Meta-analysis of epigenome-wide association studies of major depressive disorder
Bibliographic record
Abstract
Abstract Epigenetic mechanisms have been hypothesized to play a role in the etiology of major depressive disorder (MDD). In this study, we performed a meta-analysis between two case–control MDD cohorts to identify differentially methylated positions (DMPs) and differentially methylated regions (DMRs) in MDD. Using samples from two Cohorts (a total of 298 MDD cases and 63 controls with repeated samples, on average ~ 1.8 samples/subject), we performed an EWAS meta-analysis. Multiple cytosine-phosphate-guanine sites annotated to TNNT3 were associated with MDD reaching study-wide significance, including cg08337959 (p = 2.3 × 10–11). Among DMPs with association p values less than 0.0001, pathways from REACTOME such as Ras activation upon Ca2+ influx through the NMDA receptor (p = 0.0001, p-adjusted = 0.05) and long-term potentiation (p = 0.0002, p-adjusted = 0.05) were enriched in this study. A total of 127 DMRs with Sidak-corrected p value < 0.05 were identified from the meta-analysis, including DMRs annotated to TNNT3 (chr11: 1948933 to 1949130 [6 probes], Sidak corrected P value = 4.32 × 10–41), S100A13 (chr1: 153599479 to 153600972 [22 probes], Sidak corrected P value = 5.32 × 10–18), NRXN1 (chr2: 50201413 to 50201505 [4 probes], Sidak corrected P value = 1.19 × 10–11), IL17RA (chr22: 17564750 to 17565149, Sidak corrected P value = 9.31 × 10–8), and NPFFR2 (chr4: 72897565 to 72898212, Sidak corrected P value = 8.19 × 10–7). Using 2 Cohorts of depression case–control samples, we identified DMPs and DMRs associated with MDD. The molecular pathways implicated by these data include mechanisms involved in neuronal synaptic plasticity, calcium signaling, and inflammation, consistent with reports from previous genetic and protein biomarker studies indicating that these mechanisms are involved in the neurobiology of depression.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.022 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".