Genetic analysis of psychiatric disorders associated with human chromosome 18.
Bibliographic record
Abstract
Current models on the etiology of psychiatric disorders support the idea of a biologic cause as well as interactions of biologic systems with the environment. The elucidation of the genetic etiology is of paramount importance to understand the cause of psychiatric disorders. Human chromosome 18 was identified as one of the first chromosomes to be aberrant in psychiatric patients and has subsequently served as a model to identify the molecular cause. In this article I review a multitude of methodologies that can be used in determining the genetic basis of schizophrenia, affective disorder and autism associated with human chromosome 18. These strategies include the use of chromosome aberrations, linkage and association studies, mouse-human comparative genomics, mutation analysis on candidate genes, trinucleotide repeat expansion studies, search for genes demonstrating parental effects and bioinformatics. Current data from the use of these methods are cited from the literature. Linkage and association studies have suggested at least 2 candidate loci on the short and long arms of chromosome 18 for each of these psychiatric disorders. Some loci are supported by the mapping of chromosome aberrations from psychiatric patients. Mutation analyses of psychiatric patients with 4 candidate genes (NEDD4L, IMPA2, PACAP and GNAL) mapping within these loci have been unsuccessful, although an association was found with the IMPA2 gene in patients with schizophrenia. With these methods and findings, our understanding of the cause of psychiatric disorders associated with human chromosome 18 has improved and will advance, especially with emerging data from the human and rodent genome projects.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".