Secondary Analysis of Publicly Available Data Reveals Superoxide and Oxygen Radical Pathways are Enriched for Associations Between Type 2 Diabetes and Low‐Frequency Variants
Bibliographic record
Abstract
Genome-wide association studies explain at most 5%-10% of the heritable components of type 2 diabetes. Some of the "missing type 2 diabetes heritability" could be explained by low-frequency variants. We examined the associations between low-frequency variants and type 2 diabetes, using data from 2538 diabetic and 2977 nondiabetic subjects in the publicly available database of Genotypes and Phenotypes. We applied two approaches. First, we combined information from all low-frequency (1%-5%) variants at a locus in a gene-centric analysis of associations with diabetes. Next, we searched for gene ontology (GO) biological processes that were enriched for gene-centric associations, after correcting for multiple testing to control the false discovery rate (FDR). We found three GO biological processes that were significantly enriched for associations to diabetes: "response to superoxide" (FDR-adjusted p=2.7×10(-3)), "response to oxygen radical" (FDR-adjusted p=2.7×10(-3)), and "heart contraction" (FDR-adjusted p=2.6×10(-2)). There were three genes that contributed to "response to superoxide" and "oxygen radical" pathways, including the SOD1 gene. Gene-centric tests of association with low-frequency variants, followed by analysis to evaluate which biological pathways are enriched for these associations has the potential to recover, at least some proportion of, the "missing heritability" of 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".