Cross-disorder analysis of schizophrenia and 19 immune diseases reveals genetic correlation
Bibliographic record
Abstract
Abstract Epidemiological studies indicate that many immune diseases occur at different rates among people with schizophrenia compared to the general population. Here, we evaluated whether this phenotypic correlation between immune diseases and schizophrenia might be explained by shared genetic risk factors ( genetic correlation ). We used data from a large genome-wide association study (GWAS) of schizophrenia (N=35,476 cases and 46,839 controls) to compare the genetic architecture of schizophrenia to 19 immune diseases. First, we evaluated the association with schizophrenia of 581 variants previously reported to be associated with immune diseases at genome-wide significance. We identified three variants with pleiotropic effects, located in regions associated with both schizophrenia and immune disease. Our analyses provided the strongest evidence of pleiotropy at rs1734907 (∼85kb upstream of EPHB4 ), a variant which was associated with increased risk of both Crohn’s disease (OR = 1.16, P = 1.67×10 −13 ) and schizophrenia (OR = 1.07, P = 7.55×10 −6 ). Next, we investigated genome-wide sharing of common variants between schizophrenia and immune diseases using polygenic risk scores (PRS) and cross-trait LD Score regression (LDSC). PRS revealed significant genetic overlap with schizophrenia for narcolepsy (p=4.1×10 −4 ), primary biliary cirrhosis (p=1.4×10 −8 ), psoriasis (p=3.6×10 −5 ), systemic lupus erythematosus (p=2.2×10 −8 ), and ulcerative colitis (p=4.3×10 −4 ). Genetic correlations between these immune diseases and schizophrenia, estimated using LDSC, ranged from 0.10 to 0.18 and were consistent with the expected phenotypic correlation based on epidemiological data. We also observed suggestive evidence of sex-dependent genetic correlation between schizophrenia and multiple sclerosis (interaction p=0.02), with genetic risk scores for multiple sclerosis associated with greater risk of schizophrenia among males but not females. Our findings suggest that shared genetic risk factors contribute to the epidemiological co-occurrence of schizophrenia and certain immune diseases, and suggest that in some cases this genetic correlation is sex-dependent. Author Summary Immune diseases occur at different rates among patients with schizophrenia compared to the general population. While the reasons for this phenotypic correlation are unclear, shared genetic risk ( genetic correlation ) has been proposed as a contributing factor. Prior studies have estimated the genetic correlation between schizophrenia and a handful of immune diseases, with conflicting results. Here, we performed a comprehensive cross-disorder investigation of schizophrenia and 19 immune diseases. We identified three individual genetic variants associated with both schizophrenia and immune diseases, including a variant near EPHB4 – a gene whose protein product guides the migration of lymphocytes towards infected cells in the immune system and the migration of neuronal axons in the brain. We demonstrated significant genome-wide genetic correlation between schizophrenia and narcolepsy, primary biliary cirrhosis, psoriasis, systemic lupus erythematosus, and ulcerative colitis. Finally, we identified a potential sex-dependent pleiotropic effect between schizophrenia and multiple sclerosis. Our findings point to shared genetic risk for schizophrenia and at least a subset of immune diseases, which likely contributes to their epidemiological co-occurrence. These results raise the possibility that the same genetic variants may exert their effects on neurons or immune cells to influence the development of psychiatric and immune disorders, respectively.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".