Influence of polygenic risk scores for schizophrenia and resilience on the cognition of individuals at-risk for psychosis
Bibliographic record
Abstract
Cognitive impairment is a core feature of schizophrenia which precedes the onset of full psychotic symptoms, even in the ultra-high-risk stage (UHR). Polygenic risk scores (PRS) can be computed for many psychiatric disorders and phenotyping traits, including scores for resilience. We explored the correlations between several PRS and neurocognition in UHR individuals. We included 107 UHR individuals; 29.9% of them converted to psychosis (UHR-C) while 57.0% did not (UHR-NC) during the 1-year follow-up. Cognitive performances were assessed with the Wechsler Adult Intelligence Scale estimating the Intelligence Quotient (IQ), the Trail Making Test, the verbal fluency, the Stroop test, and the Wisconsin card sorting test. Linear regression models were used to test their association with the PRS for schizophrenia, bipolar disorder, major depression, ADHD, cross-disorders, cognitive performance, intelligence, education attainment, and resilience to schizophrenia. UHR-C had a lower IQ than UHR-NC. The PRS for schizophrenia negatively correlated with IQ, while the PRS for cognitive performance and for resilience positively correlated with IQ. PRS for schizophrenia showed a significant correlation with working memory and processing speed indices. PRS for schizophrenia showed a higher effect on IQ in UHR-NC, and UHR-NC with high PRS for schizophrenia had a similar IQ as UHR-C. Conversely, UHR-C with a high PRS for resilience performed as well as UHR-NC. Our findings suggest that cognitive deficits may predate the onset of psychosis. The genetic architecture of schizophrenia seems to impacts the cognition in UHR-NC. Cognition is also mediated by PRS for resilience.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".