Schizophrenia Genetics and Neuropsychiatric Features in Childhood-onset Systemic Lupus Erythematosus
Bibliographic record
Abstract
Objective We examined the association between schizophrenia genetic susceptibility loci and neuropsychiatric systemic lupus erythematosus (NPSLE) features in childhood-onset SLE (cSLE) participants. Methods Study participants from the Lupus Clinic at the Hospital for Sick Children, Toronto, met ≥ 4 of the American College of Rheumatology and/or SLE International Collaborating Clinics SLE classification criteria and were genotyped using the Illumina Multi-Ethnic Global Array or the Global Screening Array. Ungenotyped single-nucleotide polymorphisms (SNPs) were imputed, and ancestry was genetically inferred. We calculated 2 additive schizophrenia-weighted polygenic risk scores (PRS) using (1) genome-wide significant SNPs (P < 5 × 10–8), and (2) an expanded list of SNPs with significance at P < 0.05. We defined 2 outcomes compared to absence of NPSLE features: (1) any NPSLE feature, and (2) subtypes of NPSLE features (psychosis and nonpsychosis NPSLE). We completed logistic and multinomial regressions, first adjusted for inferred ancestry only and then added for variables significantly associated with NPSLE in our cohort (P < 0.05). Results We included 513 participants with cSLE. Median age at diagnosis was 13.8 years (IQR 11.2–15.6), 83% were female, and 31% were of European ancestry. An increasing schizophrenia genome-wide association PRS was not associated with NPSLE (OR 1.04, 95% CI 0.87–1.26, P = 0.62), nor with the NPSLE subtypes, psychosis (OR 0.97, 95% CI 0.73–1.29, P = 0.84) and other nonpsychosis NPSLE (OR 1.08, 95% CI 0.88–1.34, P = 0.45), in ancestry-adjusted models. Results were similar for the model including covariates (ancestry, malar rash, oral/nasal ulcers, arthritis, lymphopenia, Coombs-positive hemolytic anemia, lupus anticoagulant, and anticardiolipin antibodies) and for the expanded PRS estimates. Conclusion We did not observe an association between known risk loci for schizophrenia and NPSLE in a multiethnic cSLE cohort. This work warrants further validation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".