Comparative immunogenetics of autism and schizophrenia
Bibliographic record
Abstract
Autism and schizophrenia are highly heritable neurodevelopmental disorders, each mediated by a diverse suite of genetic and environmental risk factors. Comorbidity and familial aggregation of such neurodevelopmental disorders with other disease-related conditions can provide important insights into their etiology. Epidemiological studies have documented reduced rates of rheumatoid arthritis, a systemic autoimmune condition, in schizophrenia, and recent work has shown increased rates of rheumatoid arthritis in first-degree relatives of autistic individuals, especially mothers. Advances in understanding the genetic basis of rheumatoid arthritis have shown that much of the genetic liability to this condition is due to risk and protective alleles at the HLA DRB1 locus. These data allow robust testing of the hypotheses that allelic variation at DRB1 pleiotropically modulates risk of rheumatoid arthritis, autism and schizophrenia. Systematic review of the literature indicates that reported associations of DRB1 variants with these three conditions are congruent with a pleiotropic model: DRB1*04 alleles have been associated with increased risk of rheumatoid arthritis and autism but decreased risk of schizophrenia, and DRB1*13 alleles have been associated with protection from rheumatoid arthritis and autism but higher risk of schizophrenia. These convergent findings from genetics and epidemiology imply that a subset of autism and schizophrenia cases may be underlain by genetically based neuroimmune alterations, and that analyses of the causes of risk and protective effects from DRB1 variants may provide new approaches to therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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".