A genome-wide association study of educational attainment was conducted in a discovery sample of 101,069 individuals and a replication sample of 25,490. Three independent SNPs are genome-wide significant (rs9320913, rs11584700, rs4851266), and all three replicate. Estimated effects sizes are small (R2 ≈ 0.02%), approximately 1 month of schooling per allele. A linear polygenic score from all measured SNPs accounts for ≈ 2% of the variance in both educational attainment and cognitive function. Genes in the region of the loci have previously been associated with health, cognitive, and central nervous system phenotypes, and bioinformatics analyses suggest the involvement of the anterior caudate nucleus. These findings provide promising candidate SNPs for follow-up work, and our effect size estimates can anchor power analyses in social-science genetics.

General intelligence (g) and virtually all other behavioral traits are heritable. Associations between g and specific single-nucleotide polymorphisms (SNPs) in several candidate genes involved in brain function have been reported. We sought to replicate published associations between 12 specific genetic variants and g using three independent, longitudinal datasets of 5571, 1759, and 2441 well-characterized individuals. Of 32 independent tests across all three datasets, only one was nominally significant at the p < .05 level. By contrast, power analyses showed that we should have expected 10–15 significant associations, given reasonable assumptions for genotype effect sizes. As positive controls, we confirmed accepted genetic associations for Alzheimer disease and body mass index, and we used SNP-based relatedness calculations to replicate estimates that about half of the variance in g is accounted for by common genetic variation among individuals. We conclude that different approaches than candidate genes are needed in the molecular genetics of psychology and social science.

Preferences are fundamental building blocks in all models of economic and political behavior. We study a new sample of comprehensively genotyped subjects with data on economic and political preferences and educational attainment. We use dense single nucleotide polymorphism (SNP) data to estimate the proportion of variation in these traits explained by common SNPs and to conduct genome-wide association study (GWAS) and prediction analyses. The pattern of results is consistent with findings for other complex traits. First, the estimated fraction of phenotypic variation that could, in principle, be explained by dense SNP arrays is around one-half of the narrow heritability estimated using twin and family samples. The molecular-genetic-based heritability estimates, therefore, partially corroborate evidence of significant heritability from behavior genetic studies. Second, our analyses suggest that these traits have a polygenic architecture, with the heritable variation explained by many genes with small effects. Our results suggest that most published genetic association studies with economic and political traits are dramatically underpowered, which implies a high false discovery rate. These results convey a cautionary message for whether, how, and how soon molecular genetic data can contribute to, and potentially transform, research in social science. We propose some constructive responses to the inferential challenges posed by the small explanatory power of individual SNPs.genoeconomics | genopolitics | GCTA T here has been growing enthusiasm for the use of molecular genetic data in social science research. This enthusiasm is based on a number of potential contributions that such research could make to social science (1-3). For example, if specific genetic markers can be identified that are associated with a behavioral trait, then such predictive markers may shed light on the biological pathways underlying that trait (3, 4). If a set of genetic markers is sufficiently predictive, then these markers could be used in social science research as control variables, as instrumental variables (5, 6; for critical perspectives, see refs. 7, 8) or, under certain conditions, as factors for identifying at-risk individuals (1-3).The extent to which this potential of molecular genetic data will be fulfilled for a given trait hinges on the trait's "molecular genetic architecture," i.e., the joint distribution of effect sizes and allele frequencies of the causal genetic variants (9). The architecture-which is the result of evolutionary forces, including mutation, drift, and selection-determines the difficulty with which the genetic variants associated with a trait can be identified and what sample sizes will be required for gene discovery. It also determines the out-of-sample aggregate predictability that can be derived from a set of genetic markers considered jointly.Existing studies claiming to have established genetic associations with economic and political traits typically use samples of several hundred individuals, and no...

The costs of comprehensively genotyping human subjects have fallen to the point where major funding bodies, even in the social sciences, are beginning to incorporate genetic and biological markers into major social surveys. How, if at all, should economists use and combine molecular genetic and economic data from these surveys? What challenges arise when analyzing genetically informative data? To illustrate, we present results from a "genome-wide association study" of educational attainment. We use a sample of 7,500 individuals from the Framingham Heart Study; our dataset contains over 360,000 genetic markers per person. We get some initially promising results linking genetic markers to educational attainment, but these fail to replicate in a second large sample of 9,500 people from the Rotterdam Study. Unfortunately such failure is typical in molecular genetic studies of this type, so the example is also cautionary. We discuss a number of methodological challenges that face researchers who use molecular genetics to reliably identify genetic associates of economic traits. Our overall assessment is cautiously optimistic: this new data source has potential in economics. But researchers and consumers of the genoeconomic literature should be wary of the pitfalls, most notably the difficulty of doing reliable inference when faced with multiple hypothesis problems on a scale never before encountered in social science.

The American Psychiatric Association estimates that 3 to 7 per cent of all school aged children are diagnosed with attention deficit hyperactivity disorder (ADHD). Even after correcting for general cognitive ability, numerous studies report a negative association between ADHD and educational achievement. With polygenic scores we examined whether genetic variants that have a positive influence on educational attainment have a protective effect against ADHD. The effect sizes from a large GWA metaanalysis of educational attainment in adults were used to calculate polygenic scores in an independent sample of 12-year-old children from the Netherlands Twin Register. Linear mixed models showed that the polygenic scores significantly predicted educational achievement, school performance, ADHD symptoms and attention problems in children. These results confirm the genetic overlap between ADHD and educational achievement, indicating that one way to gain insight into genetic variants responsible for variation in ADHD is to include data on educational achievement, which are available at a larger scale.