Predicting educational achievement from genomic measures and socioeconomic status

Predicting educational achievement from genomic measures and socioeconomic status. Sophie von Stumm et al. Developmental Science, November 23 2019. https://doi.org/10.1111/desc.12925

Abstract: The two best predictors of children’s educational achievement available from birth are parents’ socioeconomic status (SES) and, recently, children’s inherited DNA differences that can be aggregated in genome‐wide polygenic scores (GPS). Here we chart for the first time the developmental interplay between these two predictors of educational achievement at ages 7, 11, 14 and 16 in a sample of almost 5,000 UK school children. We show that the prediction of educational achievement from both GPS and SES increases steadily throughout the school years. Using latent growth curve models, we find that GPS and SES not only predict educational achievement in the first grade but they also account for systematic changes in achievement across the school years. At the end of compulsory education at age 16, GPS and SES respectively predict 14% and 23% of the variance of educational achievement. Analyses of the extremes of GPS and SES highlight their influence and interplay: In children who have high GPS and come from high SES families, 77% go to university, whereas 21% of children with low GPS and from low SES backgrounds attend university. We find that the associations of GPS and SES with educational achievement are primarily additive, suggesting that their joint influence is particularly dramatic for children at the extreme ends of the distribution.

Conclusions

Our major finding is that SES and inherited DNA differences aggregated in GPS are powerful
predictors of educational achievement, accounting together for 27% of children's differences in
achievement across the course of compulsory schooling. The influence of GPS and SES is
particularly dramatic at the extremes of the distribution. We suggested, for example, that
GPS partially compensates for the disadvantages of children from low-SES families, increasing
their chances of going to university from 21% to 47%. This raises the possibility of doing more to
help this group reach its full potential. Nonetheless, the substantial overlap between the
distributions of scores within the lowest and highest deciles for GPS and SES indicates the limits
of prediction at the level of individual students.

The potential application of predictive capacity of the kind demonstrated here will require
complex decision-making. The basis for those decisions goes beyond purely scientific criteria to
issues of ethics and social values. Papers like the present one provide an essential empirical
grounding for discussion. It is our hope that our results and others like them can serve to open
doors for individual children, not close them, by stimulating the development and provision of
personalized environments that can appropriately enhance, supplement, and remediate
educational achievement.