PT  - JOURNAL ARTICLE
AU  - Sophie von Stumm
AU  - Emily Smith-Woolley
AU  - Ziada Ayorech
AU  - Andrew McMillan
AU  - Kaili Rimfeld
AU  - Philip S. Dale
AU  - Robert Plomin
TI  - Predicting educational achievement from genomic measures and socioeconomic status
AID  - 10.1101/538108
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 538108
4099  - http://biorxiv.org/content/early/2019/02/04/538108.short
4100  - http://biorxiv.org/content/early/2019/02/04/538108.full
AB  - 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; controlling for genetic influence on SES reduces its predictive power to 16%. 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 effects of GPS and SES are primarily additive, suggesting that their joint impact is particularly dramatic for children at the extreme ends of the distribution.