Genetic analyses of interferon pathway-related genes reveal multiple new loci associated with systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVE: The overexpression of interferon (IFN)-inducible genes is a prominent feature of systemic lupus erythematosus (SLE); it serves as a marker for active and more severe disease, and is also observed in other autoimmune and inflammatory conditions. This study was undertaken to investigate the genetic variations responsible for sustained activation of IFN-responsive genes in SLE. METHODS: We systematically evaluated association of SLE with a total of 1,754 IFN pathway-related genes, including IFN-inducible genes known to be differentially expressed in SLE patients and their direct regulators. We used a 3-stage study design in which 2 cohorts (total of 939 SLE cases and 3,398 controls) were analyzed independently and jointly for association with SLE, and the results were adjusted for the number of comparisons. RESULTS: A total of 15,166 single-nucleotide polymorphisms (SNPs) passed all quality control filters; 305 of these SNPs demonstrated replicated association with SLE in both cohorts. Nine variants were further genotyped for confirmation in an average of 1,316 independent SLE cases and 3,215 independent controls. Association with SLE was confirmed for several genes, including those for the transmembrane receptor CD44 (CD44 [rs507230]; P = 3.98 × 10⁻¹²), the cytokine pleiotrophin (PTN [rs919581]; P = 5.38 × 10⁻⁴), the heat-shock protein DnaJ (DNAJA1 [rs10971259]; P = 6.31 × 10⁻³), and the nuclear import protein karyopherin α1 (KPNA [rs6810306]; P = 4.91 × 10⁻²). CONCLUSION: This study expands the number of candidate genes that have been shown to be associated with SLE and highlights potential of pathway-based approaches for gene discovery. Identification of the causal alleles will help elucidate the molecular mechanisms responsible for activation of the IFN system in SLE.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".