Genetic Architecture of Cortical Thickness and White Matter hyperintensities: Evidence of Gene‐Environment Interaction with Cardiovascular Health and Late‐Life Depression
Bibliographic record
Abstract
Abstract Background Modifiable risk factors account for over 40% of dementias, offering potential for intervention. However, the impact of these risk factors, including depression and vascular disease, on brain health in the context of variable genetic influences is not well understood. Here we map the genetic landscape of brain structure, testing for gene‐by‐environment (GxE) interactions and relevance to cognitive performance in mid‐ and late‐life. Method We conducted genome‐wide association studies (GWAS) to identify genetic variants associated with cortical thickness in over 34,500 UK Biobank participants. We used postmortem bulk RNA sequencing of frontal cortex paired with antemortem MRI in a late‐life sample (n = 66) to directly corroborate implicated GWAS loci. Focusing on GWAS‐significant loci, we tested for GxE interactions and associations with cognition in 25,254 Canadian Longitudinal Study of Ageing (CLSA) participants. Result We identified 367 loci in the UK Biobank participants (age 45‐81), associated with global or regional cortical thickness (p<5×10−8), with genetic correlations between pairs of anatomical regions identifying three distinct modules. Corroboration with postmortem bulk RNA sequencing and MRI in a late‐life sample confirmed effects of STMN4, ARL17A and ARL17B genes on cortical thickness. Ten GWAS‐implicated loci from the UK Biobank were also associated with executive function or memory in CLSA, including rs1562330, a locus linked to the STMN4 gene. Notably, 37 variants also showed significant interactions with cardiovascular health or depression (P<2×10−4). Conclusion These findings advance our understanding of the genetic architecture of brain structure and show that modifiable factors alter the effects of heritable genetic background on brain and cognitive health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".