Genome-wide approaches to antidepressant treatment: working towards understanding and predicting response
Bibliographic record
Abstract
Antidepressants are among the most commonly prescribed drugs, and a range of medications are available. However, treatment response to a particular drug varies greatly between patients, with only 30% of patients responding well to the first treatment administered. Given evidence that antidepressant treatment response is a heritable trait, together with technological advances in genetic research, three recently published genome-wide investigations into antidepressant responses have examined the determinants of variability in treatment outcomes between depressed patients. Here, we review these studies within the context of wider research efforts to identify treatment response predictors. Some interesting genes have been implicated, but no variants have yet been robustly and reliably linked to response. This may suggest that genetic effect sizes are smaller than originally anticipated. Candidate gene approaches in these samples have lent support to the involvement of serotonergic, glutamatergic and stress-response systems in treatment response, although corroborative evidence from genome-wide analyses indicates these results should be interpreted cautiously. Closer examination of antidepressant response, considering it as a complex trait, has indicated that multiple genes of small effect are likely to be involved. Furthermore, there is some evidence that genetic influence on response to treatment may vary between patients with different symptom profiles or environmental exposures. This has implications for the translation of pharmacogenetic findings into clinical practice: genotypic information from multiple loci and data on non-genetic factors are likely to be needed to tailor antidepressant treatment to the individual patient.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".