Interrogating the Evolutionary Paradox of Schizophrenia: A Novel Framework and Evidence Supporting Recent Negative Selection of Schizophrenia Risk Alleles
Bibliographic record
Abstract
Schizophrenia is a psychiatric disorder with a worldwide prevalence of ∼1%. The high heritability and reduced fertility among schizophrenia patients have raised an evolutionary paradox: why has negative selection not eliminated schizophrenia associated alleles during evolution? To address this question, we examined evolutionary markers, known as modern-human-specific (MD) sites and archaic-human-specific sites, using existing genome-wide association study (GWAS) data from 34,241 individuals with schizophrenia and 45,604 healthy controls included in the Psychiatric Genomics Consortium (PGC). By testing the distribution of schizophrenia single nucleotide polymorphisms (SNPs) with risk and protective effects in the human-specific sites, we observed a negative selection of risk alleles for schizophrenia in modern humans relative to archaic humans (e.g., Neanderthal and Denisovans). Such findings indicate that risk alleles of schizophrenia have been gradually removed from the modern human genome due to negative selection pressure. This novel evidence contributes to our understanding of the genetic origins of schizophrenia.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".