Educational Attainment Polygenic Scores, Socioeconomic Factors, and Cortical Structure in Children and Adolescents
Bibliographic record
Abstract
Abstract Background Genome-wide polygenic scores for educational attainment (PGS-EA) and socioeconomic factors, which are correlated with each other, have been consistently associated with academic achievement and general cognitive ability in children and adolescents. Yet, the independent associations of PGS-EA and socioeconomic factors with specific underlying factors at the neural and neurocognitive levels are not well understood. The goal of this study was to examine the unique contributions of PGS-EA and parental education to cortical thickness (CT), cortical surface area (SA), and neurocognitive skills in children and adolescents. Methods Participants were typically developing children and adolescents (3-21 years of age; 53% male; N = 391). High-resolution, T1-weighted magnetic resonance imaging data were acquired. PGS-EA were computed based on the most recent genome-wide association study of educational attainment. Sustained attention, inhibitory control, working memory, vocabulary, and episodic memory were measured. Results PGS-EA and parental education were independently and significantly associated with SA, vocabulary, and attention outcomes but were not associated with CT. Vertex-wise analyses indicated that higher PGS-EA was significantly associated with greater SA in the left medial orbitofrontal gyrus and inferior frontal gyrus after accounting for parental education. Higher parental education was significantly associated with greater SA in the left parahippocampal gyrus after accounting for PGS-EA. Conclusions These findings suggest that education-linked genetics may influence SA, particularly in certain frontal regions, leading to variability in academic achievement. Results suggested genetic confounding in associations between parental education and SA in children and adolescents, with these associations remaining significant after controlling for PGS-EA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".