Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia, Bipolar Disorder, and Depression in the VA Health Care System
Bibliographic record
Abstract
ABSTRACT Background Serious mental illnesses, including schizophrenia, bipolar disorder and depression are heritable, highly multifactorial disorders and major causes of disability worldwide. Polygenic risk scores (PRS) aggregate variants identified from genome-wide association studies (GWAS) into individual-level estimates of liability, and are a promising tool for clinical risk stratification. Methods By leveraging the VA’s extensive electronic health record (EHR) and a cohort of 9,378 individuals with confirmed diagnoses of schizophrenia or bipolar I disorder, we validated automated case-control assignments based on ICD-9/10 codes, and benchmarked the performance of current PRS for schizophrenia, bipolar disorder, and major depression in 400,000 Million Veteran Program (MVP) participants. We explored broader relationships between PRS and 1,650 disease categories via phenome-wide association studies (PheWAS). Finally, we applied genomic structural equation modeling (gSEM) to derive novel PRS indexing common and disorder-specific latent genetic factors. Findings Among 3,953 and 5,425 individuals with diagnoses of schizophrenia or bipolar disorder type I that were confirmed by structured clinical interviews, 95% were correctly identified using ICD-9/10 codes (2 or more). Current PRS were robustly associated with case status in European (p<10 −254 ) and African (p<10 −5 ) participants and were higher among more frequently hospitalized patients (p<10 −4 ). PheWAS confirmed previous associations among higher neuropsychiatric PRS and elevated risk for psychiatric and physical health problems and extended these findings to African Americans. Interpretation Using diagnoses confirmed by in-person structured clinical interviews and current neuropsychiatric PRS, we demonstrated the validity of an EHR-based phenotyping approach in US veterans, highlighting the potential of PRS for disentangling biological and mediated pleiotropy. Funding Department of Veterans Affairs Cooperative Studies Program (CSP) #572; Million Veteran Program (MVP-000, MVP-006); Office of Research and Development, Department of Veterans Affairs.
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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 | 0.003 | 0.012 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".