Education can Reduce Health Disparities Related to Genetic Risk of Obesity: Evidence from a British Reform

Abstract

This paper investigates whether genetic makeup moderates the effects of education on health. Low statistical power and endogenous measures of environment have been obstacles to the credible estimation of such gene-by-environment interactions.We overcome these obstacles by combining a natural experiment that generated variation in secondary education with polygenic scores for a quarter million individuals. The additional schooling affected body size, lung function, and blood pressure in middle age. The improvements in body size and lung function were larger for individuals with high genetic predisposition to obesity. As a result, education reduced the gap in unhealthy body size between those with high and low genetic risk of obesity from 20 to 6 percentage points.Educational policies may increase or decrease health disparities depending on whether they reinforce or counteract gene-related health disparities (1). Both childhood circumstances, such as education, and genetic factors are independently associated with later life health (2,3,4). A growing literature suggests that health may also depend on the interaction between these two factors (5,6,7). Where strong gene-by-environment (GxE) interactions exist, modest effects of education on average may conceal larger effects for populations with particular genotypes and lead to underestimates of the benefits of schooling. We investigate this possibility by testing whether genetic makeup moderates the effect of an additional year of secondary education on middle age health.After the publication of high-impact GxE studies (8,9,10), controversies over the replicability of results tempered the enthusiasm for this research program. Low statistical power and endogenous measures of environment are believed to be main reasons for the limited replicability (11,12,13). Many GxE studies are low-powered because behavioral traits tend to be

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

“…We favor this structural interpretation given that the variation in association observed with respect to educational attainment does not translate into any such variation in the association with occupational attainment (see also Section D of online supplement). This finding in the WLS is similar to the recent observation that a similar polygenic score predicts additional variance in outcomes in Estonia in the post-Soviet period (Rimfeld et al 2018) or to the reduction in health disparities linked to genotype after the introduction of a compulsory schooling law (Barcellos, Carvalho, and Turley 2018). These findings all tie reductions in phenotypic variance associated with specific contextual paradigms to reductions in associations with the relevant genetic predictor.…”

Genetics and Education: Recent Developments in the Context of an Ugly History and an Uncertain Future

“…Polygenic scores may offer a useful mechanism for better understanding responses to educational interventions. For example, recent work used the polygenic score for educational attainment to emphasize changes in association between genotype and phenotype after the decline of Soviet rule in Estonia (Rimfeld et al, 2018), in different retirement saving regimes (Barth, Papageorge, & Thom, 2018), and to demonstrate the effect of mandatory schooling on health (with a polygenic score for body mass index; Barcellos, Carvalho, & Turley, 2018). Findings from these studies support the concept that social policies that reduce choice-in the case of mandatory schooling laws in the United Kingdom, for example-also reduce the salience of genotype.…”

The effect of education on health: evidence from national compulsory schooling reforms

“…Many mechanisms have been suggested on how education could improve health: raising efficiency in health production (productive efficiency) (Grossman 1972), changing inputs in health production (allocative efficiency) (Grossman 2005), changing time preference (Becker and Mulligan 1997), changing behavioral patterns, e.g., smoking, obesity, preventive care (Huisman et al 2005;Mackenbach et al 2008; Barcellos et al 2018); and finally, gaining more resources, e.g., higher income, occupational status, better housing, better food, better quality of care, and living environment (i.e., Case and Deaton 2005;Cutler and Lleras-Muney 2008).…”