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. 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 (|truer^g| ~ 0.25 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 found no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

Human altruism shaped our evolutionary history and pervades social and political life. There are, however, enormous individual differences in altruism. Some people are almost completely selfish, while others display strong altruism, and the factors behind this heterogeneity are only poorly understood. We examine the neuroanatomical basis of these differences with voxel-based morphometry and show that gray matter (GM) volume in the right temporoparietal junction (TPJ) is strongly associated with both individuals' altruism and the individual-specific conditions under which this brain region is recruited during altruistic decision making. Thus, individual differences in GM volume in TPJ not only translate into individual differences in the general propensity to behave altruistically, but they also create a link between brain structure and brain function by indicating the conditions under which individuals are likely to recruit this region when they face a conflict between altruistic and selfish acts.

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The role of trust in promoting economic activity and societal development has received considerable academic attention by social scientists. A popular way to measure trust at the individual level is the so-called "investment game" (Berg, Dickhaut, and McCabe, 1995). It has been widely noted, however, that risk attitudes can also affect decisions in this game, and thus in principle confound inferences about trust. We provide novel evidence, shedding light on the role of risk attitudes for trusting decisions. To the best of our knowledge, our data are the first rigorous evidence that (i) aggregate investment distributions differ significantly between trust and risk environments, and (ii) risk attitudes predict individual investment decisions in risk games but not in the corresponding trust games. Our results are convergent evidence that trust decisions are not tightly connected to a person's risk attitudes, and they lend support to the "trust" interpretation of decisions in investment games. Abstract:The role of trust in promoting economic activity and societal development has received considerable academic attention by social scientists. A popular way to measure trust at the individual level is the so-called "investment game" (Berg, Dickhaut, and McCabe, 1995). It has been widely noted, however, that risk attitudes can also affect decisions in this game, and thus in principle confound inferences about trust. We provide novel evidence, shedding light on the role of risk attitudes for trusting decisions. To the best of our knowledge, our data are the first rigorous evidence that (i) aggregate investment distributions differ significantly between trust and risk environments, and (ii) risk attitudes predict individual investment decisions in risk games but not in the corresponding trust games. Our results are convergent evidence that trust decisions are not tightly connected to a person's risk attitudes, and they lend support to the "trust" interpretation of decisions in investment games.

Reputation formation pervades human social life. In fact, many people go to great lengths to acquire a good reputation, even though building a good reputation is costly in many cases. Little is known about the neural underpinnings of this important social mechanism, however. In the present study, we show that disruption of the right, but not the left, lateral prefrontal cortex (PFC) with low-frequency repetitive transcranial magnetic stimulation (rTMS) diminishes subjects' ability to build a favorable reputation. This effect occurs even though subjects' ability to behave altruistically in the absence of reputation incentives remains intact, and even though they are still able to recognize both the fairness standards necessary for acquiring and the future benefits of a good reputation. Thus, subjects with a disrupted right lateral PFC no longer seem to be able to resist the temptation to defect, even though they know that this has detrimental effects on their future reputation. This suggests an important dissociation between the knowledge about one's own best interests and the ability to act accordingly in social contexts. These results link findings on the neural underpinnings of self-control and temptation with the study of human social behavior, and they may help explain why reputation formation remains less prominent in most other species with less developed prefrontal cortices.decision making ͉ social interaction ͉ transcranial magnetic stimulation

The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008–2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0.

JEL-CODES: C15, C421 I gratefully acknowledge many astute comments by

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