Genome-wide association analyses identify distinct genetic architectures for early-onset and late-onset depression
Bibliographic record
Abstract
Abstract Major depressive disorder (MDD) is a common and heterogeneous disorder of complex etiology. Studying more homogeneous groups stratified according to clinical characteristics, such as age of onset, can improve the identification of the underlying genetic causes and lead to more targeted treatment strategies. We leveraged Nordic biobanks with longitudinal health registries to investigate differences in the genetic architectures of early-onset (eoMDD; n = 46,708 cases) and late-onset (loMDD; n = 37,168 cases) MDD. We identified 12 genomic loci for eoMDD and two for loMDD. Overall, the two MDD subtypes correlated moderately (genetic correlation, r g = 0.58) and differed in their genetic correlations with related traits. These findings suggest that eoMDD and loMDD have partially distinct genetic signatures, with a specific developmental brain signature for eoMDD. Importantly, we demonstrate that polygenic risk scores (PRS) for eoMDD predict suicide attempts within the first 10 years after the initial diagnosis: the absolute risk for suicide attempt was 26% in the top PRS decile, compared to 12% and 20% in the bottom decile and the intermediate group, respectively. Taken together, our findings can inform precision psychiatry approaches for MDD.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".