Rare Structural Variants Uncovered by Optical Genome Mapping in Multisystem Inflammatory Syndrome in Children (MIS‐C)
Bibliographic record
Abstract
ABSTRACT Multisystem inflammatory syndrome in children (MIS‐C) is a pediatric complication of SARS‐CoV‐2 infection characterized by multiorgan inflammation and frequently by cardiovascular dysfunction. In a single‐center prospective cohort study, optical genome mapping (OGM) was performed on 14 patients, including 11 meeting CDC criteria for MIS‐C and 3 with MIS‐C–like (MIS‐CL) presentations. SVs and CNVs were filtered against population and internal OGM control databases. Seven patients (50%) harbored prioritized variants within or near genes implicated in immune regulation or SARS‐CoV‐2 response. These included intronic insertions or deletions in ORAI1 , STAT4 , and ITPR1 (n = 4 patients); a heterozygous insertion disrupting BATF ; a large deletion spanning exons 2–10 of CFHR5 ; and an upstream insertion near DOCK2 . Application of OGM to patients with MIS‐C and MIS‐CL revealed SVs potentially impacting inflammation, COVID‐19 severity, and Kawasaki Disease susceptibility. Although causality cannot yet be assigned, the identification of rare structural variants highlights biologically plausible mechanisms that may contribute to disease heterogeneity. These findings establish the feasibility and value of OGM in the assessment of complex pediatric syndromes, such as children with MIS‐C or a severe course of SARS‐CoV‐2 infection.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".