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Meta-Analysis
. 2019 Feb;51(2):245-257.
doi: 10.1038/s41588-018-0309-3. Epub 2019 Jan 14.

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 Linnér  1   2   3 , Pietro Biroli  4 , Edward Kong  5 , S Fleur W Meddens  6   7   8 , Robbee Wedow  9   10   11   12 , Mark Alan Fontana  13   14 , Maël Lebreton  15   16 , Stephen P Tino  17 , Abdel Abdellaoui  18 , Anke R Hammerschlag  6 , Michel G Nivard  18 , Aysu Okbay  6   8 , Cornelius A Rietveld  7   19   20 , Pascal N Timshel  21   22 , Maciej Trzaskowski  23 , Ronald de Vlaming  6   7   8 , Christian L Zünd  4 , Yanchun Bao  24 , Laura Buzdugan  25   4 , Ann H Caplin  26 , Chia-Yen Chen  9   11   27 , Peter Eibich  28   29   30 , Pierre Fontanillas  31 , Juan R Gonzalez  32   33   34 , Peter K Joshi  35 , Ville Karhunen  36 , Aaron Kleinman  31 , Remy Z Levin  37 , Christina M Lill  38 , Gerardus A Meddens  39 , Gerard Muntané  40   41   42 , Sandra Sanchez-Roige  43 , Frank J van Rooij  20 , Erdogan Taskesen  6 , Yang Wu  23 , Futao Zhang  23 ; 23and Me Research Team; eQTLgen Consortium; International Cannabis Consortium; Social Science Genetic Association Consortium; Adam Auton  31 , Jason D Boardman  44   45   46 , David W Clark  35 , Andrew Conlin  47 , Conor C Dolan  18 , Urs Fischbacher  48   49 , Patrick J F Groenen  7   50 , Kathleen Mullan Harris  51   52 , Gregor Hasler  53 , Albert Hofman  10   20 , Mohammad A Ikram  20 , Sonia Jain  54 , Robert Karlsson  55 , Ronald C Kessler  56 , Maarten Kooyman  57 , James MacKillop  58   59 , Minna Männikkö  36 , Carlos Morcillo-Suarez  40 , Matthew B McQueen  60 , Klaus M Schmidt  61 , Melissa C Smart  24 , Matthias Sutter  62   63   64 , A Roy Thurik  7   65 , André G Uitterlinden  66 , Jon White  67 , Harriet de Wit  68 , Jian Yang  23   69 , Lars Bertram  38   70   71 , Dorret I Boomsma  18 , Tõnu Esko  72 , Ernst Fehr  4 , David A Hinds  31 , Magnus Johannesson  73 , Meena Kumari  24 , David Laibson  5 , Patrik K E Magnusson  55 , Michelle N Meyer  74 , Arcadi Navarro  40   75   76 , Abraham A Palmer  43   77 , Tune H Pers  21   22 , Danielle Posthuma  6   78 , Daniel Schunk  79 , Murray B Stein  43   54 , Rauli Svento  47 , Henning Tiemeier  20 , Paul R H J Timmers  35 , Patrick Turley  9   11   80 , Robert J Ursano  81 , Gert G Wagner  29   82 , James F Wilson  35   83 , Jacob Gratten  23   84 , James J Lee  85 , David Cesarini  86 , Daniel J Benjamin  80   87   88 , Philipp D Koellinger  6   8   89 , Jonathan P Beauchamp  90
Collaborators, Affiliations
Meta-Analysis

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 Linnér et al. Nat Genet. 2019 Feb.

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 ([Formula: see text] ~ 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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Figures

Figure 1 |
Figure 1 |. Manhattan plots.
In all panels, the x-axis is chromosomal position; the y-axis is the GWAS P value on a −log10 scale (based on a two-tailed z-test); each lead SNP is marked by a red “×”; each conditional association is marked by a red “o”; and each SNP that is both a lead SNP and a conditional association is marked by a red “⊗”. a, Manhattan plots for the discovery GWAS of general risk tolerance (n = 939,908). b, Local Manhattan plots of a long-range LD region on chromosome 3 and a candidate inversion on chromosome 18 that contain lead SNPs for all seven of our GWAS. The gray background marks the locations of long-range LD or candidate inversion regions. c, Local Manhattan plots of the areas around the 15 most commonly tested candidate genes in the prior literature on the genetics of risk tolerance. Each local plot shows all SNPs within 500 kb of the gene’s borders that are in weak LD (r2 > 0.1) with a SNP in the gene. The 15 plots are concatenated and shown together in the panel, divided by the black vertical lines. The 15 genes are not particularly strongly associated with general risk tolerance or the risky behaviors, as can be seen by comparing the results within each row across panels b and c (the three rows correspond to the GWAS of general risk tolerance, adventurousness (n = 557,923), and the first PC of the four risky behaviors (n = 315,894)).
Figure 2 |
Figure 2 |. Genetic correlations with general risk tolerance.
The genetic correlations were estimated using bivariate LD Score (LDSC) regression. Error bars show 95% confidence intervals. For the supplementary phenotypes and the additional risky behaviors, green bars represent significant estimates with the expected signs, where higher risk tolerance is associated with riskier behavior. For the other phenotypes, blue bars represent significant estimates. Light green and light blue bars represent genetic correlations that are statistically significant at the 5% level, and dark green and dark blue bars represent correlations that are statistically significant after Bonferroni correction for 35 tests (the total number of phenotypes tested). Grey bars represent correlations that are not statistically significant at the 5% level. The two dotted vertical lines indicate genetic correlations of −0.5 and 0.5, respectively. All significance tests are two-sided.
Figure 3 |
Figure 3 |. Results from selected biological analyses.
a, DEPICT gene-set enrichment diagram. We identified 93 reconstituted gene sets that are significantly enriched (FDR < 0.01) for genes overlapping DEPICT-defined loci associated with general risk tolerance; using the Affinity Propagation method, these were grouped into the 13 clusters displayed in the graph. Each cluster was named after its exemplary gene set, as chosen by the Affinity Propagation tool, and each cluster’s color represents the permutation P value of its most significant gene set. The “synapse part” cluster includes the gene set “glutamate receptor activity,” and several members of the “GABAA receptor activation” cluster are defined by gamma-aminobutyric acid signaling. Overlap between the named representatives of two clusters is represented by an edge. Edge width represents the Pearson correlation ρ between the two respective vectors of gene membership scores (ρ < 0.3, no edge; 0.3 ≤ ρ < 0.5, thin edge; 0.5 ≤ ρ < 0.7, intermediate edge; ρ ≥ 0.7, thick edge). b, Results of DEPICT tissue enrichment analysis using GTEx data. The panel shows whether the genes overlapping DEPICT-defined loci associated with general risk tolerance are significantly overexpressed (relative to genes in random sets of loci matched by gene density) in various tissues. Tissues are grouped by organ or tissue type. The orange bars correspond to tissues with significant overexpression (FDR < 0.01). The y-axis is the significance on a −log10 scale. See Supplementary Note for additional details.
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