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

Individual differences in school achievement are a complex interplay of genetic predispositions and environmental factors. The bioecological model posits multiple environmental levels influencing achievement, including family, school, neighborhood, and broader societal contexts. While the family and school are considered most influential, distal factors also play a role. Gene-environment interaction (GxE) research focuses on the environmental dependence of genetic effects. The Scarr-Rowe hypothesis suggests that disadvantage suppresses genetic influence on cognitive development, while advantage allows greater expression of genetic differences. However, empirical support for this model is inconsistent across studies, with some showing stronger genetic influences in higher socioeconomic status families and others finding null or opposing results. Three key limitations hinder previous GxE research: a narrow focus on family-level environments, the overwhelming reliance on specific environmental measures, and the failure to adequately account for gene-environment correlation. This study aims to overcome these limitations by using a population-wide sample to comprehensively assess the impact of multiple environmental levels on achievement, controlling for gene-environment correlation through the use of parent EA-PGI, to estimate the total magnitude of interactions between students' EA-PGI and various environmental contexts.

Prior research on gene-environment interaction and its impact on educational achievement has yielded inconsistent results. Studies investigating the Scarr-Rowe interaction hypothesis, which posits that genetic influence is suppressed in disadvantaged environments and amplified in advantageous ones, have produced conflicting findings. Some U.S. twin studies show stronger genetic influence in higher socioeconomic status families, while European and Australian studies reveal null or opposite results. Genomic studies using polygenic indices (PGI) generally show limited interaction between PGI and environmental measures. Several limitations in previous research include a narrow focus on family-level environments, reliance on specific environmental measures, and inadequate control for gene-environment correlation. Studies highlighting the importance of contextual factors beyond the family and suggesting gene-environment interactions beyond the family environment warrant further research. This study sought to overcome the limitations of prior studies by examining multiple environmental levels simultaneously and using a rigorous methodology to control for gene-environment correlation.

This study utilized a large sample of >23,000 parent-child trios from the Norwegian Mother, Father, and Child Cohort Study (MoBa), linked with nationwide administrative data on standardized test scores (math, reading, English), school identifiers, and residential information (neighborhood, district, municipality). The researchers used multilevel models to analyze the data. The models included the students' educational attainment polygenic index (EA-PGI), parental EA-PGI (to control for gene-environment correlation), parental education and income, grade level, and school and residential identifiers. To test for GxE interactions, the authors compared models with and without random slopes (representing interactions between EA-PGI and school/area effects). Additionally, they included five school-level sociodemographic covariates (average parental education, income, proportion of non-Western immigrants, and Gini coefficients for income and education inequality) to explore whether measured school characteristics explain any observed interaction. The data were pooled across grades 5, 8, and 9; time was represented as a continuous variable centered at grade 9; and the standardized test scores were residualized for sex, age, and testing age. The models were compared using the AIC fit statistic to determine which model best fit the data.

The study found a significant interaction between students' EA-PGI and school effects on achievement. Higher-performing schools demonstrated a compensatory effect, raising overall achievement without leaving students with lower EA-PGI behind. The effect of school was more pronounced for students with lower EA-PGI, explaining 4% of achievement variance for those 2 standard deviations below the mean EA-PGI, compared to 2% for those 2 standard deviations above the mean. Residential area variations (neighborhoods, districts, municipalities) contributed little to achievement variation (<2% collectively) and did not interact with students' EA-PGI. The interaction was more evident in math and reading than in English. Crucially, this interaction remained significant even after controlling for parental EA-PGI, family socioeconomic background, and residential area. Further analysis incorporating school-level covariates showed that these factors failed to explain the observed PGI-school interaction.

The findings challenge the Scarr-Rowe model, as genetic effects were not suppressed in less advantaged environments but rather magnified in lower-performing schools. This suggests that simply improving school quality across the board might not suffice to reduce achievement gaps linked to EA-PGI. The study emphasizes that the effects of EA-PGI and school quality cannot be considered independently. The significant school-level interaction highlights the importance of focusing on school-level interventions to address achievement inequalities. Because the interaction was latent, future studies should investigate specific aspects of schools that might explain these differences. The fact that measured school covariates did not account for the observed interaction underscores the need to better understand the underlying characteristics of schools that moderate genetic influence on academic achievement. The minimal impact of residential environments suggests that addressing neighborhood-level factors might not effectively reduce achievement gaps.

This study reveals a significant interaction between students' EA-PGI and school effects on educational attainment. Higher-performing schools provide a compensatory effect, particularly benefitting students with lower EA-PGI. Residential factors play a minimal role. Future research should focus on identifying specific school-level factors that modulate the influence of genetic predispositions on achievement. This comprehensive approach, integrating genetic and environmental data, provides valuable insights into the complex interplay of factors influencing educational achievement and offers guidance for policy interventions.

The study's generalizability is limited by the inclusion of only participants of European ancestry and potential non-random participation in the MoBa cohort. The current EA-PGI may not capture the full genetic component of educational attainment, and EA-PGIs were based on data pooled across contexts, potentially not reflecting the heritable components most sensitive to school and residential differences. While the study controlled for passive gene-environment correlation, active gene-environment correlation might remain, although this was mitigated by the absence of selective schools in Norway. The study primarily focuses on elementary and middle school; the effects may differ at the high school level.