Systematic Confirmation Study of GWAS-Identified Genetic Variants for Kawasaki Disease in A Chinese Population
Bibliographic record
Abstract
Genome-wide association studies (GWASs) have identified multiple single nucleotide polymorphisms (SNPs) associated with Kawasaki disease (KD). In this study, we replicated the associations of 10 GWAS-identified SNPs with KD in a Han Chinese population. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by logistic regression, and cumulative effect of non-risk genotypes were also performed. Although none of the SNPs reached the corrected significance level, 4 SNPs showed nominal associations with KD risk. Compared with their respective wild type counterparts, rs1801274 AG+GG genotypes and rs3818298 TC+CC genotypes were nominally associated with the reduced risk of KD (OR = 0.77, 95% CI = 0.59-0.99, P = 0.045; OR = 0.74, 95% CI = 0.56-0.98, P = 0.038). Meanwhile, rs1801274 GG genotype, rs2736340 CC genotype or rs4813003 TT genotype showed a reduced risk trend (OR = 0.57, 95% CI = 0.35-0.93, P = 0.024; OR = 0.46, 95% CI = 0.26-0.83, P = 0.010; OR = 0.64, 95% CI = 0.43-0.94, P = 0.022), compared with rs1801274 AG+AA genotypes, rs2736340 CT+TT genotypes or rs4813003 TC+CC genotypes, respectively. Furthermore, a cumulative effect was observed with the ORs being gradually decreased with the increasing accumulative number of non-risk genotypes (Ptrend<0.001). In conclusion, our study suggests that 4 GWAS-identified SNPs, rs2736340, rs4813003, rs3818298 and rs1801274, were nominally associated with KD risk in a Han Chinese population individually and jointly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".