Gene-Environment Interplay in the Generation of Health and Education Inequalities


Can high-quality child care overcome genetic disadvantage in educational attainment? Does a stable socioeconomic environment limit genetic susceptibility to risky health behaviours? This NORFACE-funded project will examine how Genes and the Environment (GxE) interact to generate inequalities in education and health over the life course. We will go beyond the old nature versus nurture debate by testing two novel hypotheses: (i) children born into advantaged environments are better able to reach their genetically conditioned education potential, and (ii) a privileged environment protects against genetic susceptibility to risky health behaviour. Both hypotheses propose a GxE interplay that influences the transition from early childhood to adulthood in periods that are critical to the generation of inequalities. 

The innovation is to combine methods from genetics and econometrics. Building on the discovery of genetic variants that exhibit robust associations with behavioural outcomes and the recent availability of large datasets with information on both environments and genes, we will grasp unprecedented opportunities to fill the gap in knowledge about the combined role of genes and environments in causing inequality in education and health.

We will place particular emphasis on taking into account the endogenous nature of the environment, by exploiting natural experiments in environmental circumstances (e.g., changes in child care subsidies, increases of excise taxes on cigarettes, changes in family income due to the financial crisis, etc.) using state-of-the-art econometric techniques such as Instrumental Variables, Difference-in-Difference, and Regression Discontinuity.


Econometrics; Inequality; Genes; Environment; Interaction; Education; Health; Health Behaviours


Inequalities in education and health are pervasive, persistent and deeply intertwined. Poor neonatal health affects intelligence and later life education (e.g., Black et al. 2007, Bharadwaj et al. 2016). In high-income countries, male college graduates can expect to live 7.7 years longer than their less educated peers (OECD, 2015). While there is a large and growing body of work documenting inequalities in education and health, we know very little about the mechanisms underlying them and even less about the potential to weaken them through policy (Figlio et al. 2014).

Recent advances in the collection and analysis of genetic data offer exciting opportunities to deepen understanding of the fundamental causes of inequalities. Genome-wide association studies (GWAS) have discovered specific genetic variants (single nucleotide polymorphisms, SNPs) that exhibit credible and robust associations with educational attainment, smoking, and obesity (Frayling et al. 2007; Speliotes et al. 2010; Thorgeirsson et al. 2010, TAG Consortium 2010; Rietveld et al. 2013; Hancock et al. 2015; Locke et al. 2015; Okbay et al. 2016a). While this evidence represents a major scientific breakthrough, the mere knowledge that differences in health behaviour and education have some genetic basis leaves policymakers largely empty-handed with respect to the correction of inequalities. Further, genes typically explain a small portion of the variation in social outcomes (Visscher et al. 2012, Chabris et al. 2015). While this appears to leave ample scope for environmental explanations, it is increasingly appreciated that complex interactions may exist between genes and environment (Turkheimer, 2000; Rutter, 2006; Heckman, 2007). 

The aim of this project is to understand and document the interplay between Genes and the Environment (GxE) in the life course formation of inequalities in education and health. GxE interplay encompasses both gene-environment interaction (e.g., genes influence outcomes only in certain environmental circumstances) and compensation (e.g., social advantage overcomes genetically influenced limitations).

The project consists of two related subprojects:

  1. Are children who grow up in advantaged environments better able to reach their genetically conditioned potential in educational attainment and (non-) cognitive skills? 
  2. Does an advantaged environment cushion genetic susceptibility to risky health behaviours?

Subproject 1 advances knowledge by testing whether genetically influenced low potential ability can be overcome by a stable, high-quality childhood environment. Subproject 2 advances knowledge by investigating which individual, social and policy environments are conducive to limiting risky health behaviour among individuals with elevated genetic risk.


The approach is to leverage the results from GWAS and select specific SNPs that are known to be correlated with the outcome of interest, or a polygenic score that is a weighted prediction score on the basis of all (or a subset of) SNPs. We will start with a simple model like:

where  is the outcome variable (e.g., educational attainment, adiposity) for individual in year t; gi is a binary indicator for carrying a specific genetic variant or not, or a polygenic risk score;  is a vector of environmental measures (e.g., parental income, childhood health, the policy environment),  is a matrix of (time-varying) demographic factors including corrections for genetic differences across population subgroups (Price et al. 2006), and  is the error term.

Data sources

Data sources include the UK Avon Longitudinal Study of Parents And Children (ALSPAC), a rich ongoing cohort study that follows thousands of women and their offspring from pregnancy onwards. An advantage of these data is that women were recruited during pregnancy (rather than after child birth), allowing us to examine prenatal as well as postnatal risk factors that affect the health and development of their children. The UK National Child Development Study (NCDS) is a cohort study of people born in one particular week in 1958. Its members have experienced completely different environmental exposures compared to the ALSPAC cohort.

Studies that focus more on the working-age population during early and later adulthood include the Estonian Genome Center Biobank (18+) and the UK Biobank (500,000+ participants aged 40-69). Studies that observe individuals close to and post retirement include the English Longitudinal Study of Ageing (ELSA) and the US Health and Retirement Study.

Literature references

  • Bharadwaj, P., Lundborg, P., & Rooth, D.-O. (2016). The Effects of Birth Weight over the Life Cycle. Working Paper.
  • Black, S.E., P.J. Devereux, and K.G. Salvanes (2007), “From the Cradle to the Labor Market? The Effect of Birth Weight on Adult Outcomes”, Quarterly Journal of Economics, 122(1), 409-439.
  • Caspi, A., Hariri, A. R., Holmes, A., Uher, R., & Moffitt, T. E. (2010). “Genetic sensitivity to the environment: the case of the serotonin transporter gene and its implications for studying complex diseases and traits.” American Journal of Psychiatry 167, 509–527
  • Center for Disease Control and Prevention (CDC) (2000), “Gene-Environment Interaction Fact Sheet” Office of Genetics and Disease Prevention, August 2000.
  • Chabris, C. F., Lee, J. J., Cesarini, D., Benjamin, D. J., and Laibson, D. I. (2015). “The Fourth Law of Behavior Genetics.” Current Directions in Psychological Science, 24(4), 304–312.
  • Figlio, D., Guryan, J., Karbownik, K., and Roth, J. (2014). The Effects of Poor Neonatal Health on Children's Cognitive Development. American Economic Review, 104(12), 3921-55.
  • Frayling, T.M. et al. (2007), “A Common Variant in the FTO Gene Is Associated with Body Mass Index and Predisposes to Childhood and Adult Obesity”, Science, 316(5826), 889-894.
  • Hancock, D.B., Reginsson, G.W., Gaddis, N.C., Chen, X., Saccone, N.L., Lutz, S.M., ... & Stacey, S.N. (2015), “Genome-wide meta-analysis reveals common splice site acceptor variant in CHRNA4 associated with nicotine dependence” Translational Psychiatry, 5(10), e651.
  • Heckman, J. J. (2007). The economics, technology, and neuroscience of human capability formation. Proceedings of the National Academy of Sciences, 104(33), 13250-13255.
  • Locke, A. E., Kahali, B., Berndt, S. I., Justice, A. E., Pers, T. H., Day, F. R., … Speliotes, E. K. (2015). Genetic studies of body mass index yield new insights for obesity biology. Nature, 518(7538), 197–206.
  • Okbay, A., Beauchamp, J.P., Fontana, M., Lee, J.J., Pers, T.H., Rietveld, C.A., ... Benjamin, D.J. (2016a). Genome-wide association study identifies 74 loci associated with educational attainment. Nature. Forthcoming.
  • Plomin, R. (2014). Genotype-Environment Correlation in the Era of DNA. Behavior Genetics, 44(6), 629-638.
  • Price, A.L., Patterson, N.J., Plenge, R.M., Weinblatt, M.E., Shadick, N.A., & Reich, D. (2006), “Principal components analysis corrects for stratification in genome-wide association studies. Nature genetics, 38(8), 904-909.
  • Rietveld, C.A., Medland, S.E., Derringer, J., Yang, J., Esko, T., Martin, N.W., ... Koellinger, P.D. (2013). “GWAS of 126,559 individuals identifies genetic variants associated with educational attainment.”  Science, 340, 1467-1471
  • Rutter, M. (2006). Genes and behavior: Nature-nurture interplay explained. Blackwell Publishing.
  • Speliotes, E.K., Willer, C.J., Berndt, S.I., Monda, K.L., Thorleifsson, G., Jackson, A.U., … Loos, R. J. F. (2010). "Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index." Nature Genetics, 42(11), 937–48.
  • TAG, Tobacco and Genetics Consortium (2010), “Genome-wide meta-analyses identify multiple loci associated with smoking behavior”, Nature genetics, 42(5): pp. 441-447.
  • Thorgeirsson, T.E., Gudbjartsson, D.F., Surakka, I., Vink, J. M., Amin, N., Geller, F., ... & Gieger, C. (2010), “Sequence variants at CHRNB3-CHRNA6 and CYP2A6 affect smoking behavior”, Nature genetics, 42(5): pp. 448-453.
  • Tobacco and Genetics Consortium. (2010). Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat Genet 42(5): 441–447. doi: 10.1038/ng.571
  • Turkheimer, E. (2000). Three laws of behavior genetics and what they mean. Current Directions in Psychological Science, 9(5), 160-164.
  • Visscher, P. M., Brown, M. A., McCarthy, M. I., & Yang, J. (2012). Five years of GWAS discovery. The American Journal of Human Genetics, 90(1), 7-24.


Within the Erasmus University there will be close collaboration with Dr. Niels Rietveld (Applied Economics) who was at the forefront of discovering the genetic architecture of educational attainment (e.g., Rietveld et al. Science 2013).

Outside of Erasmus University, there will be potential collaboration with Dr. Stephanie von Hinke (Bristol University) and Dr. Pietro Biroli (University of Zurich). Short term visits to these institutes are possible.

Expected output

The candidate is expected to write at least 3 academic papers, to be resulting in a PhD thesis, partly in collaboration with supervisors and/or cooperation partners (Rietveld, Von Hinke, Biroli).

Scientific relevance

The analysis of GxE interplay in education and health, using state-of-the-art econometric approaches to deal with the endogeneity of the environment, is likely to have a sustained and powerful scientific impact. First, we believe one of G×E research’s important contributions is teaching the falsehood of genetic determinism (Caspi et al. 2010; Manuck and McCaffery, 2014). Second, the discovery of specific genetic variants that affect educational outcomes and health behaviours, and their increased availability in large datasets, allows testing the biological basis underlying a wealth of theoretical models that heretofore has not been possible. Third, using careful identification of exogenous variation in environmental effects, we can better inform policy on the critical environmental exposures that produce GxE interplay. Fourth, the National Coalition for Health Profession Education in Genetics explicitly recognizes the benefits of interdisciplinary collaboration, arguing that “geneticists (…) benefit from the guidance of social and behavioural researchers” in measuring the environment.

Societal relevance

In terms of societal relevance, quantifying GxE interplay for (i) different environmental exposures, (ii) at different stages of the life course, and (iii) for different outcomes of interest allows exploration of the existence of critical periods and policy interventions that are most promising in ameliorating inequalities in human capital and health behaviours. Examples include using neonatal interventions to encourage higher birth weights if the analyses show that high birth weight babies are more likely to reach their genetic potential, or longer maternity leave if we establish that children with elevated risk for dropping out of high school or developing risky health behaviours benefit from spending more time with their mother early in life. Additionally, the discovery of differential responses to policy intervention by genetic risk factors potentially improves their effectiveness by identifying and targeting individuals most and least amenable to intervention (CDC, 2000; Manuck and McCaffery, 2014).

Phd candidate profile

The candidate is expected to (i) have a (research) master degree in Economics or Econometrics, (ii) have good quantitative skills and experience with statistical software (Stata, R, or similar programs), (iii) excellent communication and writing skills in English, and (iv) have an interest in gaining a sufficient amount of knowledge on genetics and biology.


Prof. Dr. Eddy van Doorslaer
T: +31 (0)10 4082566

Dr. Hans van Kippersluis
T: +31 (0)10 4088837

Graduate school

This project is affiliated with the Tinbergen Institute graduate school, applicants for this project need to pass the Tinbergen Institute's admission requirements before they can be considered for a PhD position at Erasmus School of Economics.

Note that the Tinbergen Institute requires valid GRE General Test results from all applicants. More information about the GRE test is available here. Be aware that available seats for this test fill up very fast so book your test well in advance. Please contact the GRE program for specific questions about the GRE test.


Application deadline: 15 January 2018


Apply for this project using our online application form. Please use the project code below to apply for this project.

Tinbergen project code:

TI PhD 2018 ED HvK