Tuesday, April 09, 2019

Genomic prediction of student flow through high school math curriculum

Compute polygenic EA scores for 3000 US high school students of European ancestry. Track individual progress from 9th to 12th grade, focusing on mathematics courses. The students are out-of-sample: not used in training of predictor. In fact, a big portion (over half?) of individuals used in predictor training are not even from the US -- they are from the UK/EU.

Results: predictor captures about as much variance as family background (SES = Social Economic Status). Students with lower polygenic scores are less likely to take advanced math (e.g., Geometry and beyond).

Typical education paths of individuals with, e.g., bottom few percentile polygenic score are radically different from those in the top percentiles, even after controlling for SES. For example, consider only rich kids or kids at superior schools and compare educational trajectory vs polygenic score. Looks like (bottom figure) odds ratio for taking Geometry in 9th grade is about 4-6x higher for top polygenic score kids.
Genetic Associations with Mathematics Tracking and Persistence in Secondary School

K. Paige Harden and Benjamin W. Domingue, et al.

...we address this question using student polygenic scores, which are DNA-based indicators of propensity to succeed in education8. We integrated genetic and official school transcript data from over 3,000 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...

...including family-SES and school-SES as covariates attenuated the association between the education-PGS and mathematics tracking in the 9th-grade only by about 20% (attenuated from b = 0.583, SE = .034, to b = 0.461, SE = .036, p < 2 × 10-16, Supplementary Table S3). Note that the association with genetics was roughly comparable in magnitude to the association with familySES...

See also Game Over: Genomic Prediction of Social Mobility (some overlap in authors with the new paper).

A talk by the first author:

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