Predicting educational achievement from genomic measures and socioeconomic status

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; 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.

Polygenic prediction of school performance in children with and without psychiatric disorders

“…First, we studied the association of EA-PGS with school performance in the full sample and compared the results to those from previous studies. 8,9 In our sample, the maximum variance explained in school performance by EA-PGS was 8.4%. This was lower compared to the estimates reported in previous studies 8,9 (R 2 =14%).…”

“…Most of the previous studies on the association of EA-PGS with EA or school performance were based on individuals from the general population. [7][8][9] It was therefore not clear if results from such studies could be generalized to those with psychiatric disorders. Nation-wide register based cohorts such as iPSYCH 25 are well suited to address such research questions as they offer a unique opportunity to study both the genetic and environmental factors of multiple psychiatric disorders at the same time within a single population setting.…”

“…In summary, our initial analysis confirmed that EA-PGS significantly associate with school performance and explain a substantial proportion of variance in school grades thereby corroborating with previous studies. [7][8][9] Benchmarking effect sizes of polygenic deciles against ADHD Given the strong correlation between EA-PGS and school performance, it can be expected that school performance of those at the lower end of the EA-PGS distribution might be comparable to the school performance of those with learning difficulties. However, to our knowledge, this has not been objectively evaluated so far using a proper reference group such as those with learning difficulties.…”

“…4 Likewise, EA-PGS has also been shown to associate strongly with school performance at around 16 years of age, explaining up to 14% of the phenotypic variance in school grades. [7][8][9] Although EA-PGS strongly associates with school grades, its association is influenced by numerous factors such as ancestry, 10 environmental factors 11 and indirect genetic effects such as genetic nurture 12 and social genetic effects. 13 Existing GWASs of EA have been conducted in individuals from European ancestries.…”

Cognitive and brain development is independently influenced by socioeconomic status and polygenic scores for educational attainment

“…If this result reflects a true effect, it could represent a catch-up -subjects with lower WM show greater gains in WM during adolescence. However, a previous study of PGS and SES on IQ showed a widening gap between subjects with low and high EduYears-PGS 45 . At least in part, our results here could alternatively be explained by ceiling effects, which could artificially lead to high performing subjects having less room to improve.…”