Identification of Novel T1D Risk Loci and Their Association With Age and Islet Function at Diagnosis in Autoantibody-Positive T1D Individuals: Based on a Two-Stage Genome-Wide Association Study
Bibliographic record
Abstract
OBJECTIVE Type 1 diabetes (T1D) is a highly heritable disease with much lower incidence but more adult-onset cases in the Chinese population. Although genome-wide association studies (GWAS) have identified >60 T1D loci in Caucasians, less is known in Asians. RESEARCH DESIGN AND METHODS We performed the first two-stage GWAS of T1D using 2,596 autoantibody-positive T1D case subjects and 5,082 control subjects in a Chinese Han population and evaluated the associations between the identified T1D risk loci and age and fasting C-peptide levels at T1D diagnosis. RESULTS We observed a high genetic correlation between children/adolescents and adult T1D case subjects (rg = 0.87), as well as subgroups of autoantibody status (rg ≥ 0.90). We identified four T1D risk loci reaching genome-wide significance in the Chinese Han population, including two novel loci, rs4320356 near BTN3A1 (odds ratio [OR] 1.26, P = 2.70 × 10−8) and rs3802604 in GATA3 (OR 1.24, P = 2.06 × 10−8), and two previously reported loci, rs1770 in MHC (OR 4.28, P = 2.25 × 10−232) and rs705699 in SUOX (OR 1.46, P = 7.48 × 10−20). Further fine mapping in the MHC region revealed five independent variants, including another novel locus, HLA-C position 275 (omnibus P = 9.78 × 10−12), specific to the Chinese population. Based on the identified eight variants, we achieved an area under the curve value of 0.86 (95% CI 0.85–0.88). By building a genetic risk score (GRS) with these variants, we observed that the higher GRS were associated with an earlier age of T1D diagnosis (P = 9.08 × 10−11) and lower fasting C-peptide levels (P = 7.19 × 10−3) in individuals newly diagnosed with T1D. CONCLUSIONS Our results extend current knowledge on genetic contributions to T1D risk. Further investigations in different populations are needed for genetic heterogeneity and subsequent precision medicine.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".