Assessment of Genetic Associations between Common Single Nucleotide Polymorphisms in RIG-I-Like Receptor and IL-4 Signaling Genes and Severe Respiratory Syncytial Virus Infection in Children: A Candidate Gene Case-Control Study
Bibliographic record
Abstract
The majority of cases of severe pediatric respiratory syncytial virus (RSV) infection occur in otherwise healthy infants who have no identifiable risk factors, suggesting that additional subclinical factors, such as population genetic variation, influence the course of RSV infection. The objective of this study was to test if common single nucleotide polymorphisms (SNPs) in genes encoding for immune signalling components of the RIG-I-like receptor (RLR) and IL-4-signalling pathways affect the outcome of RSV infection in early life. We genotyped 8 SNPs using allele-specific probes combined with real-time PCR. Each of the SNPs tested had previously been established to have a functional impact on immune responsiveness and two of the SNPs in the IL4 and IL4R genes had previously been associated with severe RSV bronchiolitis. Association with susceptibility to severe RSV infection was tested by statistically comparing genotype and allele frequencies in infants and young children hospitalized with severe RSV bronchiolitis (n = 140) with two control groups-children who tested positive for RSV but did not require hospitalization (n = 100), and a general population control group (n = 285). Our study was designed with sufficient power (>80%) to detect clinically-relevant associations with effect sizes ≥1.5. However, we detected no statistically significant differences in allele and genotype frequencies of the investigated SNPs between the inpatient and control groups. To conclude, we could not replicate the previously reported association with SNPs in the IL4 and IL4R genes in our independent cohort, nor did we find that common SNPs in genes encoding for RLRs and the downstream adapter MAVS were associated with susceptibility to severe RSV infections. Despite the existing evidence demonstrating a functional immunological impact of these SNPs, our data suggest that the biological effect of each individual SNP is unlikely to affect clinical outcomes of RSV 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".