Enrichment of Rare Variants of Hemophagocytic Lymphohistiocytosis Genes in Systemic Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
Abstract Objective To evaluate whether there is an enrichment of rare variants in familial hemophagocytic lymphohistiocytosis (HLH) genes and systemic juvenile idiopathic arthritis (sJIA) with or without macrophage activation syndrome (MAS). Methods Targeted sequencing of HLH genes ( LYST, PRF1, RAB27A, STX11, STXBP2, UNC13D ) was performed in sJIA subjects from an established cohort. Sequence data from control subjects were obtained in silico (dbGaP:phs000280.v8.p2). Rare variant association testing (RVT) was performed with sequence kernel association test (SKAT) package. Significance was defined as p<0.05 after 100,000 permutations. Results Sequencing data from 524 sJIA cases were jointly called and harmonized with exome-derived target data from 3000 controls. Quality control operations produced a set of 481 cases and 2924 ancestrally-matched control subjects. RVT of sJIA cases and controls revealed a significant association with rare protein-altering variants (minor allele frequency [MAF]<0.01) of STXBP2 (p=0.020), and ultra-rare variants (MAF<0.001) of STXBP2 (p=0.007) and UNC13D (p=0.045). A subanalysis of 32 cases with known MAS and 90 without revealed significant association of rare UNC13D variants (p=0.0047). Additionally, sJIA patients more often carried ≥2 HLH variants than did controls (p=0.007), driven largely by digenic combinations involving LYST . Conclusion We identified an enrichment of rare HLH variants in sJIA patients compared with healthy controls, driven by STXBP2 and UNC13D . Biallelic variation in HLH genes was associated with sJIA, driven by LYST . Only UNC13D displayed enrichment in patients with MAS. This suggests that HLH variants may contribute to the pathophysiology of sJIA, even without MAS.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".