Genetic Predisposition of Vulnerable Groups to Schizophrenia and Bipolar I via AKAP11 Variant Analysis
Bibliographic record
Abstract
Recent exome sequencing analyses have shown that schizophrenia and bipolar I disorder, collectively affecting 2% of the population, may extend from a common genetic origin of the AKAP11 gene. Though several studies have been put forth to examine the relationship between the pathogenesis of these mental illnesses and AKAP11 variants, a model has yet to be created that tests this hypothesis by accounting for real–world diagnosis frequencies in addition to a genetic framework. Analogously, no genetic study has been put forth to identify specific vulnerable groups in the development of either schizophrenia or bipolar disorder. To provide additional insight into the pathogenesis of these diseases, we perform ordinal logistic regression on every AKAP11 variant in the SCHEMA and BipEx data sets. Primarily using the CADD genome annotation, we establish the probability that each variant is deleterious. We use a chi–square goodness of fit test to demonstrate that the amino acids coded for by high–risk variants are similar between schizophrenia and bipolar disorder, suggesting a common underlying genetic origin. Additionally, a sequence of one–sided t tests is run to compare the age and sex frequencies of these high–risk variants to the frequencies of schizophrenia and bipolar I diagnoses among the Canadian population. In total, we find that AKAP11 exhibits a strong correlation with bipolar I disorder and a moderate correlation with schizophrenia. We also find that males under 30 and females between 50 and 55 are most vulnerable to schizophrenia and bipolar I, respectively.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".