Significance This paper makes two contributions to research on the link between the social environment and health. Using data from a birth cohort study, we show that, among African American boys, those who grow up in highly disadvantaged environments have shorter telomeres (at age 9) than boys who grow up in highly advantaged environments. We also find that the association between the social environment and telomere length (TL) is moderated by genetic variation within the serotonin and dopamine pathways. Boys with the highest genetic sensitivity scores had the shortest TL when exposed to disadvantaged environments and the longest TL when exposed to advantaged environments. To our knowledge, this report is the first to document a gene–social environment interaction for TL, a biomarker of stress exposure.

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 (GWAS) 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. Non-cognitive genetics were enriched in the same brain tissues and cell types as cognitive performance but showed different associations with gray-matter brain volumes. Non-cognitive genetics were further distinguished by associations with personality traits, less risky behavior,and increased risk for certain psychiatric disorders.For socioeconomic success and longevity, non-cognitive and cognitive-performance genetics demonstrated similar-magnitude associations. By conducting a GWAS of a phenotype that was not directly measured, we offer a first view of genetic architecture of non-cognitive skills influencing educational success.

The last decades of neuroscience research have produced immense progress in the methods available to understand brain structure and function. Social, cognitive, clinical, affective, economic, communication, and developmental neurosciences have begun to map the relationships between neuro-psychological processes and behavioral outcomes, yielding a new understanding of human behavior and promising interventions. However, a limitation of this fast moving research is that most findings are based on small samples of convenience. Furthermore, our understanding of individual differences may be distorted by unrepresentative samples, undermining findings regarding brain-behavior mechanisms. These limitations are issues that social demographers, epidemiologists, and other population scientists have tackled, with solutions that can be applied to neuroscience. By contrast, nearly all social science disciplines, including social demography, sociology, political science, economics, communication science, and psychology, make assumptions about processes that involve the brain, but have incorporated neural measures to differing, and often limited, degrees; many still treat the brain as a black box. In this article, we describe and promote a perspective-population neuroscience-that leverages interdisciplinary expertise to (i) emphasize the importance of sampling to more clearly define the relevant populations and sampling strategies needed when using neuroscience methods to address such questions; and (ii) deepen understanding of mechanisms within population science by providing insight regarding underlying neural mechanisms. Doing so will increase our confidence in the generalizability of the findings. We provide examples to illustrate the population neuroscience approach for specific types of research questions and discuss the potential for theoretical and applied advances from this approach across areas.neuroimaging | life course | statistics | survey methodology | physics Why Population Neuroscience? How do biology, social situations, and the broader environmental context interact to guide behavior, health, and development? This question is fundamental to most, if not all, social and behavioral sciences. We argue that to effectively address the many topics that stem from this larger question across disciplines, it is necessary to (i) bring a "population perspective" to neuroscience and (ii) leverage neuroscience tools within population sciences, which are subdisciplines of many fields, areas, and departments focused on documenting and understanding the dynamics of human populations, including outcomes such as health, well-being, behavior, etc.

Introduction Genome-wide association studies (GWAS) on a wide range of important human traits have identified hundreds of variants in multi-cohort meta-analyses that have highly significant associations (p<5 x10-8) that replicate across studies. However, the identified genetic variants, or single nucleotide polymorphisms (SNPs), generally explain a very small fraction of variability in the trait of interest. Moreover, for most behavioral and health scientists, the use of such massive and complex genetic data is unwieldy. In response to the desire to harness genome-wide information, use of polygenic scores (PGSs), also known as "genetic risk scores", has grown rapidly in the social, behavioral, and health sciences recently (see (1)). PGSs use the results of GWAS-typically in the form of effect estimates or p-values for each nucleotide locus-to summarize an individual's genetic association with a given trait. They can provide a single summary of measured genetic contribution of a trait that is easily integrated into more mainstream analyses of health and behavior. PGSs have multiple uses including improving risk prediction modeling controlling for some portion of variation due to genetics, investigating the common genetic basis among diseases, and estimating the genetic susceptibility of traits not measured but of high interest in a cohort (e.g. a PGS for post-traumatic stress disorder in a study of veterans or a cardiovascular PGS in a cognition study). PGSs are approximately normally distributed and relatively easy to construct. In short they appear to provide a relatively straightforward mechanism to integrate large amounts of genetic information into studies that are too small for genetic discovery, did not collect the health outcomes of interest, or do not have experience using large-scale genetic data. The relative ease of creation and use has led to a wide range of practices in creating PGSs (2-4).These practices vary based on researchers' decisions, including: 1) using genotyped SNPs versus imputed SNPs, 2) selecting SNPs for inclusion in the PGS from a meta-analysis of GWAS studies based on a particular p-value threshold, 3) whether to account for linkage disequilibrium (LD) across the genome, and 4) the effect of different options for accounting for linkage disequilibrium. And yet, despite their rapid adoption in the social sciences, best practices for PGS construction and evaluation remains relatively unexplored. Many researchers use programs such as PLINK (5), PRSice (6), and LDPred (7), designed to make PGS creation relatively simple. For novice users, these programs have produced a 'black box' creation platform that does not explore and display the range of PGSs that could be estimated. Moreover, researchers rarely report in sufficient detail the decisions, thresholds, and options used in the construction of their scores, which is likely to affect the replicability and reliability of PGSs. The degree to which all of these decisions and the combination of these decisions influence the. CC-BY-NC-ND 4.0...

This review describes stress-related biological mechanisms linking interpersonal racism to life course health trajectories among African Americans. Interpersonal racism, a form of social exclusion enacted via discrimination, remains a salient issue in the lives of African Americans, and it triggers a cascade of biological processes originating as perceived social exclusion and registering as social pain. Exposure to discrimination increases sympathetic nervous system activation and upregulates the HPA axis, increasing physiological wear and tear and elevating the risks of cardiometabolic conditions. Consequently, discrimination is associated with morbidities including low birth weight, hypertension, abdominal obesity, and cardiovascular disease. Biological measures can provide important analytic tools to study the interactions between social experiences such as racial discrimination and health outcomes over the life course. We make future recommendations for the study of discrimination and health outcomes, including the integration of neuroscience, genomics, and new health technologies; interdisciplinary engagement; and the diversification of scholars engaged in biosocial inequities research.