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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

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 (\(\left| {\hat r_{\mathrm{g}}} \right|\) ~ 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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Fig. 1: Manhattan plots.
Fig. 2: Genetic correlations with general risk tolerance.
Fig. 3: Results from selected biological analyses.

Data availability

GWAS summary statistics can be downloaded from http://www.thessgac.org/data. SNP-level summary statistics from analyses based entirely or in part on 23andMe data can only be reported for up to 10,000 SNPs. For general risk tolerance, we provide association results for all SNPs that passed quality-control filters in a GWAS meta-analysis of general risk tolerance that excludes the research participants from 23andMe; we also provide association results from the complete GWAS (which includes data from 23andMe) for all lead SNPs identified in our discovery GWAS and MTAG analysis of general risk tolerance and for the 4,000 most significant SNPs in the meta-analysis of the discovery and replication GWAS of risk tolerance. For adventurousness, we provide association results from the complete GWAS (which includes only data from 23andMe) for all lead SNPs and for the next 4,000 most significant SNPs. For automobile speeding propensity, drinks per week, ever smoker, number of sexual partners, and the first PC of the four risky behaviors, we provide association results from the complete GWAS for all SNPs that passed quality-control filters. Contact information for the cohorts included in this paper can be found in the Supplementary Note.

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Acknowledgements

This research was carried out under the auspices of the Social Science Genetic Association Consortium. The research was also conducted using the UK Biobank Resource under application number 11425. The study was supported by funding from the Ragnar Söderberg Foundation (E9/11 and E42/15); the Swedish Research Council (421-2013-1061); the Jan Wallander and Tom Hedelius Foundation; an ERC Consolidator Grant to Philipp Koellinger (647648 EdGe); the Pershing Square Fund of the Foundations of Human Behavior; the Open Philanthropy Project; the National Institute on Aging, National Institutes of Health through grants P01-AG005842, P01-AG005842-20S2, P30-AG012810, and T32-AG000186-23 to the National Bureau of Economic Research and R01-AG042568-02 to the University of Southern California; the government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-152); and the Social Sciences and Humanities Research Council of Canada. We thank the International Cannabis Consortium, the eQTLgen Consortium, and the Psychiatric Genomics Consortium for sharing summary statistics from the GWAS of lifetime cannabis use, eQTL summary statistics, and summary statistics from the GWAS of ADHD, respectively. A full list of acknowledgments is provided in the Supplementary Note.

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Contributions

J.P.B., P.D.K., and D.J.B. designed and oversaw the study. J.P.B. closely supervised the analyses and led the writing of the manuscript. R.K.L. was the lead analyst, responsible for quality control, the meta-analyses, summarizing the overlap across the results of the various GWAS, and the SNP heritability analyses. E.K. conducted the population stratification, replication, and proxy-phenotype analyses. R.W. led the genetic correlation analyses, and M.A.F. contributed to those analyses. P.B. led the polygenic score prediction analyses, and R.K.L., E.K., R.W., A.A., R.d.V., M.A.F., and M.G.N. contributed to those analyses. P.B. and C.L.Z. conducted the MTAG analyses. The bioinformatics analyses were led by S.F.W.M. Analysts who assisted S.F.W.M. include J.G. and M.T. (SMR), A.R.H. (MAGMA), G.A.M. (Gene Network), and P.N.T. (DEPICT). M.L. conducted the review of the literature attempting to link risk tolerance to biological pathways. R.K.L. prepared the majority of figures; E.K. and S.P.T. also prepared some figures. A.O., C.A.R., and S.P.T. helped with several additional analyses. D.C., J.G., and J.J.L. provided helpful advice and feedback on various aspects of the study design. All authors contributed to and critically reviewed the manuscript. P.B., R.K.L., E.K., S.F.W.M., and R.W. made especially major contributions to the writing and editing; these authors contributed equally, and lead authors 2–5 are listed aphabetically in the author list.

Corresponding authors

Correspondence to Richard Karlsson Linnér or Jonathan P. Beauchamp.

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Competing interests

A. Auton, P.F., D.A.H., and A.K. are employees of 23andMe. R.C.K., in the past three years, received support for his epidemiological studies from Sanofi Aventis; was a consultant for Johnson & Johnson Wellness and Prevention, Sage Pharmaceuticals, Shire, and Takeda; and served on an advisory board for the Johnson & Johnson Services Inc. Lake Nona Life Project. R.C.K. is a co-owner of DataStat Inc., a market research firm that carries out healthcare research. J.M. is a principal in BEAM Diagnostics Inc. The authors declare no other competing interests.

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Karlsson Linnér, R., Biroli, P., Kong, E. 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). https://doi.org/10.1038/s41588-018-0309-3

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