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Understanding the social and biological mechanisms that lead to homogamy (similar individuals marrying one another) has been a long-standing issue across many fields of scientific inquiry. Using a nationally representative sample of non-Hispanic white US adults from the Health and Retirement Study and information from 1.7 million single-nucleotide polymorphisms, we compare genetic similarity among married couples to noncoupled pairs in the population. We provide evidence for genetic assortative mating in this population but the strength of this association is substantially smaller than the strength of educational assortative mating in the same sample. Furthermore, genetic similarity explains at most 10% of the assortative mating by education levels. Results are replicated using comparable data from the Framingham Heart Study.

Women’s opportunities have been profoundly altered over the past century by reductions in the social and structural constraints that limit women’s educational attainment. Do social constraints manifest as a suppressing influence on genetic indicators of potential, and if so, did equalizing opportunity mean equalizing the role of genetics? We address this with three cohort studies: the Wisconsin Longitudinal Study (WLS; birth years 1939 to 1940), the Health and Retirement Study, and the National Longitudinal Study of Adolescent Health (Add Health; birth years 1975 to 1982). These studies include a “polygenic score” for educational attainment, providing a novel opportunity to explore this question. We find that within the WLS cohort, the relationship between genetics and educational outcomes is weaker for women than for men. However, as opportunities changed in the 1970s and 1980s, and many middle-aged women went back to school, the relationship between genetic factors and education strengthened for women as they aged. Furthermore, utilizing the HRS and Add Health, we find that as constraints limiting women’s educational attainment declined, gender differences in the relationship between genetics and educational outcomes weakened. We demonstrate that genetic influence must be understood through the lens of historical change, the life course, and social structures like gender.

Understanding how parents’ cognitive and non-cognitive skills influence offspring education is essential for educational, family and economic policy. We use genetics (GWAS-by-subtraction) to assess a latent, broad non-cognitive skills dimension. To index parental effects controlling for genetic transmission, we estimate indirect parental genetic effects of polygenic scores on childhood and adulthood educational outcomes, using siblings ( N  = 47,459), adoptees ( N  = 6407), and parent-offspring trios ( N  = 2534) in three UK and Dutch cohorts. We find that parental cognitive and non-cognitive skills affect offspring education through their environment: on average across cohorts and designs, indirect genetic effects explain 36–40% of population polygenic score associations. However, indirect genetic effects are lower for achievement in the Dutch cohort, and for the adoption design. We identify potential causes of higher sibling- and trio-based estimates: prenatal indirect genetic effects, population stratification, and assortative mating. Our phenotype-agnostic, genetically sensitive approach has established overall environmental effects of parents’ skills, facilitating future mechanistic work. Understanding how parents’ cognitive and non-cognitive skills influence their children’s educational trajectories is important for educational, family and economic policy. Here, the authors investigate parental influence on children’s education using genetic approaches.

Selection bias in genome-wide association studies (GWASs) due to volunteer-based sampling (volunteer bias) is poorly understood. The UK Biobank (UKB), one of the largest and most widely used cohorts, is highly selected. Using inverse probability (IP) weights we estimate inverse probability weighted GWAS (WGWAS) to correct GWAS summary statistics in the UKB for volunteer bias. Our IP weights were estimated using UK Census data – the largest source of population-representative data – made representative of the UKB’s sampling population. These weights have a substantial SNP-based heritability of 4.8% (s.e. 0.8%), suggesting they capture volunteer bias in GWAS. Across ten phenotypes, WGWAS yields larger SNP effect sizes, larger heritability estimates, and altered gene-set tissue expression, despite decreasing the effective sample size by 62% on average, compared to GWAS. The impact of volunteer bias on GWAS results varies by phenotype. Traits related to disease, health behaviors, and socioeconomic status are most affected. We recommend that GWAS consortia provide population weights for their data sets, or use population-representative samples. Genetic studies may be biased due to volunteer-based biobanks. Using UK Biobank, the authors apply inverse probability weighting based on UK Census data, finding that genome-wide association studies showed bias in SNP effect sizes, heritability, and gene-set tissue expression.

Understanding the “fit” of models designed to predict binary outcomes has been a long-standing problem across the social sciences. We propose a flexible, portable, and intuitive metric for quantifying the change in accuracy between two predictive systems in the case of a binary outcome: the InterModel Vigorish (IMV). The IMV is based on an analogy to weighted coins, well-characterized physical systems with tractable probabilities. The IMV is always a statement about the change in fit relative to some baseline model—which can be as simple as the prevalence—whereas other metrics are stand-alone measures that need to be further manipulated to yield indices related to differences in fit across models. Moreover, the IMV is consistently interpretable independent of baseline prevalence. We contrast this metric with alternatives in numerous simulations. The IMV is more sensitive to estimation error than many alternatives and also shows distinctive sensitivity to prevalence. We consider its performance using examples spanning the social and natural sciences. The IMV allows for precise answers to questions about changes in model fit in a variety of settings in a manner that will be useful for furthering research and the understanding of social outcomes.

About the Authors: Benjamin W. Domingue * E-mail: bdomingue@stanford.edu (BWD); dbelsky@duke.edu (DWB) Affiliation: Graduate School of Education, Stanford University, Stanford California, United States of America ORCID http://orcid.org/0000-0002-3894-9049 Daniel W. Belsky * E-mail: bdomingue@stanford.edu (BWD); dbelsky@duke.edu (DWB) Affiliations Department of Medicine, Duke University School of Medicine, Durham, North Carolina, United States of America, Duke University Population Research Institute, Duke University, Durham, North Carolina, United States of America ORCID http://orcid.org/0000-0001-5463-2212Citation: Domingue BW, Belsky DW (2017) The social genome: The arrows depict social genetic processes. https://doi.org/10.1371/journal.pgen.1006615.g001 The present study Baud et al. conducted experiments with cage-dwelling mice to examine the effects of genetic composition of animals’ social environments on psychosocial and physiological phenotypes. Pathway analysis of blood gene expression corroborated phenotypic evidence; SGEs on phenotype were reflected in SGEs on patterns of gene expression. Using a combination of directly measured genetic data and pedigree information, analysis decomposed variance into direct genetic effects (the effects of a mouse’s own genes) and SGEs. In sum, the study by Baud et al. suggests SGEs (i) are pervasive, affecting many phenotypes; (ii) can be pronounced, contributing as much to phenotypic variance in some cases as direct genetic effects; and (iii) are nonignorable, as they may lead to bias in other estimates if not taken into account. Implications for research in humans These experiments with mice highlight opportunities and challenges for social genetic research in humans. Using genetic measures of the social environment to conduct a social version of Mendelian randomization analysis [13] may provide stronger grounds for...

This study asks two related questions about the shifting landscape of marriage and reproduction in US society over the course of the last century with respect to a range of health and behavioral phenotypes and their associated genetic architecture: (i) Has assortment on measured genetic factors influencing reproductive and social fitness traits changed over the course of the 20th century? (ii) Has the genetic covariance between fitness (as measured by total fertility) and other traits changed over time? The answers to these questions inform our understanding of how the genetic landscape of American society has changed over the past century and have implications for population trends. We show that husbands and wives carry similar loadings for genetic factors related to education and height. However, the magnitude of this similarity is modest and has been fairly consistent over the course of the 20th century. This consistency is particularly notable in the case of education, for which phenotypic similarity among spouses has increased in recent years. Likewise, changing patterns of the number of children ever born by phenotype are not matched by shifts in genotype–fertility relationships over time. Taken together, these trends provide no evidence that social sorting is becoming increasingly genetic in nature or that dysgenic dynamics have accelerated.

Interest in the study of gene-environment interaction has recently grown due to the sudden availability of molecular genetic data-in particular, polygenic scores-in many long-running longitudinal studies. Identifying and estimating statistical interactions comes with several analytic and inferential challenges; these challenges are heightened when used to integrate observational genomic and social science data. We articulate some of these key challenges, provide new perspectives on the study of gene-environment interactions, and end by offering some practical guidance for conducting research in this area. Given the sudden availability of well-powered polygenic scores, we anticipate a substantial increase in research testing for interaction between such scores and environments. The issues we discuss, if not properly addressed, may impact the enduring scientific value of gene-environment interaction studies.

Driven by our recent mapping of the human genome, genetics research is increasingly prominent and beginning to reintersect with education research. We describe previous intersections of these fields, focusing on the ways that they were harmful. We then discuss novel features of genetics research in the current era, with an emphasis on possibilities deriving from the availability of molecular genetic data and the proliferation of genome-wide association studies. We discuss both the promises and potential pitfalls resulting from the convergence of molecular genetic research and education research. The floodgates of genetic data have opened. Collaboration between those in the social and biomedical sciences; open conversation among policy makers, educators, and researchers; and public engagement will all prove critical for enacting regulations and research designs that emphasize equity.

Recent research suggests that the genotype of one individual in a friendship pair is predictive of the genotype of his/her friend. These results provide tentative support for the genetic homophily perspective, which has important implications for social and genetic epidemiology because it substantiates a particular form of gene–environment correlation. This process may also have important implications for social scientists who study the social factors related to health and health-related behaviors. We extend this work by considering the ways in which school context shapes genetically similar friendships. Using the network, school, and genetic information from the National Longitudinal Study of Adolescent Health, we show that genetic homophily for the TaqI A polymorphism within the DRD2 gene is stronger in schools with greater levels of inequality. Our results suggest that individuals with similar genotypes may not actively select into friendships; rather, they may be placed into these contexts by institutional mechanisms outside of their control. Our work highlights the fundamental role played by broad social structures in the extent to which genetic factors explain complex behaviors, such as friendships.