Genetic associations with mathematics tracking and persistence in secondary school

Abstract

Maximizing the flow of students through the science, technology, engineering, and math (STEM) pipeline is important to promoting human capital development and reducing economic inequality. A critical juncture in the STEM pipeline is the highly cumulative sequence of secondary school math courses. Students from disadvantaged schools are less likely to complete advanced math courses. Here, we conduct an analysis of how the math pipeline differs across schools using student polygenic scores, which are DNA-based indicators of propensity to succeed in education. We integrated genetic and official school transcript data from over 3000 European-ancestry students from U.S. high schools. We used polygenic scores as a molecular tracer to understand how the flow of students through the high school math pipeline differs in socioeconomically advantaged versus disadvantaged schools. Students with higher education polygenic scores were tracked to more advanced math already at the beginning of high school and persisted in math for more years. Analyses using genetics as a molecular tracer revealed that the dynamics of the math pipeline differed by school advantage. Compared to disadvantaged schools, advantaged schools buffered students with low polygenic scores from dropping out of math. Across all schools, even students with exceptional polygenic scores (top 2%) were unlikely to take the most advanced math classes, suggesting substantial room for improvement in the development of potential STEM talent. These results link new molecular genetic discoveries to a common target of educational-policy reforms.

Keywords: Education; Human behaviour.

Fig. 1. Students with higher education-associated polygenic scores are tracked to more advanced math and persist for longer in math.

Error bars represent 95% confidence intervals around the mean.

Fig. 2. Genetic associations with persistence in math recur year-after-year.

Error bars represent 95% confidence intervals around the mean. Size of the dots represents number of students enrolled or not enrolled in math in each year.

Fig. 3. Student DNA can be used to visualize the flow of students through the high school math curriculum.

Columns represent year of secondary school; rows represent mathematics course sequence ranging from least to most advanced. Width of the rivers connecting columns proportional to number of students. Shading of rivers represents the average education polygenic score for students in a particular course in a particular year, ranging from low (orange) to high (blue).

Fig. 4. Student tracking and persistence vary as a function of school.

a Students with high education-associated polygenic scores are more likely to be tracked into advanced math in advantaged schools than in disadvantaged schools. Fitted probabilities of being tracked to Geometry or higher in the 9th grade, based on cumulative link logit model. School status measured by percent of students whose mother graduated from high school. See Supplementary Table 3 for full model results. b Students with low education-associated polygenic scores persist more in math in advantaged schools than in disadvantaged schools. Model-implied number of advancing steps from 9–to–12th-grade, based on Poisson model. At least 1 year of math beyond the 9th grade was compulsory in most U.S. states. See Supplementary Table 4 for full model results.