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Published in final edited form as: Psychol Sci. 2017 Jul 17;28(9):1302–1310. doi: 10.1177/0956797617707270

Genetic influence on intergenerational educational attainment

Ziada Ayorech 1, Eva Krapohl 1, Robert Plomin 1, Sophie von Stumm 2,*
PMCID: PMC5595239  NIHMSID: NIHMS866970  PMID: 28715641

Abstract

Using twin (6,105 twin pairs) and genomic (5,825 unrelated individuals) analyses, we tested for genetic influences on the parent-offspring correspondence in educational attainment. Genetics accounted for nearly half of the variance in intergenerational educational attainment. A genome-wide polygenic score (GPS) for years of education from a recent genome-wide association study (Okbay et al., 2016) was also associated with intergenerational educational attainment: The highest and lowest GPS means were found for offspring in stably educated (M = 0.43; SD=0.97) and stably uneducated (M = −0.19; SD= 0.97) families, while the GPS scores fell in between for families that were upwardly mobile (parents not university educated, offspring taking A-levels) (M = 0.05; SD = 0.96) and downwardly mobile (parents university educated, offspring not taking A-levels) (M = 0.28; SD = 1.03). Genetic influences on intergenerational educational attainment can be viewed as an index of equality of educational opportunity.

Keywords: intergenerational educational attainment, twin studies, behavioral genetics, polygenic score

Introduction

Educational attainment is key to a wide range of life outcomes including employability, health and even life expectancy (Bradley & Corwyn, 2002). Despite the profound benefits accrued by educational qualifications, access to education — especially higher education — remains unequal (Boliver, 2013). Specifically, children whose parents obtained university degrees are more likely to register for higher education than children from less educated family backgrounds (Blanden & Machin, 2004).

Traditionally, differences in educational access have been attributed to socio-political constraints that result from social inequalities. Indeed, parents with higher educational backgrounds have access to greater material and social resources that enable them to afford better opportunities for their children than less educated parents (Bradley & Cowyn, 2002). For example, children from better educated families have more learning support in primary and secondary school, and they also receive tailored advice when preparing for university entry (Mackenbach et al., 2008; Pomerantz & Moorman, 2010).

A less frequently investigated factor that may contribute to parent-offspring similarity in educational attainment is genetics. Ubiquitous genetic influence is widely accepted for psychological traits (Knopik, Neiderheiser, DeFries, & Plomin, 2017), including academic achievement throughout the school years (Rimfeld, Ayorech, Dale, Kovas, & Plomin, 2016). However, the mechanisms contributing to intergenerational phenotypes are less understood.

One of the most widely studied parent-offspring associations is the link between family socioeconomic status (SES) and children’s educational outcomes (Sirin, 2005). Although this correlation is often interpreted causally, such that SES-related environmental factors directly cause the differences in children’s educational achievement, it is at least partly due to genetic influence (Krapohl & Plomin, 2015; Trzaskowski et al., 2014).

A game-changer for genetic research in psychology is the genome-wide polygenic score (GPS), which can be used to estimate genetic strengths and weaknesses of unrelated individuals from DNA (Plomin & Simpson, 2013; Wray et al., 2014). A GPS aggregates the effects of thousands of DNA variants that were identified in corresponding genome-wide association (GWA) studies. A GPS from the first GWA study of years of education with more than 100,000 individuals (Rietveld et al., 2013) accounted for ~2% of the variance in years of education in independent samples, for 2.5% of the variance in family SES, and for almost 3% of the variance in children’s educational achievement (Krapohl & Plomin, 2015). This GPS based on the 2013 GWA report has demonstrated predictive potential for several socioeconomic outcomes including mobility (Belsky et al., 2016; Conley et al., 2015; Domingue, Belsky, Conley, Harris, & Boardman, 2015), although effect sizes were small.

Specifically, two recent studies showed that adolescents’ GPS (Rietveld et al., 2013) predicted educational attainment between and within families (Conley et al., 2015; Domingue et al., 2015). Furthermore, parents' GPS (Rietveld et al., 2013) were found to mediate the parent-offspring association in educational attainment, accounting for about 15% of the association (Conley et al., 2015). However contrary to previous research on family SES (Turkheimer, Haley, Waldron, D'Onofrio, & Gottesman, 2003), the effects of offspring genotype on educational attainment were not moderated by parents’ socio-demographic background (Conley at al., 2015).

We extend these earlier studies in three ways. First, we compared educational levels of parents and their offspring. We provide a concrete index of intergenerational educational attainment by comparing GPS scores of offspring in all four parent-offspring attainment categories -- downwardly mobile, upwardly mobile, stably educated and stably uneducated. Second, we used a new GPS for years of education that doubles the amount of variance explained in years spent in education as compared to the 2013 GPS (Rietfeld et al.) used in the previous studies. This new GPS, derived from a 2016 GWA analysis of years of education with nearly 300,000 individuals, explains 3.9% of the variance in years of education in independent samples (Okbay et al., 2016). Finally, in addition to the DNA analyses, we use twin data to provide the first well powered estimate of the degree to which intergenerational educational attainment is heritable.

Method

Participants were drawn from the Twins Early Development Study (TEDS), a longitudinal birth cohort study of over 15,000 twin pairs born in England and Wales between 1994 and 1996. Although there has been some attrition, more than 10,000 twin pairs remain actively involved in TEDS. The representativeness of the TEDS sample has been assessed at first contact in infancy, early childhood, middle childhood, and adolescence. TEDS has been shown to be representative of the UK population at each age on ethnicity, family SES and parental occupation (Haworth, Davis, & Plomin, 2013; Kovas et al., 2007). For example, 92% of the families at first contact self-reported as white, compared to 93% in UK population percentages at that time. The TEDS families are also representative in terms of parents’ highest educational level (http://dera.ioe.ac.uk/22771/1/SN04252.pdf) and twins’ completion rate of A-levels ("Department for Education," 2016), a key variable in our analyses. A-levels are a two-year school option offered at the end of compulsory education in the UK at age 16, which is the first time students are free to choose whether or not to continue with formal education.

Twin sample

Data on the twin’s completion of A-levels along with their parent’s highest educational qualification were available for 12,210 individuals (6,105 twin pairs), excluding twins with a severe medical or psychiatric history or unknown zygosity. Zygosity was determined by parent-reported questionnaire which is over 95% accurate compared to DNA testing (Price et al., 2000). Where zygosity was unclear from this questionnaire, DNA testing was conducted. Our analysis sample included 2,128 monozygotic (MZ) twin pairs, 1,997 dizygotic same sex (DZss) twin pairs and 1,980 dizygotic opposite-sex (DZos) twin pairs.

Genotyped sample

Genome-wide genotype data were available for 5,825 unrelated individuals, one member of twin pairs from the TEDS sample. The genotyped subsample is representative of UK census data at first contact. Samples were removed from the GPS analyses on the basis of suspected non-European ancestry, as is standard practice. For more detail, see Selzam et al. (2016).

DNA was genotyped using Illumina HumanOmniExpressExome-8v1.1or Affymetrix Affymetrix GeneChip 6.0 DNA arrays. A total sample of 5,825 individuals (2,698 genotyped on Illumina and 3,127 genotyped on Affymetrix) remained after quality control. Genome-wide genotypes from the two arrays were separately imputed using the Haplotype Reference Consortium (McCarthy et al., 2016) and Minimac3 1.0.13 (Fuchsberger, Abecasis, & Hinds, 2015), which are available from the Michigan Imputation Server (https://imputationserver.sph.umich.edu). A series of quality checks were performed before merging data from the two arrays. Details about quality control and imputation were described by(Selzam et al., 2016).

After stringent pruning to remove markers in linkage disequilibrium (r2 > 0.1) and excluding eight genomic regions in high linkage disequilibrium so as to ensure that only genome-wide effects were detected, we performed Principal Component Analysis to correct for possible stratification using a subset of 40,745 autosomal SNPs that remained after applying our quality control criteria and that overlapped between the two genotyping arrays. We regressed the GPS on the first 10 principal components and used the residuals in all subsequent analyses. More details of the quality control and preprocessing procedures can be found in the online Supplemental Material.

Measures

Intergenerational educational attainment

Intergenerational educational attainment can be defined as the degree of similarity between the education levels of parents and their offspring (Kuntz & Lampert, 2013; Oberdabernig & Schneebaum, 2015). In the TEDS sample, intergenerational attainment was operationalized by comparing undergraduate degree attainment of at least one parent per family to the twins’ Alevel attainment at age 18.

A questionnaire to collect A-level and other post-16 qualifications, along with work destinations, was sent to all TEDS families at the end of the academic school year when twins reached age 18. The questionnaire was completed either by twins themselves or by their parents.

Information on parental education was collected when the TEDS sample were first contacted between 1995 and 1998. Children with at least one parent who had obtained a university degree were considered as born into a university-educated family.

Intergenerational educational data were used to create four groups: downwardly mobile, stably educated, upwardly mobile and stably uneducated. The downwardly mobile group identified twins who did not complete A-levels but were raised in families with at least one university-educated parent. The upwardly mobile group identified twins, who completed A-levels but whose parents had not attended university. The stably educated and stably uneducated groups represented parent-offspring similarity in pursuing education post compulsory schooling, which was measured as having versus not having university degree in parents and as completing versus not completing A-levels in their offspring.

For twin analyses, dichotomous indices of downward and upward mobility were computed as follows. The dichotomous downward mobility variable took values of either zero or one and only included those individuals from families with at least one university-educated parent. A value of one identified those twins who were downwardly mobile while a value of zero identified those twins who were stably educated. The dichotomous upward mobility variable could also take values zero and one and included those individuals from families with no university-educated parent. Here, a value of one described offspring, who completed A-levels and thus, was upwardly mobile, while a value of zero referred to the stably uneducated.

Statistical Analyses

Twin analyses

Univariate twin analyses assess the relative genetic and environmental contribution to variance in a trait by comparing intraclass correlations between MZ twins who share all of their genes and DZ twins who on average share 50% of their segregating genes (Rijsdijk & Sham, 2002). The extent to which MZ twin pair correlations are greater than DZ twin pair correlations serves as an index of heritability—the proportion of phenotypic differences that can be attributed to genetic differences (Knopik et al., 2017).

When the available phenotypic data are categorical, the univariate model can be extended to a liability model that estimates additive genetic (A), shared (C) and unique (E) environmental etiologies, assuming that binary variables reflect an unobserved normal distribution (Neale & Cardon, 1992).

Liability correlations, known as tetrachoric correlations, were computed based on the concordance rates for intergenerational educational attainment in MZ and DZ twins. Tetrachoric correlations and ACE components were estimated with maximum likelihood using OpenMx (Boker et al., 2011). Liability twin models in TEDS have been described elsewhere in detail (Shakeshaft et al., 2013).

Comparing the tetrachoric correlations across MZ and DZ pairs allows estimating genetic influences on intergenerational attainment. If intergenerational educational attainment is predominantly driven by non-genetic factors shared within a family, twins’ correspondence in mobility and stability will be similar for MZ and DZ pairs. That is, if one twin is downwardly mobile (i.e. parent has a university degree but twin has not taken A-levels), the likelihood that the co-twin is also downwardly mobile will be the same for MZ and DZ pairs. By contrast, if MZ twins are more similar in their intergenerational educational attainment than DZ twins, genetic influences are implied.

GPS analyses

GPS analyses tested whether aggregates of SNPs associated with years of education significantly predicted intergenerational educational attainment in our sample. A GPS score sums genotypic values (0, 1, or 2) for each SNP weighted by its association in the discovery GWA sample. GPS scores based on summary statistics (β-weights and p-values) from the 2016 GWA study of educational attainment (Okbay et al., 2016) were created for each of the 5,825 unrelated individuals in the TEDS sample. We used all matched SNPs (i.e., p-value threshold of 1.0) to compute our GPS, irrespective of nominal significance for their association with educational attainment. The resulting GPS was normally distributed and was standardized to have a mean of zero and a standard deviation of 1.0.

Separate logistic regression models were fitted to test the association between EduYears GPS and intergenerational educational attainment, with the Nagelkerke r2 (Nagelkerke, 1991) indicating the amount of variance in the liability explained. Analysis of variance (ANOVA) tested mean GPS differences between educational attainment groups, followed by planned comparisons that were corrected for multiple comparisons. Also, analysis of covariance (ANCOVA) tested whether GPS differences between educational attainment groups remained significant after controlling for previous academic performance, specifically twin's grades on the UK General Certificate of Secondary Education, which is administered at the end of compulsory education at age 16.

Results

Of the total sample of 6,105 twin families, 1,790 families included at least one parent who was university educated (29%). By comparison, 6,304 of the 12,210 twins (51%) completed A-levels, reflecting an overall increase between generations in the pursuit of higher education.

Of the 3,580 twins in the 1,790 families with at least one university-educated parent, 989 (28%) did not complete A-levels, which we refer to as ‘downward educational mobility’. By contrast, of the 8,650 twins in the 4,325 families with parents who were not university educated, 3,713 (43%) completed A-levels, which we refer to as ‘upward educational mobility’.

We refer to the other two groups as ‘stably educated’ and ‘stably uneducated’. That is, of the 3,580 twins raised by university-educated parents, 2,591 continued to Alevels (i.e., 72% ‘stably educated’). Of the 8,630 twins whose parents were not university educated, 4,917 did not pursue A-levels (i.e., 57% ‘stably uneducated’).

Table 1 shows the concordance of MZ and DZ twins for A-level attainment separately for families with and without at least one university-educated parent. These data are used to calculate twin tetrachoric correlations. However, a rough index of twin similarity is the probandwise twin concordance (McGue, 1992). The MZ and DZ concordances are 0.95 and 0.85, respectively in families with at least one university-educated parent, and 0.93 and 0.83 in families without university-educated parents, suggesting some genetic influence on intergenerational educational attainment.

Table 1.

MZ and DZ twin concordance data for A-level attainment in children of university-educated and non-university-educated families

Families with university-educated
parent		 Families without university-educated
parents
N pairs Concordant Discordant N pairs Concordant Discordant
MZ 602 537 (89%) 65 (11%) 1,526 1,314 (86%) 212 (13%)
DZ 1,188 822 (69%) 366 (30%) 2,789 1,830 (65%) 959 (34%)
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Note: MZ = monozygotic twin pairs, DZ = dizygotic twin pairs. N = number of complete pairs.

Twin tetrachoric correlations

Twin tetrachoric correlations were derived from the data in Table 1 and analyzed using the liability threshold model in order to estimate genetic and environmental parameters and their confidence intervals. Twin tetrachoric correlations were greater for MZ than for DZ twin pairs for children from families with university-educated parents (MZr = 0.91; DZr = 0.68) and children of parents without a university education (MZr = 0.91; DZr = 0.65), indicating genetic influence on downward and upward intergenerational educational attainment. Because the DZ concordances are greater than half the MZ concordances, shared environmental influences are also assumed.

Twin analyses using the liability threshold model

Figure 1 illustrates substantial genetic influence on intergenerational educational attainment, with approximately half of the phenotypic variance in liability attributed to inherited DNA differences for both dichotomous upward and downward mobility variables. The influence of shared environmental factors that contribute to similarities between twins growing up in the same home were almost as large, accounting for approximately 40% of the variance. Non-shared environmental factors that do not contribute to twin similarity explained less than 11% of the variance in both upward and downward mobility.

Fig. 1.

Fig. 1

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Liability threshold model results with 95% confidence intervals. A=additive genetic, C=shared environmental and E= non-shared environmental components of variance for downward and upward educational mobility variables. Downward mobility refers to the dichotomous variable for children from families with at least one university-educated parent, representing children who are either downwardly mobile (no A-levels) or stably educated (A-levels). Upward mobility refers to the dichotomous variable for children of parents without a university education, representing those children who are either upwardly mobile or (A-levels) stably uneducated (no A-levels).

GPS analyses

Figure 2a shows mean differences in EduYears GPS between the four educational attainment groups. Children from families with at least one university-educated parent who themselves had not obtained A-levels (i.e. downward mobility) had a lower GPS mean (M = 0.28; SD = 1.03) than children who were stably educated (M = 0.43; SD=0.97), F(1,1181) = 5.36, p<.001. Children whose parents had not attended university but completed A-levels themselves (i.e. upward mobility) had a significantly higher GPS (M = 0.05; SD = 0.96) than children who were stably uneducated (M = −0.19; SD= 0.97), F(1,2759) = 43.6, p<.001.

Fig. 2.

Fig. 2

Fig. 2

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a. GPS means across educational mobility groups. N= sample size after exclusions (unrelated individuals); error bars = standard error. Stably educated= children from families with at least one university-educated parent who take A-levels; Downwardly mobile= children from families with at least one university-educated parent who do not take A-levels; Upwardly mobile= children of parents without a university degree who take A-levels; Stably uneducated= children of parents without a university degree who do not take A-levels.

b. GPS means corrected for grades on the General Certificate of Secondary Education (GCSE). N= sample size after exclusions (unrelated individuals); error bars = standard error. For label explanations, see Figure 2a.

EduYears GPS accounted for a significant proportion of liability variance in both upward mobility (Nagelkerke r2 = 0.021, p < .001) and downward mobility (Nagelkerke r2 = 0.016, p < .001) in our independent sample of over 5,000 unrelated individuals. A one-SD increase in GPS was associated with a 36% increase in the odds of experiencing upward educational mobility in children whose parents did not have a university degree (OR = 1.36; 95%CI= [1.25 – 1.47]; n = 2,792). Similarly, a one SD increase in GPS was associated with a 29% decrease in the odds of experiencing downward educational mobility in children from families with at least one university-educated parent (OR = 0.71; 95%CI= [0.62 – 0.82]; n = 1,200).

Similar results were found when analyses were rerun separately for mothers and fathers. (See Figures S1 and S2 in the Supplemental Material available online.)

We investigated the extent to which genetic influence on upward and downward educational mobility depended on previous school performance, assessed by GCSE grades. Figure 2b shows that after adjusting GPS scores for GCSE grades, the mean GPS differences between the stably educated (M=0.17; SD=1.01) and downwardly mobile (M=0.19; SD=1.07) groups were no longer significant (F(1, 1047)=0.035, p= 0.85). Likewise, the mean GPS difference between the stably uneducated (M=−0.03; SD=0.99) and upwardly mobile (M=−0.09; SD=0.98) groups were no longer significant (F(1,2194)=2.659, p=0.10). However, a mean GPS difference between twins in university-educated families (M=0.18; SD= 1.02) and twins in families without university education (M= −0.06; SD= 0.98) remained significant (F(1,3242)=40.79, p<.001).

Discussion

We report the first study on the genetics of intergenerational educational attainment that uses both twin and genomic data. Our twin analyses indicate that half of the variance in intergenerational educational attainment can be attributed to genetic differences. These results demonstrate that parent and offspring educational outcomes are similar for genetic as well as environmental reasons.

The results from our twin models were supported by our genomic analyses. For a GPS derived from GWA results for adult educational attainment (Okbay et al., 2016), we observed significant mean GPS differences across four groups of intergenerational educational attainment. The highest and lowest mean GPS were observed in stably educated and stably uneducated families, respectively—the GPS scores were in between for families that were upwardly and downwardly mobile. This finding is in line with two prior studies on molecular genetic correlates of educational attainment that relied on a less powerful GPS. Consistent with our results,Conley et al. (2015) reported evidence of genetic transmission in parent-child educational correlations, while Domingue et al. (2015) found participants with higher polygenic scores were more likely to grow up in socially advantaged families.

By contrast to previous studies (e.g. Selzam et al., 2016), we used here the 2016 GPS to predict the attainment of educational qualifications rather than achievement in terms of school grades. School leaving certificates like the British A-levels regulate the access to further education and thus, inform career opportunities. Notwithstanding the importance for people's life trajectories, our study is the first to test genetic influences in terms of twin-based heritability and associations with the 2016 GPS (Okbay et al., 2016) on an intergenerational phenotype of educational attainment.

Polygenic-score associations with intergenerational attainment are likely to be mediated by many psychological characteristics, all of which are under substantial genetic influence. The most obvious candidate here is prior academic achievement, which greatly informs children’s decision to go on to A-levels. In the current analyses, after adjusting GPS scores for the children’s academic performance at age 16 (GCSE grades), mean GPS differences between the stably educated and downwardly mobile groups were no longer significant, and neither were the differences between the stably uneducated and upwardly mobile groups. These results suggest that GPS effects on educational mobility are largely driven by children's differences in prior academic performance. That said, the genetic effect of parents' education level on children's attainment remained.. Future studies may explore other specific psychological mechanisms that explain the association between DNA and intergenerational educational attainment.

A noteworthy finding in the present study is that intergenerational educational attainment is influenced to a large extent by shared environmental factors. Although it is reasonable to assume that shared environmental effects, such as the home that children grow up in or the schools that siblings attend, shape educational trajectories, strong shared environmental influences, like those reported here, are rare in the psychological literature (Plomin, 2011). Our twin analyses estimate that shared environmental influences account for 40% of the variance in liability of intergenerational educational attainment, whereas estimates of shared environment rarely exceed 20% for other education-related measures (Knopik et al., 2017).

Genetic influences on intergenerational attainment suggest that some individuals, who were born into socially disadvantaged families but surpass the constraints typically associated with lower SES, do this in part because of their genetic propensities. For example, we found higher mean GPS in the upwardly mobile group compared to the stably uneducated group, indicating children with more education associated alleles went on to attain A-level qualifications despite their familial environment.

A compelling implication of our results is that, to the extent that genetics is important, parent-offspring resemblance could be viewed as an index of environmental equality rather than inequality. This is because heritability estimates index the extent to which genetic differences account for phenotypic variance in a particular population with its particular mix of genetic and environmental influences. As environmental opportunities improve across a society, genetic influences are maximized such that an individual’s educational attainment is increasingly a function of individual characteristics and less a product of social status (Conley et al., 2015; Tucker-Drob, Briley, & Harden, 2013).

Strengths and limitations

The present study benefits from a large sample size and inclusion of both twin and genomic analyses. Nonetheless, our results must be considered together in light of three limitations, in addition to the general limitations of the twin method (Knopik et al., 2017).

First, our measure of educational attainment is a proxy measure given offspring Alevel attainment extends only through age 18 years, unlike our parental educational attainment measure, which reflects adult attainment. As a result, some individuals who did not obtain an A-level qualification may still go on to obtain a university degree, while others who did take A-levels may fail to complete their university degree. That said, in Britain fewer than 17% of students are admitted to university without A-level qualification (https://www.hesa.ac.uk/content/view/2880), while only 6% of 2013–14 university enrollees who obtained their A-levels failed to complete their degree (https://www.hesa.ac.uk/data-and-analysis/performanceindicators/summary).

The second limitation is that educational attainment is partly conditioned by cohort changes in educational norms. Our analyses are based on a single European birth cohort and the generalizability of our results outside of this population has yet to be formally tested.

Lastly, although the 2016 GPS accounts for 3.9% of the variance in years of education, this GPS only explains about 6.5% of the heritability of years of education as estimated in twin studies (Selzam et al., 2016). This limits the potential effect size for our genetic analysis of intergenerational educational attainment. However, as the so-called ‘missing heritability’ gap is closed, these GPS results will get stronger.

Conclusion

Our findings emphasize the need for genetically sensitive studies of the factors that influence intergenerational educational outcomes and inequality.

Supplementary Material

Acknowledgments

Funding

We gratefully acknowledge the ongoing contribution of the participants in TEDS and their families. TEDS is supported by a program grant to RP from the UK Medical Research Council [MR/M021475/1; and previously G0901245], with additional support from the US National Institutes of Health [HD044454; HD059215; AG046903] and the European Commission [602768]. RP is also supported by a Medical Research Council Research Professorship award [G19/2] and a European Research Council Advanced Investigator award [295366]. SvS is supported by a Jacobs Foundation Research Fellowship award (2017–2019).

Footnotes

Author Contributions

ZA, RP, and SvS conceived and designed the study and wrote the paper. ZA analyzed the data. EK processed and quality controlled the genotype data and created the genome-wide polygenic scores. All authors discussed the results and implications and commented on the manuscript at all stages.

Declaration of Conflicting Interests

The authors declare no conflicting interest in the authorship or publication of this paper.

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