Pharmacogenomics of coronary artery response to intravenous gamma globulin in kawasaki disease
Bibliographic record
Abstract
Kawasaki disease (KD) is a multisystem inflammatory illness of infants and young children that can result in acute vasculitis. The mechanism of coronary artery aneurysms (CAA) in KD despite intravenous gamma globulin (IVIG) treatment is not known. We performed a Whole Genome Sequencing (WGS) association analysis in a racially diverse cohort of KD patients treated with IVIG, both using AHA guidelines. We defined coronary aneurysm (CAA) (N = 234) as coronary z ≥ 2.5 and large coronary aneurysm (CAA/L) (N = 92) as z ≥ 5.0. We conducted logistic regression models to examine the association of genetic variants with CAA/L during acute KD and with persistence >6 weeks using an additive model between cases and 238 controls with no CAA. We adjusted for age, gender and three principal components of genetic ancestry. The top significant variants associated with CAA/L were in the intergenic regions (rs62154092 p < 6.32E-08 most significant). Variants in SMAT4, LOC100127, PTPRD, TCAF2 and KLRC2 were the most significant non-intergenic SNPs. Functional mapping and annotation (FUMA) analysis identified 12 genomic risk loci with eQTL or chromatin interactions mapped to 48 genes. Of these NDUFA5 has been implicated in KD CAA and MICU and ZMAT4 has potential functional implications. Genetic risk score using these 12 genomic risk loci yielded an area under the receiver operating characteristic curve (AUC) of 0.86. This pharmacogenomics study provides insights into the pathogenesis of CAA/L in IVIG-treated KD and shows that genomics can help define the cause of CAA/L to guide management and improve risk stratification of KD patients.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".