On the synthesis and interpretation of consistent but weak gene-disease associations in the era of genome-wide association studies
Bibliographic record
Abstract
Emerging technologies are allowing researchers to study hundreds of thousands of genetic variants simultaneously as risk factors for common complex diseases. Both theoretical considerations and empirical evidence suggest that specific genetic variants causally associated with common diseases will have small effects (risk ratios mostly <2.0). However, the combination of even a few small effects (e.g. effects of fewer than 20 common genetic variants) could account for a sizeable population attributable fraction of common diseases and shed important light on disease pathogenesis and environmental determinants. Nevertheless, the inauguration of genome-wide association studies only magnifies the challenge of differentiating between the expected, true weak associations from the numerous spurious effects caused by misclassification, confounding and significance-chasing biases. Standards are urgently needed for presenting and interpreting cumulative evidence on gene-disease associations, especially for consistent but weak associations. Criteria for synthesis of the evidence should include sound methods for study conduct and analysis, biological plausibility, experimental evidence and adequate replication in large-scale, collaborative studies. Efforts by the Human Genome Epidemiology Network (HuGENet) are currently ongoing to streamline and operationalize these criteria for data on genetic associations with common diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".