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We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57.

Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs' prediction accuracies, we constructed them using genome-wide association studies-some not previously published-from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the 'additive SNP factor'. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.

Genetic variants identified in genome-wide association studies of educational attainment have been linked with a range of positive life course development outcomes. However, it remains unclear whether school environments may moderate these genetic associations. We analyze data from two biosocial surveys that contain both genetic data and follow students from secondary school through mid- to late life. We test if the magnitudes of the associations with educational and occupational attainments varied across the secondary schools that participants attended or with characteristics of those schools. Although we find little evidence that genetic associations with educational and occupational attainment varied across schools or with school characteristics, genetic associations with any postsecondary education and college completion were moderated by school-level socioeconomic status. Along similar lines, we observe substantial between-school variation in the average level of educational attainment students achieved for a fixed genotype. These findings emphasize the importance of social context in the interpretation of genetic associations. Specifically, our results suggest that though existing measures of individual genetic endowment have a linear relationship with years of schooling that is relatively consistent across school environments, school context is crucial in connecting an individual's genotype to his or her likelihood of crossing meaningful educational thresholds.

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.

Data from the General Social Survey indicate that higher-fertility individuals and their children are more conservative on "family values" issues, especially regarding abortion and same-sex marriage. This pattern implies that differential fertility has increased and will continue to increase public support for conservative policies on these issues. The association of family size with conservatism is specific to traditional-family issues and can be attributed in large part to the greater religiosity and lower educational attainment of individuals from larger families. Over the 2004 to 2018 period, opposition to same-sex marriage and abortion was 3 to 4 percentage points more prevalent than it would have been were traditional-family conservatism independent of family size in the current generation. For same-sex marriage, evolutionary forces have grown in relative importance as society as a whole has liberalized. As of 2018, differential fertility raised the number of US adults opposed to same-sex marriage by 17%, from 46.9 million to 54.8 million.

Despite a steep rise in income inequality over the past five decades, Americans' preferences for redistribution have remained stagnant. Previous research suggests that redistributive preferences are rooted in stable institutional and cultural contexts but can change with exposure to information. We investigate the role of understandings of the link between income and psychological well-being in shaping policy preferences. Further, we consider whether effects differ if similar information is framed in terms of disadvantages for the poor versus advantages for the affluent. In a large, preregistered online experiment (N = 2,751), we examined the effects of three common themes in scholarship on happiness and well-being: Money Prevents Unhappiness, Money Provides Happiness, and Money Doesn't Matter. Results show that learning that Money Prevents Unhappiness (versus the other two themes) increased egalitarian preferences. Effects were moderated by political ideology, income, and subjective social class but not by race. We discuss the implications of these findings in light of the current cultural discourse about happiness, which often privileges non-income causes and positive emotions. [ABSTRACT FROM AUTHOR]

This article addresses a potentially serious problem with the widely used self-rated health (SRH) survey item: that different groups have systematically different ways of using the item's response categories. Analyses based on unadjusted SRH may thus yield misleading results. The authors evaluate anchoring vignettes as a possible solution to this problem. Using vignettes specifically designed to calibrate the SRH item and data from the Wisconsin Longitudinal Study (WLS; N = 2,625), the authors show how demographic and health-related factors, including sex and education, predict differences in rating styles. Such differences, when not adjusted for statistically, may be sufficiently large to lead to mistakes in rank orderings of groups. In the present sample, unadjusted models show that women have better SRH than men, but this difference disappears in models adjusting for women's greater health-optimism. Anchoring vignettes appear a promising tool for improving intergroup comparability of SRH. (Contains 9 notes, 7 tables, and 2 figures.)