A genomic investigation of major depressive disorder and antidepressant response
Bibliographic record
Abstract
Major depressive disorder (MDD) is a common and complex disorder with consistent evidence of genetic influence on predisposition. The search for susceptibility genes has proved to be an arduous task with studies having ever increasing sample numbers still not leading to replicable findings. It is generally believed that the failure of common genetic studies to identify replicable genetic associations is a consequence of the multifactorial and heterogeneous nature of MDD. This etiological heterogeneity may also explain significant variability in treatment response. Accordingly, while effective treatments for MDD are available, approximately half of the patients fail to respond to conventional antidepressant treatment. We hypothesized that peripheral gene expression could help us better understand illness heterogeneity and mechanisms of antidepressant response, possibly helping to identify biomarkers. To test this hypothesis, we have prospectively followed, and treated with the antidepressant citalopram for eight weeks, a cohort of medication naive individuals with MDD. RNA and DNA from pre- and post-treatment blood samples of this cohort were used to perform high-throughput pharmacogenomic and genetic studies to identify genes involved in treatment response and in the pathophysiology of MDD. Significant gene expression alterations in immune related genes were observed after citalopram treatment, pointing to a possible mode of action of the treatment response, as well as possible biomarkers for future treatment response. Additionally, we identified copy number variable regions differentiating MDD and controls and having a significant impact on gene expression. These results provide important additional information which can be used to identify the genomic and molecular underpinnings of MDD and antidepressant response.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".