Functional characterization of the Thr946Ala SNP at the type 1 diabetes<i>IFIH1</i>locus
Bibliographic record
Abstract
The Thr allele at the Thr946Ala non-synonymous single-nucleotide polymorphism (nsSNP) in the IFIH1 gene confers risk for type 1 diabetes (T1D). IFIH1 binds viral double-stranded RNA (dsRNA), inducing a type I interferon (IFN) response. Reports of this nsSNP's role in IFIH1 expression regulation have produced conflicting results and a study evaluating transfected Thr946Ala protein alleles in an artificial system overexpressing IFIH1 shows that the SNP does not affect IFH1 function. In this study, we examine the effects of the Thr946Ala polymorphism on IFN-α response in a cell line that endogenously expresses physiological levels of IFIH1. Eleven lymphoblastoid cell lines (LCLs) homozygous for the major predisposing allele (Thr/Thr) and 6 LCLs homozygous for the minor protective allele (Ala/Ala) were electroporated with the viral dsRNA mimic, poly I:C, in three independent experiments. Media were collected 24 hours later and measured for IFN-α production by ELISA. Basal IFN response is minimal in mock-transfected cells from both genotypes and increases by about 8-fold in cells treated with poly I:C. LCLs with the Ala/Ala genotype have slightly higher IFN-α levels than their Thr/Thr counterparts but this did not reach statistical significance because of the large variability of the IFN response, due mostly to two high outliers (biological, not technical). A larger sample size would be needed to determine whether the Thr946Ala SNP affects the poly I:C-driven IFN-α response. Additionally, the possibility that this nsSNP recognizes viral dsRNA specificities cannot be ruled out. Thus, the mechanism of the observed association of this SNP with T1D remains to be determined.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 0.001 |
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".