Bibliographic record
Abstract
Despite significant effort, understanding of the molecular causes and mechanisms of bipolar disorder (BD) remains a major challenge. Numerous molecular genetic linkage and association studies have been conducted over the last two decades; however, the data are quite inconsistent or even controversial. This article develops an argument that molecular studies of BD would benefit significantly from adding an epigenetic (epiG) perspective. EpiG factors refer to modifications of DNA and chromatin that "orchestrate" the activity of the genome, including regulation of gene expression. EpiG mechanisms are consistent with various non-Mendelian features of BD such as the relatively high degree of discordance in monozygotic (MZ) twins, the critical age group for susceptibility to the disease, clinical differences in males and females, and fluctuation of the disease course, including interchanges of manic and depressive phases, among others. Apart from the phenomenological consistency, molecular epiG peculiarities may shed new light on the understanding of controversial molecular genetic findings. The relevance of epigenetics for the molecular studies of BD is demonstrated using the examples of genetic studies of BD on chromosome 11p and the X chromosome. A spectrum of epiG mechanisms such as genomic imprinting, tissue-specific effects, paramutagenesis, and epiG polymorphism, as well as epiG regulation of X chromosome inactivation, is introduced. All this serves the goal of demonstrating that epiG factors cannot be ignored anymore in complex phenotypes such as BD, and systematic large-scale epiG studies of BD have to be initiated.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".