Gene—Environment Interactions in Major Depressive Disorder
Bibliographic record
Abstract
Family, twin, and epidemiologic studies have suggested that both genes and environment are important risk factors for the development of major depressive disorder (MDD). In the absence of consistent and strong main genetic effects, numerous studies have supported gene–environment interactions in this disorder. While the impact of negative environmental factors, such as early life stress, traumatic experiences, and negative life events have been established as risk factors, they are not sufficient to predict MDD. This article will review evidence suggesting that genetic variants moderate the effects of adversities on the development of MDD, with a focus on the importance of careful characterization of the stressful life events as well as systemic and molecular mechanisms that potentially mediate these gene–environment interactions. Les études sur les familles, les jumeaux et l'épidémiologie suggèrent que les gènes et l'environnement sont d'importants facteurs de risque du développement d'un trouble dépressif majeur (TDM). En l'absence de grands effets génétiques cohérents et marqués, nombre d'études soutiennent des interactions gène–environnement dans ce trouble. Bien que l'effet de facteurs environnementaux négatifs, comme le stress en début de vie, les expériences traumatiques et les événements négatifs de la vie ait été établi comme facteur de risque, il ne suffit pas à prédire le TDM. Cet article examine les données probantes qui suggèrent que les variantes génétiques modèrent les effets de l'adversité sur le développement du TDM, et met l'accent sur l'importance de la caractérisation prudente des événements stressants de la vie ainsi que des mécanismes systémiques et moléculaires qui assistent potentiellement ces interactions gène–environnement.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, 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".