Genomic imprinting in the development and evolution of psychotic spectrum conditions
Bibliographic record
Abstract
I review and evaluate genetic and genomic evidence salient to the hypothesis that the development and evolution of psychotic spectrum conditions have been mediated in part by alterations of imprinted genes expressed in the brain. Evidence from the genetics and genomics of schizophrenia, bipolar disorder, major depression, Prader-Willi syndrome, Klinefelter syndrome, and other neurogenetic conditions support the hypothesis that the etiologies of psychotic spectrum conditions commonly involve genetic and epigenetic imbalances in the effects of imprinted genes, with a bias towards increased relative effects from imprinted genes with maternal expression or other genes favouring maternal interests. By contrast, autistic spectrum conditions, including Kanner autism, Asperger syndrome, Rett syndrome, Turner syndrome, Angelman syndrome, and Beckwith-Wiedemann syndrome, commonly engender increased relative effects from paternally expressed imprinted genes, or reduced effects from genes favouring maternal interests. Imprinted-gene effects on the etiologies of autistic and psychotic spectrum conditions parallel the diametric effects of imprinted genes in placental and foetal development, in that psychotic spectrum conditions tend to be associated with undergrowth and relatively-slow brain development, whereas some autistic spectrum conditions involve brain and body overgrowth, especially in foetal development and early childhood. An important role for imprinted genes in the etiologies of psychotic and autistic spectrum conditions is consistent with neurodevelopmental models of these disorders, and with predictions from the conflict theory of genomic imprinting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".