Significance Genome-wide association study (GWAS) discoveries about educational attainment have raised questions about the meaning of the genetics of success. These discoveries could offer clues about biological mechanisms or, because children inherit genetics and social class from parents, education-linked genetics could be spurious correlates of socially transmitted advantages. To distinguish between these hypotheses, we studied social mobility in five cohorts from three countries. We found that people with more education-linked genetics were more successful compared with parents and siblings. We also found mothers’ education-linked genetics predicted their children’s attainment over and above the children’s own genetics, indicating an environmentally mediated genetic effect. Findings reject pure social-transmission explanations of education GWAS discoveries. Instead, genetics influences attainment directly through social mobility and indirectly through family environments.

Abstract A summary genetic measure, called a “polygenic score,” derived from a genome-wide association study (GWAS) of education can modestly predict a person’s educational and economic success. This prediction could signal a biological mechanism: Education-linked genetics could encode characteristics that help people get ahead in life. Alternatively, prediction could reflect social history: People from well-off families might stay well-off for social reasons, and these families might also look alike genetically. A key test to distinguish biological mechanism from social history is if people with higher education polygenic scores tend to climb the social ladder beyond their parents’ position. Upward mobility would indicate education-linked genetics encodes characteristics that foster success. We tested if education-linked polygenic scores predicted social mobility in >20,000 individuals in five longitudinal studies in the United States, Britain, and New Zealand. Participants with higher polygenic scores achieved more education and career success and accumulated more wealth. However, they also tended to come from better-off families. In the key test, participants with higher polygenic scores tended to be upwardly mobile compared with their parents. Moreover, in sibling-difference analysis, the sibling with the higher polygenic score was more upwardly mobile. Thus, education GWAS discoveries are not mere correlates of privilege; they influence social mobility within a life. Additional analyses revealed that a mother’s polygenic score predicted her child’s attainment over and above the child’s own polygenic score, suggesting parents’ genetics can also affect their children’s attainment through environmental pathways. Education GWAS discoveries affect socioeconomic attainment through influence on individuals’ family-of-origin environments and their social mobility.

Genetics and social class may be thought of as fundamental causes of life outcomes because they are present at the beginning of human development; they are associated with a range of important life outcomes; they affect these outcomes through many different pathways; and their influences persist across cultures and over time (1⇓⇓⇓–5). Twin studies and related designs establish that genetics influences education and social class (6, 7), but they are mostly silent on the nature of this influence (8, 9). Recently, genome-wide association studies (GWAS) have discovered molecular genetic associations with education (10⇓–12), a determinant of social class (13). Observations that genetics discovered in GWAS of education are associated with social-class origins and with socioeconomic attainments (14⇓⇓⇓–18) suggest these genetics and social class are connected. We considered three potential explanations for this connection.

One explanation for the connection between education-linked genetics and social class is that a person’s education-linked genetics have causal effects on their attainments. A person’s genetics cannot have a direct effect on their education. Instead, under this explanation, a person’s genetics would influence their development of traits and behaviors that, in turn, contribute to their educational success (16, 19). For example, education-linked genetics could influence brain development in ways that affect behavior, leading to differences in achievement in school and beyond. If this explanation is right, education-linked genetics could help us understand molecular and behavioral mechanisms of social attainment, including gene–environment interplay in which genetics influences the environments and opportunities children encounter as they grow up.

A second explanation for the connection between education-linked genetics and social class is that education-linked genetics carried by a person’s relatives have causal effects on that person’s attainments. Such effects could arise from genetic influences on parental characteristics that specifically affect their children, e.g., parental nurturance. They could also arise through genetic influences on attainment that affect children through processes of social transmission. Specifically, if genetics influence social attainment, children will inherit genetics that helped shape their parents’ social class along with their parents’ social class itself. In this way, genetics may influence a child’s attainment through effects on the child’s environment (20, 21). Such environmentally mediated genetic effects, in which parents’ genetics affects the household environment in ways that influence their children’s outcomes, are ruled out in genetic estimates from twin studies. However, they are not ruled out in GWAS (22, 23). Associations between a child’s education-linked genetics and their attainment could thus reflect genetic influences on the child’s traits and behaviors as well as effects of environments influenced by parents’ genetics. If this explanation is right, it would direct attention toward features of the family environment as mechanisms linking DNA with social attainment.

A third explanation for the connection between education-linked genetics and social class is that it is spurious, e.g., because education-linked genetics is a correlate of a privileged social inheritance, i.e., having well-off ancestors. For example, social positions established long ago might be passed down across generations via socially transmitted inheritances, such as wealth transfers (24, 25). Because people tend to have children with mates from the same social class (26, 27), these historical differences in social position could carry a genetic signature. To the extent this genetic signature is not captured by principal components used to adjust for ancestry-related confounding in GWAS, it could be detected in GWAS but would have little to do with traits and behaviors that influence achievement. If this explanation is right, rather than providing clues to causal genetic processes identified in family-based genetic studies, education GWAS discoveries would be merely of genealogical interest.

One way to test the hypothesis that education-linked genetics influence social-class outcomes is to test if having more education-linked genetics predicts upward social mobility, defined as achieving more socioeconomic success relative to one’s parents. By focusing on change in social position within a person’s own lifetime, the analysis can separate the genetic and social legacy a child is born with from the influence of their genetics on future attainment. For example, if education-linked genetics mainly reflect a legacy of social privilege, then controlling for the socioeconomic status of a child’s parents should reduce the association between the child’s genetics and their future social attainment to zero. In contrast, if genetic associations with attainment persist even after controlling for parents’ social class, this result would suggest that education-linked genetics influences social mobility.

If education-linked genetics influence social mobility, this raises the question of how one generation’s genetics may affect their children’s attainments. Direct transmission of genetics from parents to children is one path. However, a social–genetic effect in which parents’ genetics influence their children’s attainments through environmental pathways (28) is also possible. One way to test for such environmental transmission is to test if parental genetics predicts their children’s attainments over and above the child’s own genetics (22, 29).

We tested associations between education-linked genetics and social mobility in more than 20,000 individuals tracked over more than 1 million person-years of follow-up spanning birth through late life in five population-based longitudinal studies in the United States, the United Kingdom, and New Zealand (Fig. 1 and SI Appendix, 1.1–1.5 and Table S1). We measured education-linked genetics using the polygenic score method (30). This method uses GWAS results as a scoring algorithm to compute a summary measure of genome-wide genetic influences on a phenotype. We measured social-class origins using data on parents’ education, occupation, income, and financial difficulties. We analyzed social attainment in terms of education in adolescents, in terms of occupational attainment in young and midlife adults, and in terms of wealth in older adults. To test genetic associations with social attainment within a single lifetime, we first tested if participants’ polygenic scores for education predicted their social attainments. We next tested if participants’ polygenic scores were correlated with their social-class origins. This analysis tested for gene–environment correlations that could confound polygenic score associations with attainment. Finally, we tested polygenic score associations with social mobility by comparing the attainments of participants relative to their social-class origins. As a further analysis of mobility, we conducted sibling-difference analyses that tested if sibling differences in polygenic score predicted sibling differences in attainment. This analysis rules out confounding by any factors shared by siblings in a family that might not be captured in our measures of social-class origins. To test how parents’ genetics might influence their children’s attainments, we conducted mother–child social–genetic analysis. Specifically, we tested if mothers’ polygenic scores predicted their children’s attainments independent of the child’s own polygenic score. This analysis tested if parents’ genetics might influence their children’s attainments through mechanisms of environmental transmission.

Fig. 1. Cohorts included in analysis of social mobility. Sample sizes reflect participants of European descent with available genetic and attainment data.

Discussion We tested if genetics discovered in GWAS of educational attainment were related to socioeconomic mobility across the life course in five cohorts from the United States, Britain, and New Zealand. Across these studies, there were three consistent findings. First, education-linked genetics were related to social attainment: Children with higher education polygenic scores tended to complete more years of schooling, build more successful occupational careers, and accumulate more wealth. Second, there was a gene–environment correlation: Children with higher polygenic scores tended to grow up in socioeconomically better-off homes. Third, education-linked genetics were related to social mobility: Regardless of where they started in life, children with higher polygenic scores tended to move up the social ladder in terms of education, occupation, and wealth, even compared with siblings in their own families. These findings clarify how education-linked genetics and social class are connected. First, the findings argue against the explanation that the connection is spurious. The finding that participants’ education-linked genetics predicted change in their social position within their own lives, replicated across five cohorts in three countries, argues against the explanation that education-linked genetics are simply a correlate of a privileged social inheritance that escaped ancestry controls in GWAS. Instead, findings support the explanation that education-linked genetics are connected to social class because they influences attainment: Participants’ education-linked genetics predicted their social mobility, and differences in education-linked genetics between siblings predicted differences between siblings in life-course attainments. Second, the findings suggest that education-linked genetics may be connected to social class in part because education-linked genetics carried by a person’s relatives can influence that person’s own attainment. Genetic associations with attainments were attenuated when models accounted for participants’ social origins. This finding suggests that genetic associations with social attainment could arise, in part, from gene–environment correlations between participants’ education-linked genetics and environments related to participants’ social origins. Such gene–environment correlations could reflect effects of parents’ genetics on family environments, which parents subsequently give to their children along with genotypes (5, 21). Our social–genetic analysis of pairs of mothers and children found mothers’ polygenic scores predicted their children’s educational attainment independent of the children’s own polygenic scores. This finding is consistent with the hypothesis that parents’ education-linked genetics contribute to shaping the environments that influence their children’s subsequent attainment. We acknowledge limitations. First, our genetic measurement is imprecise. The education polygenic score explains only a fraction of the estimated total genetic influence on education (10). Our effect sizes are thus attenuated by substantial measurement error in the polygenic score. This bias toward the null makes our analysis a conservative estimate of genetic associations with social mobility. To provide an estimate of the extent of this bias, effect-size estimates corrected for measurement error using a recently proposed method (46, 47) are reported in SI Appendix, Table S7. The problem is more severe in analysis of non-Europeans (SI Appendix, 2.3). With larger GWAS sample sizes, new GWAS in non-European populations, and identification of which specific genetic variants are causal, these limitations will be partly mitigated (48, 49). Second, analyses do not completely exclude potential bias due to population stratification (50), the nonrandom patterning of genotypes across different ancestries. We used the best available methods to account for confounding by ancestry-related genetic differences that could be correlated with social attainment. We focused analyses on relatively genetically homogenous samples of individuals of European descent and further applied covariate adjustment for genetic principal components (SI Appendix, 1.6). Even so, it is possible that unmeasured population stratification could influence results. Sibling-difference analysis does exclude population stratification as a confounder (15, 51), establishing a floor for effect-size magnitudes. Third, the genetics of socioeconomic attainment and mobility may vary slightly across different birth cohorts, presumably reflecting changes in the social context of attainment (52). This could cause incomplete genetic correlation between mothers and their children and may introduce confounding into our social–genetic mother-child analysis. Fourth, we lack complete genetic information on the parents of the people whose lives we studied. In the E-Risk cohort, in which we analyzed maternal genetic data, fathers did not give DNA. Thus, we cannot fully isolate genetic from environmental mechanisms of intergenerational transmission. However, our designs do allow certain conclusions about the direct genetic effects of an individual’s own DNA on their attainment and about the socially transmitted genetic effects of a parent’s DNA on their child’s attainment. Sibling-difference analysis, which controls for the genetics of both parents, can test for direct genetic effects. Our sibling-difference analysis establishes a floor for the size of direct genetic effects and rules out purely social transmission as an explanation for the associations between children’s education-linked genetics and their attainment. Social–genetic analysis in which the child’s education is regressed on the polygenic scores of one parent and the child can test for socially transmitted genetic effects. Assuming the parent’s and child’s polygenic scores are measured with the same error, the effects of genetics transmitted from parent to child are captured by the child’s polygenic score; i.e., the child’s polygenic score acts as a control for the direct genetic effect. The residual association between the parent’s polygenic score and the child’s attainment can be interpreted as a socially transmitted parental genetic effect. Although we cannot rule out differences in measurement error between polygenic scores of mothers and their children, our social–genetic analysis provides some evidence for socially transmitted maternal genetic effects on children’s educational attainment, consistent with a recent analysis that included genetic information from both parents (22, 53). Against the background of these limitations, our analysis suggests three take-home messages. The first take-home message is that genetics research should incorporate information about social origins. For genetics, our findings suggest that estimates of genetic associations with socioeconomic achievement reflect direct genetic effects as well as the effects of social inheritance correlated with genetics. Future genetic studies of social attainment can refine inferences about direct genetic effects by including measures of social origins in their study designs. The same is true for genetic studies of other phenotypes, because childhood socioeconomic circumstances are implicated in the etiology of many different traits and health conditions (54⇓–56). Such analysis will help clarify interpretation of studies that analyzed GWAS data and found evidence of genetic overlap between educational attainment and several biomedical phenotypes (57, 58). The advent of national biobanks and other large genetic datasets is increasing the power of GWAS to map genetic risks. Research to investigate how much of the genetic risk measured from GWAS discoveries arises within a single generation and how much accrues from social inheritance correlated with genetics across successive generations is needed. The second take-home message is that social science research should incorporate information about genetic inheritance. For the social sciences, our findings provide molecular evidence across birth cohorts and countries of genetic influence on social attainment and social mobility. This evidence supports theory in the social sciences that frames genetics as one mechanism among several through which social position is transmitted across generations (9, 20, 21, 59). These theories imply that genetic factors can confound estimates of social environmental effects. However, because genetics have been difficult to measure, studies addressing these theories have had to estimate genetic contributions to attainment indirectly, while other social science research has simply ignored the problem. Now, genetically informed theories of social attainment and mobility can be revisited, tested, and elaborated using molecular genetic data available in an ever-growing array of genetically informed social surveys and longitudinal cohort studies. Beyond theory, integration of measured genetic inheritance into research on social mobility can add value in at least three ways. First, genetic controls can improve the precision of estimates of environmental effects (11, 14), e.g., of how features of parents’ social circumstances shape children’s development. Second, genetic measurements can provide a starting point for developmental investigations of pathways to social mobility (16, 60), e.g., to identify skills and behaviors that can serve as targets for environmental interventions to lift children out of poverty. Third, genetic measurements can be used to study gene–environment interplay; e.g., how policies and programs may strengthen or weaken genetic and nongenetic mechanisms of intergenerational transmission (61). In our analysis, modeling effects of social origins attenuated genetic-effect sizes by 10–50%, depending on the outcome and cohort. This variation is consistent with evidence that genetic influences on individual differences may vary across cultures and cohorts and across stages of the life course (62, 63). Research is needed to understand how molecular genetic effects on socioeconomic attainment may operate differently across environmental, historical, or economic contexts and the extent to which they may wax or wane across adult development. The third take-home message is that genetic analysis of social mobility can inform programs and policies that change children’s environments as a way to promote positive development. The genetics we studied are related to socioeconomic attainment and mobility partly through channels that are policy-malleable. Personal characteristics linked with the attainment-related genetics we studied involve early-emerging cognitive and noncognitive skills, including learning to talk and read, act planfully, delay gratification, and get along with others (10, 16). These skills represent intervention targets in their own right, for example by policies and programs that safeguard perinatal development and provide enriching, stable family and educational environments (64). A significant contribution of our study is that the nongenetic social and material resources children inherit from their parents represent a further mechanism linking genetics and attainment over the life course. Policies and programs cannot change children’s genes, but they can help give them more of the resources that children who inherit more education-linked genetics tend to grow up with. Our findings suggest that such interventions could help close the gap. The next step is to find out precisely what those resources are.

Conclusion A long-term goal of our sociogenomic research is to use genetics to reveal novel environmental intervention approaches to mitigating socioeconomic disadvantage. The analysis reported here takes one step toward enabling a study design to accomplish this. We found that measured genetics related to patterns of social attainment and mobility, partly through direct influences on individuals and partly through predicting the environments in which they grew up. Specifically, parents’ genetics influence the environments that give children their start in life, while children’s own genetics influence their social mobility across adult life. As we learn more about how genetics discovered in GWAS of education influence processes of human development that generate and maintain wealth and poverty, we can identify specific environments that shape those processes. Ultimately, this research approach can suggest interventions that change children’s environments to promote positive development across the life-course.

Methods Detailed descriptions of data and measures are included in SI Appendix, 1.1–1.5; analysis is described in SI Appendix, 1.6 and 1.7. Data Sources. Data were used from five studies: the E-Risk Study, the Add Health Study, the Dunedin Study, the WLS, and the US HRS. Polygenic Scoring. We computed polygenic scores for participants in the E-Risk Study, Add Health Study, Dunedin Study, WLS, and HRS based on all SNPs analyzed in the most recent Social Science Genetic Association Consortium (SSGAC) GWAS of educational attainment (12). No statistical significance threshold was applied to select SNPs for inclusion in polygenic score analysis. For the E-Risk Study and the Dunedin Study, polygenic scores were computed following the method described by Dudbridge (30) using the PRSice software (65). For the Add Health Study, WLS, and HRS, polygenic scores were computed by the SSGAC using the LD Pred software (66). The Add Health Study, WLS, and HRS data were included in the SSGAC GWAS of educational attainment. For each of these datasets, polygenic scores were computed using summary statistics from GWAS meta-analyses from which the target dataset for polygenic scoring was excluded. Within each dataset, we regressed SSGAC-computed polygenic scores on the first 10 principal components estimated from the genome-wide SNP data (67) and calculated residual values. Finally, we standardized these residual values to have mean = 0, SD = 1 within each dataset to form the final versions of the polygenic scores used for analysis. Socioeconomic Origins and Attainments. Participants’ social origins and socioeconomic attainments were measured from available data, with the aim of deriving measurements to compare social origins with attainments. Measurements are described briefly in Fig. 1. Other Measures. We measured educational attainment as GCSE level (0, 1, 2, or 3) in the E-Risk Study, as years of schooling in the Add Health Study, WLS, and HRS, and as a four-category variable coding the highest degree attained, as described previously (16), in the Dunedin Study. Analysis. We tested associations using linear regression models. For cohorts of mixed birth years (Add Health Study, HRS, and WLS), we included dummy variables for years of birth. For cohorts sampled from schools (Add Health Study and WLS), we included dummy variables for school in analyses of genetic associations with attainment and mobility. School dummies were not included in the analysis of gene–environment correlation with social origins. For cohorts including siblings or spouses (Add Health Study, HRS, and WLS), we clustered SEs at the family level. We conducted sibling-difference analyses using family fixed-effects regression (68⇓–70). For analysis, we denominated polygenic scores and outcome variables in units of sample-specific SDs. We refer to effect sizes denominated in this metric as “r” to reflect their parallel interpretation with Pearson correlations. We used this same standardization for sibling-difference analysis so that effect sizes can be compared between full-sample and sibling-difference models.

Acknowledgments We thank David Corcoran, Joseph Prinz, Karen Sugden, and Benjamin Williams for assistance with E-Risk Study and Dunedin Study genetics data; Christy Avery, Heather Highland, and Joyce Tabor for assistance with the Add Health Study genetics data; David Braudt for assistance with Add Health Study occupational data; and Dan Benjamin and David Cesarini for comments on the article. This study used data from the E-Risk Study, the Add Health Study, the Dunedin Study, the HRS, and the WLS. The E-Risk Study is supported by UK Medical Research Council Grant G1002190 and Eunice Kennedy Shriver National Institute of Child Health and Human Development Grant R01HD077482. The Add Health Study is supported by Eunice Kennedy Shriver National Institute of Child Health and Human Development Grant P01HD31921 and GWAS Grants R01HD073342 and R01HD060726, with cooperative funding from 23 other federal agencies and foundations. The Dunedin Study is supported by the New Zealand Health Research Council, New Zealand Ministry of Business, Innovation, and Employment, National Institute on Aging Grant R01AG032282, and UK Medical Research Council Grant MR/P005918/1. The HRS is supported by National Institute on Aging Grants U01AG009740, RC2AG036495, and RC4AG039029 and is conducted by the University of Michigan. The WLS is supported by National Institute on Aging Grants R01AG041868 and P30AG017266. This research received additional support from National Institute on Aging Grant R24AG04506 and Russell Sage and Ford Foundation Grant 961704. D.W.B. is supported by a Jacobs Foundation Early Career Research Fellowship and by National Institute on Aging Grants R01AG032282 and P30AG028716. R.W. is supported by National Science Foundation Grant DGE1144083. L.A. is an Economic and Social Research Council Heath Leadership Fellow. This research benefitted from GWAS results made publicly available by the SSGAC. Some of the work used a high-performance computing facility partially supported by North Carolina Biotechnology Center Grant 2016-IDG-1013.

Footnotes Author contributions: D.W.B., B.W.D., J.D.B., A.C., D.C., J.M.F., J.F., T.E.M., J.W., and K.M.H. designed research; D.W.B., B.W.D., R.W., L.A., A.C., J.F., P.H., T.E.M., R.P., K.S., and K.M.H. performed research; D.W.B. and B.W.D. analyzed data; and D.W.B., B.W.D., A.C., T.E.M., and K.M.H. wrote the paper.

Reviewers: C.S.J., Harvard University; and E.M.T.-D., University of Texas at Austin.

The authors declare no conflict of interest.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1801238115/-/DCSupplemental.