Genome-Wide Association for HbA1c in Malay Identified Deletion on <i>SLC4A1</i> that Influences HbA1c Independent of Glycemia
Bibliographic record
Abstract
CONTEXT: Glycated hemoglobin A1c (HbA1c) level is used to screen and diagnose diabetes. Genetic determinants of HbA1c can vary across populations and many of the genetic variants influencing HbA1c level were specific to populations. OBJECTIVE: To discover genetic variants associated with HbA1c level in nondiabetic Malay individuals. DESIGN AND PARTICIPANTS: We conducted a genome-wide association study (GWAS) analysis for HbA1c using 2 Malay studies, the Singapore Malay Eye Study (SiMES, N = 1721 on GWAS array) and the Living Biobank study (N = 983 on GWAS array and whole-exome sequenced). We built a Malay-specific reference panel to impute ethnic-specific variants and validate the associations with HbA1c at ethnic-specific variants. RESULTS: Meta-analysis of the 1000 Genomes imputed array data identified 4 loci at genome-wide significance (P < 5 × 10-8). Of the 4 loci, 3 (ADAM15, LINC02226, JUP) were novel for HbA1c associations. At the previously reported HbA1c locus ATXN7L3-G6PC3, association analysis using the exome data fine-mapped the HbA1c associations to a 27-bp deletion (rs769664228) at SLC4A1 that reduced HbA1c by 0.38 ± 0.06% (P = 3.5 × 10-10). Further imputation of this variant in SiMES confirmed the association with HbA1c at SLC4A1. We also showed that these genetic variants influence HbA1c level independent of glucose level. CONCLUSION: We identified a deletion at SLC4A1 associated with HbA1c in Malay. The nonglycemic lowering of HbA1c at rs769664228 might cause individuals carrying this variant to be underdiagnosed for diabetes or prediabetes when HbA1c is used as the only diagnostic test for diabetes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".