The Landscape of Shared and Divergent Genetic Influences across 14 Psychiatric Disorders
Bibliographic record
Abstract
Abstract Psychiatric disorders display high levels of comorbidity and genetic overlap 1,2 . Genomic methods have shown that even for schizophrenia and bipolar disorder, two disorders long-thought to be etiologically distinct 3 , the majority of genetic signal is shared 4 . Furthermore, recent cross-disorder analyses have uncovered over a hundred pleiotropic loci shared across eight disorders 5 . However, the full scope of shared and disorder-specific genetic basis of psychopathology remains largely uncharted. Here, we address this gap by triangulating across a suite of cutting-edge statistical genetic and functional genomic analyses applied to 14 childhood- and adult-onset psychiatric disorders (1,056,201 cases). Our analyses identify and characterize five underlying genomic factors 6 that explain the majority of the genetic variance of the individual disorders (∼66% on average) and are associated with 268 pleiotropic loci. We observed particularly high levels of polygenic overlap 7 and local genetic correlation 8 and very few disorder-specific loci 9 for two factors defined by: ( i ) schizophrenia and bipolar disorder (“SB factor”), and by ( ii ) major depression, PTSD, and anxiety (“internalizing factor”). At the functional level, we applied multiple methods 10–12 which demonstrated that the shared genetic signal across the SB factor was substantially enriched in genes expressed in excitatory neurons, whereas the internalizing factor was associated with oligodendrocyte biology. By comparison, the genetic signal shared across all 14 disorders was enriched for broad biological processes (e.g., transcriptional regulation). These results indicate increasing differentiation of biological function at different levels of shared cross-disorder risk, from quite general vulnerability to more specific pathways associated with subsets of disorders. These observations may inform a more neurobiologically valid psychiatric nosology and implicate novel targets for therapeutic developments designed to treat commonly occurring comorbid presentations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".