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Genome-wide association studies (GWASs) have identified specific genetic variants associated with complex human traits and behaviors, such as educational attainment, mental disorders, and personality. However, small effect sizes for individual variants, uncertainty regarding the biological function of discovered genotypes, and potential “outsidethe-skin” environmental mechanisms leave a translational gulf between GWAS results and scientific understanding that will improve human health and well-being. We propose a set of social, behavioral, and brain-science research activities that map discovered genotypes to neural, developmental, and social mechanisms and call this research program phenotypic annotation. Phenotypic annotation involves (a) elaborating the nomological network surrounding discovered genotypes, (b) shifting focus from individual genes to whole genomes, and (c) testing how discovered genotypes affect life-span development. Phenotypic-annotation research is already advancing the understanding of GWAS discoveries for educational attainment and schizophrenia. We review examples and discuss methodological considerations for psychologists taking up the phenotypic-annotation approach.

Genes account for between approximately 50% and 70% of the variation in cognition at the population level. However, population-level estimates of heritability potentially mask marked subgroup differences. We review the body of empirical evidence indicating that (a) genetic influences on cognition increase from infancy to adulthood, and (b) genetic influences on cognition are maximized in more advantaged socioeconomic contexts (i.e., a Gene × Socioeconomic Status interaction). We discuss potential mechanisms underlying these effects, particularly transactional models of cognitive development. Transactional models predict that people in high-opportunity contexts actively evoke and select positive learning experiences on the basis of their genetic predispositions; these learning experiences, in turn, reciprocally influence cognition. The net result of this transactional process is increasing genetic influence with increasing age and increasing environmental opportunity.

Social science genetics is concerned with understanding whether, how and why genetic differences between human beings are linked to differences in behaviours and socioeconomic outcomes. Our review discusses the goals, methods, challenges and implications of this research endeavour. We survey how the recent developments in genetics are beginning to provide social scientists with a powerful new toolbox they can use to better understand environmental effects, and we illustrate this with several substantive examples. Furthermore, we examine how medical research can benefit from genetic insights into social-scientific outcomes and vice versa. Finally, we discuss the ethical challenges of this work and clarify several common misunderstandings and misinterpretations of genetic research on individual differences. Harden and Koellinger discuss the goals, methods and challenges of social science genetics, which aims to unravel the genetic underpinnings of individual differences in social, behavioural and health outcomes.

Genetic associations show how parenting matters for children's education Children resemble their parents in health, wealth, and well-being. Is parent-child similarity in traits and behaviors due to nature (the genes that children inherit from their parents) or nurture (the environment that parents provide for their children)? Answering this enduring question can directly inform our efforts to reduce social inequality and disease burden. On page 424 of this issue, Kong et al. ( 1 ) use genetic data from trios of parents and offspring to address this question in an intriguing way. By measuring parents' and children's genes, they provide evidence that inherited family environments influence children's educational success, a phenomenon termed genetic nurture.

Recent evidence suggests that impulsivity and sensation seeking are not stable risk factors for substance use among adolescents and early adults but rather that they undergo significant developmental maturation and change. Further, developmental trends of both personality facets may vary across individuals. In the current investigation, we used longitudinal data from ages 15 to 26 on 5,632 individuals drawn from the offspring generation of the National Longitudinal Survey of Youth to examine whether interindividual differences in intraindividual change in impulsivity and sensation seeking predicted the escalation of alcohol, marijuana, and cigarette use in adolescence and early adulthood. Latent growth curve models revealed significant individual differences in rates of change in both personality and substance use. Age-related changes in personality were positively associated with individual differences in substance-use change. Individuals who declined more slowly in impulsivity increased in alcohol, marijuana, and cigarette more rapidly, whereas individuals who declined more slowly in sensation seeking increased more rapidly in alcohol use only. Although risk for substance use across the population may peak during adolescence and early adulthood, this risk may be highest among those who decline more gradually in impulsivity.

Little is known about the genetic architecture of traits affecting educational attainment other than cognitive ability. We used genomic structural equation modeling and prior genome-wide association studies (GWASs) of educational attainment ( n  = 1,131,881) and cognitive test performance ( n  = 257,841) to estimate SNP associations with educational attainment variation that is independent of cognitive ability. We identified 157 genome-wide-significant loci and a polygenic architecture accounting for 57% of genetic variance in educational attainment. Noncognitive genetics were enriched in the same brain tissues and cell types as cognitive performance, but showed different associations with gray-matter brain volumes. Noncognitive genetics were further distinguished by associations with personality traits, less risky behavior and increased risk for certain psychiatric disorders. For socioeconomic success and longevity, noncognitive and cognitive-performance genetics demonstrated associations of similar magnitude. By conducting a GWAS of a phenotype that was not directly measured, we offer a view of genetic architecture of noncognitive skills influencing educational success. Genomic structural equation modeling of genome-wide association data for educational attainment and cognitive test performance is used to estimate the genetic component of variation in educational attainment that is independent of cognitive ability. The study finds that noncognitive skills account for 57% of genetic variation in educational attainment.

Genetic correlations estimated from genome-wide association studies (GWASs) reveal pervasive pleiotropy across a wide variety of phenotypes. We introduce genomic structural equation modelling (genomic SEM): a multivariate method for analysing the joint genetic architecture of complex traits. Genomic SEM synthesizes genetic correlations and single-nucleotide polymorphism heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to model multivariate genetic associations among phenotypes, identify variants with effects on general dimensions of cross-trait liability, calculate more predictive polygenic scores and identify loci that cause divergence between traits. We demonstrate several applications of genomic SEM, including a joint analysis of summary statistics from five psychiatric traits. We identify 27 independent single-nucleotide polymorphisms not previously identified in the contributing univariate GWASs. Polygenic scores from genomic SEM consistently outperform those from univariate GWASs. Genomic SEM is flexible and open ended, and allows for continuous innovation in multivariate genetic analysis. Grotzinger et al. develop a multivariate method for analysing the joint genetic architectures of complex traits: genomic structural equation modelling. They provide several applications of the method, including a joint analysis of five psychiatric traits.

Although testosterone is associated with aggression in the popular imagination, previous research on the links between testosterone and human aggression has been inconsistent. This inconsistency might be because testosterone’s effects on aggression depend on other moderators. In a large adolescent sample (N = 984, of whom 460 provided hair samples), we examined associations between aggression and salivary testosterone, hair testosterone, and hair cortisol. Callous-unemotional traits, parental monitoring, and peer environment were examined as potential moderators of hormone-behavior associations. Salivary testosterone was not associated with aggression. Hair testosterone significantly predicted increased aggression, particularly at low levels of hair cortisol (i.e., Testosterone × Cortisol interaction). This study is the first to examine the relationship between hair hormones and externalizing behaviors and adds to the growing literature that indicates that androgenic effects on human behavior are contingent on aspects of the broader endocrine environment—in particular, levels of cortisol.

The German Socio-Economic Panel (SOEP) serves a global research community by providing representative annual longitudinal data of respondents living in private households in Germany. The dataset offers a valuable life course panorama, encompassing living conditions, socioeconomic status, familial connections, personality traits, values, preferences, health, and well-being. To amplify research opportunities further, we have extended the SOEP Innovation Sample (SOEP-IS) by collecting genetic data from 2,598 participants, yielding the first genotyped dataset for Germany based on a representative population sample (SOEP-G). The sample includes 107 full-sibling pairs, 501 parent-offspring pairs, and 152 triads, which overlap with the parent-offspring pairs. Leveraging the results from well-powered genome-wide association studies, we created a repository comprising 66 polygenic indices (PGIs) in the SOEP-G sample. We show that the PGIs for height, BMI, and educational attainment capture 22∼24%, 12∼13%, and 9% of the variance in the respective phenotypes. Using the PGIs for height and BMI, we demonstrate that the considerable increase in average height and the decrease in average BMI in more recent birth cohorts cannot be attributed to genetic shifts within the German population or to age effects alone. These findings suggest an important role of improved environmental conditions in driving these changes. Furthermore, we show that higher values in the PGIs for educational attainment and the highest math class are associated with better self-rated health, illustrating complex relationships between genetics, cognition, behavior, socio-economic status, and health. In summary, the SOEP-G data and the PGI repository we created provide a valuable resource for studying individual differences, inequalities, life-course development, health, and interactions between genetic predispositions and the environment.

Research linking genetic differences with human social and behavioural phenotypes has long been controversial. Frequently, debates about the ethical, social and legal implications of this area of research centre on questions about whether studies overtly or covertly perpetuate genetic determinism, genetic essentialism and/or genetic reductionism. Given the prominent role of the ‘-isms’ in scientific discourse and criticism, it is important for there to be consensus and clarity about the meaning of these terms. Here, the author integrates scholarship from psychology, genetics and philosophy of science to provide accessible definitions of genetic determinism, genetic reductionism and genetic essentialism. The author provides linguistic and visual examples of determinism, reductionism and essentialism in science and popular culture, discusses common misconceptions and concludes with recommendations for science communication.In this Perspective, Harden reviews the terms genetic determinism, genetic essentialism and genetic reductionism to provide consensus and clarity about the meaning of these terms. She discusses common misconceptions, illustrates examples and concludes with recommendations for science communication.