Génétique et épigénétique de la schizophrénie et des psychoses
Bibliographic record
Abstract
Schizophrenia and other psychoses are categorical psychiatric diagnoses corresponding to frequent and heterogeneous disorders. Their physiopathology still remains largely unknown despite numerous recent advances. In particular, the last decade has identified different types of genetic variants, thanks to emergence of high-throughput methods. These methods allow both the identification of rare variants with a large effect such as punctual mutations or copy-number variants and the identification of frequent variants with a limited effect such as polymorphisms. Many impacted genes have been identified showing a very high genetic heterogeneity of psychoses. These genes are overrepresented in synaptic and neurotransmission pathways. Only a small fraction of psychoses could be easily explained by genetics but this screening in clinical practice is important as it can lead to therapeutic challenge or genetic counselling. Nowadays, it is clear that the pathophysiology of the psychoses can only be understood by an integrative approach taking into account the interaction between genes and environment. This interaction could be mediated by the epigenome defined as the modification of gene expression without changes in DNA sequence. Epigenome is stable but could be modified by environmental factors. Several epigenetic mechanisms have been studied in psychosis, in particular the DNA methylation, the modification of histones and the microRNA. All of these mechanisms are under regulation by genetic factors and variants in these epigenetic-involved genes and cofactors have been also associated with schizophrenia. Thus, pathophysiology of psychosis is complex and morestudiesare needed before definitive conclusions. Altogether, the recent advances in the genetics and epigenetics of psychosis are promising and could open the way to a recategorization of these disorders as well as the identification of new therapeutic targets.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".