EIF4A2 Is a Positional Candidate Gene at the 3q27 Locus Linked to Type 2 Diabetes in French Families
Bibliographic record
Abstract
One of the most replicated loci influencing type 2 diabetes-related quantitative traits (quantitative trait loci [QTL]) is on chromosome 3q27 and modulates both type 2 diabetes-and metabolic syndrome-associated phenotypes. A QTL for type 2 diabetes age of onset (logarithm of odds [LOD] score = 3.01 at D3S3686, P = 0.0001) was identified in a set of French families. To assess genetic variation underlying both age-of-onset QTL and our previous type 2 diabetes linkage in a 3.87-Mb interval, we explored 36 single nucleotide polymorphisms (SNPs) in two biologically relevant candidate genes for glucose homeostasis, kininogen (KNG1), and eukaryotic translation initiation factor 4alpha2 (EIF4A2). Analysis of 148 families showed significant association of a frequent SNP, rs266714, located 2.47 kb upstream of EIF4A2, with familial type 2 diabetes (family-based association test, P = 0.0008) and early age of onset (P = 0.0008). This SNP also contributes to both age-of-onset QTL (1.13 LOD score decrease P = 0.02) and type 2 diabetes linkage (genotype identical-by-descent sharing test, P = 0.02). However, no association was observed in three independent European diabetic cohorts. EIF4A2 controls specific mRNA translation and protein synthesis rate in pancreatic beta-cells, and our data indicates that EIF4A2 is downregulated by high glucose in rat beta-INS832/13 cells. The potential role of EIF4A2 in glucose homeostasis and its putative contribution to type 2 diabetes in the presence of metabolic stress will require further investigation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".