Rare Genetic Variants of Large Effect Influence Risk of Type 1 Diabetes
Bibliographic record
Abstract
Most replicated genetic determinants for type 1 diabetes are common (minor allele frequency [MAF] >5%). We aimed to identify novel rare or low-frequency (MAF <5%) single nucleotide polymorphisms with large effects on risk of type 1 diabetes. We undertook deep imputation of genotyped data followed by genome-wide association testing and meta-analysis of 9,358 type 1 diabetes case and 15,705 control subjects from 12 European cohorts. Candidate variants were replicated in a separate cohort of 4,329 case and 9,543 control subjects. Our meta-analysis identified 27 independent variants outside the MHC, among which 3 were novel and had MAF <5%. Three of these variants replicated with Preplication < 0.05 and Pcombined < Pdiscovery. In silico analysis prioritized a rare variant at 2q24.3 (rs60587303 [C], MAF 0.5%) within the first intron of STK39, with an effect size comparable with those of common variants in the INS and PTPN22 loci (combined [from the discovery and replication cohorts] estimate of odds ratio [ORcombined] 1.97, 95% CI 1.58–2.47, Pcombined = 2.9 × 10−9). Pharmacological inhibition of Stk39 activity in primary murine T cells augmented effector responses through enhancement of interleukin 2 signaling. These findings provide insight into the genetic architecture of type 1 diabetes and have identified rare variants having a large effect on disease risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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) | 0.000 | 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".