Subphenotype mapping in systemic lupus erythematosus identifies multiple novel loci associated with circulating interferon alpha
Bibliographic record
Abstract
Systemic lupus erythematosus (SLE) is a chronic autoimmune disorder characterized by inflammation of multiple organ systems, loss of tolerance to self-antigens, and dysregulated interferon responses. SLE is both genetically and phenotypically heterogeneous, and we hypothesize that this greatly reduces the power of overall case-control studies in SLE. Increased circulating level of the cytokine interferon alpha (IFNα) is a stable, heritable trait in SLE which has been implicated in primary disease pathogenesis. Forty to 50% of patients have high IFNα, and high levels correspond with clinical differences. To study genetic heterogeneity in SLE, we performed a case-case study comparing patients with high versus low IFNα in over 1,800 SLE cases. Four hundred European ancestry cases formed the discovery GWAS set, and 1,443 cases from a large independent multi-ancestral replication cohort were used to validate associations. In meta-analysis, the top associations in European ancestry were PRKG1 rs7897633 ( P Meta = 2.75 × 10 ) and PNP rs1049564 (P Meta = 1.24 × 10 ). We also found evidence for cross-ancestral background associations with the ANKRD44 and PLEKHF2 loci. These loci have not been previously identified in case-control SLE genetics studies. Bioinformatic analyses implicate these loci functionally in dendritic cells and natural killer cells, both of which are involved in IFNα production in SLE. As case-control studies of complex heterogeneous diseases reach a limit of feasibility with respect to subject number and detectable effect size, the study of informative pathogenic subphenotypes becomes a highly attractive and efficient strategy for genetic discovery in complex human disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".