Schools as Moderators of Genetic Associations with Life Course Attainments: Evidence from the WLS and Add Health

Exploring the Fetal Origins Hypothesis Using Genetic Data

Birth weight is a robust predictor of valued life course outcomes, emphasizing the importance of prenatal development. But does birth weight act as a proxy for environmental conditions in utero, or do biological processes surrounding birth weight themselves play a role in healthy development? To answer this question, we leverage variation in birth weight that is, within families, orthogonal to prenatal environmental conditions: one's genes. We construct polygenic scores in two longitudinal studies (Born in Bradford, N = 2008; Wisconsin Longitudinal Study, N = 8488) to empirically explore the molecular genetic correlates of birth weight. A 1 standard deviation increase in the polygenic score is associated with an ~100-grams increase in birth weight and a 1.4 pp (22 percent) decrease in low birth weight probability. Sibling comparisons illustrate that this association largely represents a causal effect. The polygenic score-birth weight association is increased for children who spend longer in the womb and whose mothers have higher body mass index, though we find no differences across maternal socioeconomic status. Finally, the polygenic score affects social and cognitive outcomes, suggesting that birth weight is itself related to healthy prenatal development.

Implications of the genomic revolution for education research and policy

Research at the intersection of social science and genomics, 'sociogenomics', is transforming our understanding of the interplay between genomics, individual outcomes and society. It has interesting and maybe unexpected implications for education research and policy. Here we review the growing sociogenomics literature and discuss its implications for educational researchers and policymakers. We cover key concepts and methods in genomic research into educational outcomes, how genomic data can be used to investigate social or environmental effects, the methodological strengths and limitations of genomic data relative to other observational social data, the role of intergenerational transmission and potential policy implications. The increasing availability of genomic data in studies can produce a wealth of new evidence for education research. This may provide opportunities for disentangling the environmental and genomic factors that influence educational outcomes and identifying potential mechanisms for intervention.

Gene-environment interaction analysis of school quality and educational inequality

We study to what extent schools increase or decrease environmental and genetic influences on educational performance. Building on behavioral genetics literature on gene-environment interactions and sociological literature on the compensating and amplifying effects of schools on inequality, we investigate whether the role of genes and the shared environment is larger or smaller in higher-quality school environments. We apply twin models to Dutch administrative data on the educational performance of 18,384 same-sex and 11,050 opposite-sex twin pairs, enriched with data on the quality of primary schools. Our results show that school quality does not moderate genetic and shared-environmental influences on educational performance once the moderation by SES is considered. We find a gene-environment interplay for school SES: genetic variance decreases with increasing school SES. This school SES effect partly reflects parental SES influences. Yet, parental SES does not account for all the school SES moderation, suggesting that school-based processes play a role too.

Overcoming attenuation bias in regressions using polygenic indices

Measurement error in polygenic indices (PGIs) attenuates the estimation of their effects in regression models. We analyze and compare two approaches addressing this attenuation bias: Obviously Related Instrumental Variables (ORIV) and the PGI Repository Correction (PGI-RC). Through simulations, we show that the PGI-RC performs slightly better than ORIV, unless the prediction sample is very small (N < 1000) or when there is considerable assortative mating. Within families, ORIV is the best choice since the PGI-RC correction factor is generally not available. We verify the empirical validity of the simulations by predicting educational attainment and height in a sample of siblings from the UK Biobank. We show that applying ORIV between families increases the standardized effect of the PGI by 12% (height) and by 22% (educational attainment) compared to a meta-analysis-based PGI, yet estimates remain slightly below the PGI-RC estimates. Furthermore, within-family ORIV regression provides the tightest lower bound for the direct genetic effect, increasing the lower bound for the standardized direct genetic effect on educational attainment from 0.14 to 0.18 (+29%), and for height from 0.54 to 0.61 (+13%) compared to a meta-analysis-based PGI.

The nature-nurture of academic achievement at the intersection between gender, family background, and school context

We investigate the role of gender, family SES, school SES, and their intersection in educational achievement using a twin design. Drawing on theories of gene-environment interaction, we test whether high-SES environments compensate genetic risks or enhance genetic potential, and its dependency on gender. Using data on 37,000 Danish twin and sibling pairs from population-wide administrative registers, we report three main findings. First, for family SES, but not for school SES, we find that genetic influences play a slightly smaller role in high-SES environments. Second, this relationship is moderated by child gender: in high-SES families, the genetic influence is considerably lower for boys than for girls. Third, the moderating effect of family SES for boys is almost entirely driven by children attending low-SES schools. Our findings thus point to significant heterogeneity in gene-environment interactions, highlighting the importance of considering the multiplicity of social contexts.

Schooling substantially improves intelligence, but neither lessens nor widens the impacts of socioeconomics and genetics

Schooling, socioeconomic status (SES), and genetics all impact intelligence. However, it is unclear to what extent their contributions are unique and if they interact. Here we used a multi-trait polygenic score for cognition (cogPGS) with a quasi-experimental regression discontinuity design to isolate how months of schooling relate to intelligence in 6567 children (aged 9-11). We found large, independent effects of schooling (β ~ 0.15), cogPGS (β ~ 0.10), and SES (β ~ 0.20) on working memory, crystallized (cIQ), and fluid intelligence (fIQ). Notably, two years of schooling had a larger effect on intelligence than the lifetime consequences, since birth, of SES or cogPGS-based inequalities. However, schooling showed no interaction with cogPGS or SES for the three intelligence domains tested. While schooling had strong main effects on intelligence, it did not lessen, nor widen the impact of these preexisting SES or genetic factors.

A population-wide gene-environment interaction study on how genes, schools, and residential areas shape achievement

A child's environment is thought to be composed of different levels that interact with their individual genetic propensities. However, studies have not tested this theory comprehensively across multiple environmental levels. Here, we quantify the contributions of child, parent, school, neighbourhood, district, and municipality factors to achievement, and investigate interactions between polygenic indices for educational attainment (EA-PGI) and environmental levels. We link population-wide administrative data on children's standardised test results, schools and residential identifiers to the Norwegian Mother, Father, and Child Cohort Study (MoBa), which includes >23,000 genotyped parent-child trios. We test for gene-environment interactions using multilevel models with interactions between EA-PGI and random effects for school and residential environments (thus remaining agnostic to specific features of environments). We use parent EA-PGI to control for gene-environment correlation. We found an interaction between students' EA-PGI and schools suggesting compensation: higher-performing schools can raise overall achievement without leaving children with lower EA-PGI behind. Differences between schools matter more for students with lower EA-PGI, explaining 4 versus 2% of the variance in achievement for students 2 SD below versus 2 SD above the mean EA-PGI. Neighbourhood, district, and municipality variation contribute little to achievement (<2% of the variance collectively), and do not interact with children's individual EA-PGI. Policy to reduce social inequality in achievement in Norway should focus on tackling unequal support across schools for children with difficulties.

Ubiquitous bias and false discovery due to model misspecification in analysis of statistical interactions: The role of the outcome's distribution and metric properties

Studies of interaction effects are of great interest because they identify crucial interplay between predictors in explaining outcomes. Previous work has considered several potential sources of statistical bias and substantive misinterpretation in the study of interactions, but less attention has been devoted to the role of the outcome variable in such research. Here, we consider bias and false discovery associated with estimates of interaction parameters as a function of the distributional and metric properties of the outcome variable. We begin by illustrating that, for a variety of noncontinuously distributed outcomes (i.e., binary and count outcomes), attempts to use the linear model for recovery leads to catastrophic levels of bias and false discovery. Next, focusing on transformations of normally distributed variables (i.e., censoring and noninterval scaling), we show that linear models again produce spurious interaction effects. We provide explanations offering geometric and algebraic intuition as to why interactions are a challenge for these incorrectly specified models. In light of these findings, we make two specific recommendations. First, a careful consideration of the outcome's distributional properties should be a standard component of interaction studies. Second, researchers should approach research focusing on interactions with heightened levels of scrutiny. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

How interactions between ADHD and schools affect educational achievement: a family-based genetically sensitive study

BACKGROUND

Children with ADHD tend to achieve less than their peers in school. It is unknown whether schools moderate this association. Nonrandom selection of children into schools related to variations in their ADHD risk poses a methodological problem.

METHODS

We linked data on ADHD symptoms of inattention and hyperactivity and parent-child ADHD polygenic scores (PGS) from the Norwegian Mother, Father, and Child Cohort Study (MoBa) to achievement in standardised tests and school identifiers. We estimated interactions of schools with individual differences between students in inattention, hyperactivity, and ADHD-PGS using multilevel models with random slopes for ADHD effects on achievement over schools. In our PGS analyses, we adjust for parental selection of schools by adjusting for parental ADHD-PGS (a within-family PGS design). We then tested whether five school sociodemographic measures explained any interactions.

RESULTS

Analysis of up to 23,598 students attending 2,579 schools revealed interactions between school and ADHD effects on achievement. The variability between schools in the effects of inattention, hyperactivity and within-family ADHD-PGS on achievement was 0.08, 0.07 and 0.05 SDs, respectively. For example, the average effect of inattention on achievement was β = -0.23 (SE = 0.009), but in 2.5% of schools with the weakest effects, the value was -0.07 or less. ADHD has a weaker effect on achievement in higher-performing schools. Schools make more of a difference to the achievements of students with higher levels of ADHD, explaining over four times as much variance in achievement for those with high versus average inattention symptoms. School sociodemographic measures could not explain the ADHD-by-school interactions.

CONCLUSIONS

Although ADHD symptoms and genetic risk tend to hinder achievement, schools where their effects are weaker do exist. Differences between schools in support for children with ADHD should be evened out.

Polygenic Scores for Plasticity: A New Tool for Studying Gene-Environment Interplay

Fertility, health, education, and other outcomes of interest to demographers are the product of an individual's genetic makeup and their social environment. Yet, gene × environment (G×E) research deploys a limited toolkit on the genetic side to study the gene-environment interplay, relying on polygenic scores (PGSs) that reflect the influence of genetics on levels of an outcome. In this article, we develop a genetic summary measure better suited for G×E research: variance polygenic scores (vPGSs), which are PGSs that reflect genetic contributions to plasticity in outcomes. First, we use the UK Biobank (N ∼ 408,000 in the analytic sample) and the Health and Retirement Study (N ∼ 5,700 in the analytic sample) to compare four approaches to constructing PGSs for plasticity. The results show that widely used methods for discovering which genetic variants affect outcome variability fail to serve as distinctive new tools for G×E. Second, using the PGSs that do capture distinctive genetic contributions to plasticity, we analyze heterogeneous effects of a UK education reform on health and educational attainment. The results show the properties of a useful new tool for population scientists studying the interplay of nature and nurture and for population-based studies that are releasing PGSs to applied researchers.

Challenging the utility of polygenic scores for social science: Environmental confounding, downward causation, and unknown biology

The sociogenomics revolution is upon us, we are told. Whether revolutionary or not, sociogenomics is poised to flourish given the ease of incorporating polygenic scores (or PGSs) as "genetic propensities" for complex traits into social science research. Pointing to evidence of ubiquitous heritability and the accessibility of genetic data, scholars have argued that social scientists not only have an opportunity but a duty to add PGSs to social science research. Social science research that ignores genetics is, some proponents argue, at best partial and likely scientifically flawed, misleading, and wasteful. Here, I challenge arguments about the value of genetics for social science and with it the claimed necessity of incorporating PGSs into social science models as measures of genetic influences. In so doing, I discuss the impracticability of distinguishing genetic influences from environmental influences because of non-causal gene-environment correlations, especially population stratification, familial confounding, and downward causation. I explain how environmental effects masquerade as genetic influences in PGSs, which undermines their raison d'être as measures of genetic propensity, especially for complex socially contingent behaviors that are the subject of sociogenomics. Additionally, I draw attention to the partial, unknown biology, while highlighting the persistence of an implicit, unavoidable reductionist genes versus environments approach. Leaving sociopolitical and ethical concerns aside, I argue that the potential scientific rewards of adding PGSs to social science are few and greatly overstated and the scientific costs, which include obscuring structural disadvantages and cultural influences, outweigh these meager benefits for most social science applications.

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

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.

Genetic effects on educational attainment in Hungary

INTRODUCTION

Educational attainment is a substantially heritable trait, and it has recently been linked to specific genetic variants by genome-wide association studies (GWASs). However, the effects of such genetic variants are expected to vary across environments, including countries and historical eras.

METHODS

We used polygenic scores (PGSs) to assess molecular genetic effects on educational attainment in Hungary, a country in the Central Eastern European region where behavioral genetic studies are in general scarce and molecular genetic studies of educational attainment have not been previously published.

RESULTS

We found that the PGS is significantly associated with the attainment of a college degree as well as the number of years in education in a sample of Hungarian study participants (N = 829). PGS effect sizes were not significantly different when compared to an English (N = 976) comparison sample with identical measurement protocols. In line with previous Estonian findings, we found higher PGS effect sizes in Hungarian, but not in English participants who attended higher education after the fall of Communism, although we lacked statistical power for this effect to reach significance.

DISCUSSION

Our results provide evidence that polygenic scores for educational attainment have predictive value in culturally diverse European populations.

Gene-environment Interactions and School Tracking during Secondary Education: Evidence from the U.S

There is much evidence to suggest that family background and the context of secondary education both contribute to the formation of educational inequalities. Meanwhile, our knowledge about the role of ability in generating class differences in educational outcomes is still limited. By deploying genetic data that allow us to measure at least part of "innate" ability inherited through biological mechanisms from parents, this study examines how such abilities are associated with educational tracking outcomes among U.S. high schoolers. This study also details our understanding of the role of nature and nurture in the educational attainment processes by testing for gene-environment interactions-that is, a joint, mutually moderating effect of one's genetic potential and one's environment (e.g., family background or school context) on phenotypic outcomes (educational tracking). Using the National Longitudinal Study of Adolescent to Adult Health that collects a unique set of demographic, educational, and genetic characteristics of students, we report the following results: First, a positive association between the genetic potential for educational attainment and taking advanced courses holds even after controlling for previous course tracking measures. Second, results provide suggestive evidence that parental SES amplifies the association between one's genetic potential for educational attainment and mathematics tracking. In contrast to the argument by some stratification scholars that places primary emphasis on the role of social background for the reproduction of educational stratification, the present findings imply that we need to fully consider the role of genetic inheritance for educational stratification in addition to social origin.

Educational attainment polygenic score predicts inhibitory control and academic skills in early and middle childhood

Inhibitory control skills are important for academic outcomes across childhood, but it is unknown whether inhibitory control is implicated in the association between genetic variation and academic performance. This study examined the relationship between a GWAS-based (EduYears) polygenic score indexing educational attainment (EA PGS) and inhibitory control in early (Mage  = 3.80 years) and middle childhood (Mage  = 9.18 years), and whether inhibitory control in early childhood mediated the relation between EA PGS and academic skills. The sample comprised 731 low-income and racially/ethnically diverse children and their families from the longitudinal early steps multisite study. EA PGS predicted middle childhood inhibitory control (estimate = 0.09, SE = 0.05, p < 0.05) and academic skills (estimate = 0.18, SE = 0.05, p < 0.01) but did not predict early childhood inhibitory control (estimate = 0.08, SE = 0.05, p = 0.11); thus, mediation was not tested. Sensitivity analyses showed that effect sizes were similar across European and African American groups. This study suggests that inhibitory control could serve as a potential mechanism linking genetic differences to educational outcomes.

Polygenic Scores in Developmental Psychology: Invite Genetics In, Leave Biodeterminism Behind

Polygenic scores offer developmental psychologists new methods for integrating genetic information into research on how people change and develop across the life span. Indeed, polygenic scores have correlations with developmental outcomes that rival correlations with traditional developmental psychology variables, such as family income. Yet linking people's genetics with differences between them in socially valued developmental outcomes, such as educational attainment, has historically been used to justify acts of state-sponsored violence. In this review, we emphasize that an interdisciplinary understanding of the environmental and structural determinants of social inequality, in conjunction with a transactional developmental perspective on how people interact with their environments, is critical to interpreting associations between polygenic measures and phenotypes. While there is a risk of misuse, early applications of polygenic scores to developmental psychology have already provided novel findings that identify environmental mechanisms of life course processes that can be used to diagnose inequalities in social opportunity.

Separating Measured Genetic and Environmental Effects: Evidence Linking Parental Genotype and Adopted Child Outcomes

There has been widespread adoption of genome wide summary scores (polygenic scores) as tools for studying the importance of genetics and associated life course mechanisms across a range of demographic and socioeconomic outcomes. However, an often unacknowledged issue with these studies is that parental genetics impact both child environments and child genetics, leaving the effects of polygenic scores difficult to interpret. This paper uses multi-generational data containing polygenic scores for parents (n = 7193) and educational outcomes for adopted (n = 855) and biological (n = 20,939) children, many raised in the same families, which allows us to separate the influence of parental polygenic scores on children outcomes between environmental (adopted children) and environmental and genetic (biological children) effects. Our results complement recent work on "genetic nurture" by showing associations of parental polygenic scores with adopted children's schooling, providing additional evidence that polygenic scores combine genetic and environmental influences and that research designs are needed to separate these estimated impacts.

Separating Measured Genetic and Environmental Effects: Evidence Linking Parental Genotype and Adopted Child Outcomes

There has been widespread adoption of genome wide summary scores (polygenic scores) as tools for studying the importance of genetics and associated life course mechanisms across a range of demographic and socioeconomic outcomes. However, an often unacknowledged issue with these studies is that parental genetics impact both child environments and child genetics, leaving the effects of polygenic scores difficult to interpret. This paper uses multi-generational data containing polygenic scores for parents (n = 7193) and educational outcomes for adopted (n = 855) and biological (n = 20,939) children, many raised in the same families, which allows us to separate the influence of parental polygenic scores on children outcomes between environmental (adopted children) and environmental and genetic (biological children) effects. Our results complement recent work on "genetic nurture" by showing associations of parental polygenic scores with adopted children's schooling, providing additional evidence that polygenic scores combine genetic and environmental influences and that research designs are needed to separate these estimated impacts.

Interactions between Polygenic Scores and Environments: Methodological and Conceptual Challenges

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.

The Earliest Origins of Genetic Nurture: The Prenatal Environment Mediates the Association Between Maternal Genetics and Child Development

Observed genetic associations with educational attainment may be due to direct or indirect genetic influences. Recent work highlights genetic nurture, the potential effect of parents' genetics on their child's educational outcomes via rearing environments. To date, few mediating childhood environments have been tested. We used a large sample of genotyped mother-child dyads (N = 2,077) to investigate whether genetic nurture occurs via the prenatal environment. We found that mothers with more education-related genes are generally healthier and more financially stable during pregnancy. Further, measured prenatal conditions explain up to one third of the associations between maternal genetics and children's academic and developmental outcomes at the ages of 4 to 7 years. By providing the first evidence of prenatal genetic nurture and showing that genetic nurture is detectable in early childhood, this study broadens our understanding of how parental genetics may influence children and illustrates the challenges of within-person interpretation of existing genetic associations.

Can education be personalised using pupils' genetic data?

The increasing predictive power of polygenic scores for education has led to their promotion by some as a potential tool for genetically informed policy. How accurately polygenic scores predict an individual pupil's educational performance conditional on other phenotypic data is however not well understood. Using data from a UK cohort study with data linkage to national schooling records, we investigated how accurately polygenic scores for education predicted pupils' test score achievement. We also assessed the performance of polygenic scores over and above phenotypic data that are available to schools. Across our sample, there was high overlap between the polygenic score and achievement distributions, leading to poor predictive accuracy at the individual level. Prediction of educational outcomes from polygenic scores were inferior to those from parental socioeconomic factors. Conditional on prior achievement, polygenic scores failed to accurately predict later achievement. Our results suggest that while polygenic scores can be informative for identifying group level differences, they currently have limited use for accurately predicting individual educational performance or for personalised education.

Genetic associations with mathematics tracking and persistence in secondary school

Maximizing the flow of students through the science, technology, engineering, and math (STEM) pipeline is important to promoting human capital development and reducing economic inequality. A critical juncture in the STEM pipeline is the highly cumulative sequence of secondary school math courses. Students from disadvantaged schools are less likely to complete advanced math courses. Here, we conduct an analysis of how the math pipeline differs across schools using student polygenic scores, which are DNA-based indicators of propensity to succeed in education. We integrated genetic and official school transcript data from over 3000 European-ancestry students from U.S. high schools. We used polygenic scores as a molecular tracer to understand how the flow of students through the high school math pipeline differs in socioeconomically advantaged versus disadvantaged schools. Students with higher education polygenic scores were tracked to more advanced math already at the beginning of high school and persisted in math for more years. Analyses using genetics as a molecular tracer revealed that the dynamics of the math pipeline differed by school advantage. Compared to disadvantaged schools, advantaged schools buffered students with low polygenic scores from dropping out of math. Across all schools, even students with exceptional polygenic scores (top 2%) were unlikely to take the most advanced math classes, suggesting substantial room for improvement in the development of potential STEM talent. These results link new molecular genetic discoveries to a common target of educational-policy reforms.

The social and genetic inheritance of educational attainment: Genes, parental education, and educational expansion

Recently, several genome-wide association studies of educational attainment have found education-related genetic variants and enabled the integration of human inheritance into social research. This study incorporates the newest education polygenic score (Lee et al., 2018) into sociological research, and tests three gene-environment interaction hypotheses on status attainment. Using the Health and Retirement Study (N = 7599), I report three findings. First, a standard deviation increase in the education polygenic score is associated with a 58% increase in the likelihood of advancing to the next level of education, while a standard deviation increase in parental education results in a 53% increase. Second, supporting the Saunders hypothesis, the genetic effect becomes 11% smaller when parental education is one standard deviation higher, indicating that highly educated parents are more able to preserve their family's elite status in the next generation. Finally, the genetic effect is slightly greater for the younger cohort (1942-59) than the older cohort (1920-41). The findings strengthen the existing literature on the social influences in helping children achieve their innate talents.

Implications of gendered behaviour and contexts for social mobility in the USA: a nationally representative observational study

BACKGROUND

We constructed measures of an individual's gendered behaviour and their gendered environment to investigate the salience of gender norms during adolescence for social mobility during the next decade of life.

METHODS

In this nationally representative observational study, we collected individual-level data from the National Longitudinal Study of Adolescent to Adult Health (Add Health), which enrolled a cohort of nationally representative school students aged 11-19 years from across the USA and followed them up for 14 years (ie, to age 25-33 years). We characterised gendered behaviour for adolescents in a performative sense via self-reports of behaviours and beliefs. We aggregated this individual-level measure to create a proxy measure of an individual's social context by taking averages for an individual's peers of the same sex and school year.

FINDINGS

Between Jan 5, 1994, and Dec 26, 1995, Add Health collected data on a cohort of 20 745 students. 14 540 respondents were followed-up 14 years later between April 3, 2007, and Feb 1, 2009, of whom 7722 (53·1%) were female. More masculine male respondents were downwardly mobile; they were enrolled in school for fewer years and were more likely to have lower status jobs than their less masculine same-sex school peers. More masculine male respondents were also more likely to have jobs in occupational categories with larger proportions of males than their same-sex school peers. Gendered behaviour was not predictive of future educational and occupational attainment for female respondents. Male adolescents in school years with more masculine same-sex peers than male adolescents in other school years also tended to have lower educational and occupational attainment than their male peers. Educational and occupational attainment in early midlife for female respondents was not affected by their gendered environment.

INTERPRETATION

Gender, when measured as a set of gender-distinct behaviours in adolescence, was associated with differential patterns of social mobility from adolescence to young adulthood. Moreover, variation in an individual's local gender norms has implications for subsequent socioeconomic attainment, especially for male adolescents. These findings have potential implications for observed health disparities.

FUNDING

Bill & Melinda Gates Foundation.

Genetic architecture of socioeconomic outcomes: Educational attainment, occupational status, and wealth

This study takes a socio-genomic approach to examine the complex relationships among three important socioeconomic outcomes: educational attainment, occupational status, and wealth. Using more than 8,000 genetic samples from the Health and Retirement study, it first estimates the collective influence of genetic variants across the whole human genome to each of the three socioeconomic outcomes. It then tests genetic correlations among three socioeconomic outcomes, and examines the extent to which genetic influences on occupational status and wealth are mediated by educational attainment. Analyses using the genomic-relatedness-matrix restricted maximum likelihood method show significant genetic correlations among the three outcomes, and provide evidence for both mediated and independent genetic influences. A polygenic score analysis demonstrates the utility of findings in socio-genomic studies to address genetic confounding in causal relationships among the three socioeconomic outcomes.

The pathways from parental and neighbourhood socioeconomic status to adolescent educational attainment: An examination of the role of cognitive ability, teacher assessment, and educational expectations

Adolescents with high educational attainment generally have better outcomes across the lifespan than adolescents with lower educational attainment. This study investigated how three measures of socioeconomic status (SES)–maternal education, paternal education, and neighbourhood SES–combined to predict adolescent educational attainment (track level at age 17). We proposed three mechanisms for this pathway: cognitive ability (at age 11), primary school teacher assessment (stating the secondary education level suitable for a child at age 11), and educational expectations (at age 14). Using the data of 2,814 Dutch adolescents from the Prevention and Incidence of Asthma and Mite Allergy (PIAMA) study, logistic regressions tested associations between SES and educational attainment. Structural equation modelling (SEM) tested mediational pathways between SES and educational attainment. In models with three SES measures, having a medium-educated mother was associated with higher educational attainment relative to having a low-educated mother (OR; 95% CI: 1.83; 1.41–2.38), and having a high-educated mother was associated with higher educational attainment relative to having a low-educated mother (OR; 95% CI: 3.44; 2.59–4.55). The odds ratios for paternal education showed a similar pattern. We found no association between neighbourhood SES and adolescent educational attainment, so neighbourhood SES was removed from further analyses. Mediational analyses revealed that cognitive ability (30.0%), teacher assessment (28.5%), and educational expectations (1.2%) explained 59.8% of the total association between parental SES and educational attainment. The results showed that mother education and father education were both important for understanding the strong association between parental SES and adolescent educational attainment. In the Netherlands, the association between parental SES and educational attainment can be largely explained by cognitive ability and teacher assessments.

Genetic nature or genetic nurture? Introducing social genetic parameters to quantify bias in polygenic score analyses

Results from a genome-wide association study (GWAS) can be used to generate a polygenic score (PGS), an individual-level measure summarizing identified genetic influence on a trait dispersed across the genome. For complex, behavioral traits, the association between an individual's PGS and their phenotype may contain bias (from geographic, ancestral, and/or socioeconomic confounding) alongside the causal effect of the individual's genes. We formalize the introduction of a different source of bias in regression models using PGSs: the effects of parental genes on offspring outcomes, known as genetic nurture. GWAS do not discriminate between the various pathways through which genes become associated with outcomes, meaning existing PGSs capture both direct genetic effects and genetic nurture effects. We construct a theoretical model for genetic effects and show that the presence of genetic nurture biases PGS coefficients from both naïve OLS (between-family) and family fixed effects (within-family) regressions. This bias is in opposite directions; while naïve OLS estimates are biased away from zero, family fixed effects estimates are biased toward zero. We quantify this bias using two novel parameters: (1) the genetic correlation between the direct and nurture effects and (2) the ratio of the SNP heritabilities for the direct and nurture effects.