Association Testing in 9,000 People Fails to Confirm the Association of the Insulin Receptor Substrate-1 G972R Polymorphism With Type 2 Diabetes
Bibliographic record
Abstract
The insulin receptor substrate (IRS)-1 is an important component of the insulin signal transduction cascade. Several reports suggest that a Gly-->Arg change in codon 972 is associated with type 2 diabetes and related traits, and a recent meta-analysis reported a modest but nominally significant association with type 2 diabetes (odds ratio [OR] 1.25 in favor of carriers of the Arg allele [95% CI 1.05-1.48). To test the reproducibility of the model in a recent meta-analysis, we examined genotype-phenotype correlation in three large Caucasian samples (not previously reported for this variant) totaling 9,000 individuals (estimated to have >95% power to obtain a P < 0.05 for the OR of 1.25 estimated in the meta-analysis). In our combined sample, comprising 4,279 case and 3,532 control subjects, as well as 1,189 siblings discordant for type 2 diabetes, G972R was not associated with type 2 diabetes (OR 0.96 [0.84-1.10], P = 0.60). Genotype at G972R had no significant effect on various measures of insulin secretion or insulin resistance in a set of Scandinavian samples in whom we had detailed phenotypic data. In contrast, the well-documented associations of peroxisome proliferator-activated receptor gamma P12A and Kir6.2 E23K with type 2 diabetes are both robustly observed in these 9,000 subjects, including an additional (previously unpublished) confirmation of Kir6.2 E23K and type 2 diabetes in the Polish and North American samples (combined OR 1.15 [1.05-1.26], P = 0.001). Despite genotyping 9,000 people and >95% power to reproduce the estimated OR from the recent meta-analysis, we were unable to replicate the association of the IRS-1 G972R polymorphism with type 2 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 | 0.038 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".