater ventricular cerebrospinal fluid volume (both head size-normalised, Table S12). In addition, higher PRSs were associated with greater mean diffusivity (reflecting poorer white matter integrity, Table S13), but not with fractional anisotropy (FA). These findings were echoed by tract-specific analyses, where higher PRS was associated with greater MD in 9 out of 15 tracts (Table S14) but not with FA in any single tract (Table S15). Associations between the risk-taking PRS and MD but not FA are indicative of the greater sensitivity of MD. It is worth noting that PRS based on more relaxed p-value thresholds can demonstrate significance when the stringent ones do not, as increasing numbers of SNPs included in the more relaxed PRS contribute to increased genetic information, as well as increased statistical power. These structural MRI associations are tentative, therefore speculation as to their functional relevance is not warranted.

Gene expression analysis

Eight of the lead SNPs (Table 2) were present in the LIBD dataset. Three of these showed robust eQTLs (rs2304681, rs3943093 and rs17187323). Most strikingly, the chromosome 2 lead SNP (rs2304681) is associated with the expression of several nearby genes (Table S16). The most numerous and statistically robust associations are with CGREF1 (minimum FDR-corrected p = 3.4 × 10−22), with prominent associations also seen for KHK (p = 1.5 × 10−13) and DPYSL5 (p = 4.2 × 10−5). Intriguingly, for both CGREF1 and KHK there is evidence that the SNP may be associated with the expression of specific transcripts, because the rs2304681 A allele (associated with reduced risk taking) predicts increased expression of certain junctions/transcript features but decreased expression of others (Figure S4; Table S16). In contrast, for DPYSL5, the rs2304681 A allele uniformly predicts decreased expression (Table S16). CGREF1, KHK and DPYSL5 are all expressed in brain (Figure S5). Notably, in the case of CGREF1 and DPYSL5, the brain is the tissue in which these genes are most abundantly expressed.

Two of the SNPs found to predict risk taking only in women also showed eQTLs. The rs3943093 T allele (associated with lower risk of risk taking, Chr1) predicted increased expression of SDCCAG8 (p = 7.7 × 10−11). The A allele of rs17187323 (associated with increased risk taking) predicted lower expression of C15orf59 (p = 0.00014; Table S16).

All of the genes implicated in the eQTL analyses show some expression in human brain (Figure S5). Notably, in the case of CGREF1, DPYSL5 and C15orf59 expression in the brain is particularly prominent, compared with other tissues (Figure S5). Furthermore, all of the genes highlighted above show evidence of differential expression across development: CGREF1, KHK and C15orf59 show greater expression in adult brain than foetal brain, whereas this pattern is reversed for DPYSL5 and SDCCAG8.

Data mining

The predicted functional consequences of risk-taking-associated SNPs (GWAS and suggestive significance) highlighted a number of missense variants with potentially moderate impact on genes (Table S17). One variant on Chr6 was predicted to have a high impact: rs539861690-A is predicted to give rise to a premature stop codon in ZKSCAN4. Conditional analysis of the Chr6 region demonstrates that adjusting for the Chr6 lead SNPs rs188973463 and rs566858049 reduced the association of rs539861690 with risk taking to nonsignificant (p = 0.2824) or nominal significance (p = 0.0129) respectively. This suggests that rs539861690 could be the functional variant in this region but as no genotype-specific expression patterns were identified for this SNP functional studies are required to verify this.

None of the lead SNPs have previously been reported to be associated with any trait in the GWAS catalogue (2018-01-31). These risk-taking loci have previously been associated with: educational attainment32, SCZ33,34 and PTSD35 (Chr1 locus); cognitive function36,37, educational attainment32,38, adiposity39,40,41 and alcohol consumption42 (CADM2 locus); SCZ43,44 and ADHD45 (Chr6 locus); and sleep duration46 (FOXP2 locus). Of the previously reported SNPs at these loci, 16 met the threshold for “suggestive” evidence of association with risk taking in this study (Table S18). Where the reported data allowed comparison, results were as expected (Table S18), with alleles, which increased risk of SCZ33,34,43,44 associated with increased risk taking; the allele for increased sleep duration46 associated with reduced risk taking; and the allele for increased information processing speed37 also associated with reduced risk taking. In contrast, the association between alleles for increased educational attainment32 and increased risk taking may seem counter-intuitive but is consistent with previous findings from UK Biobank (n~116,000)13. The allele associated with waist circumference41 was associated with increased risk taking, but the opposite was observed for BMI39, (although the BMI study was in a Japanese population39, whereas the risk-taking study was in a European study, so ethnic-specific effects (in regulation of BMI and/or risk taking) could be responsible for this discrepancy).