Making Sense of Genome-Wide Association Studies
Bibliographic record
Abstract
G enome-wide association studies (GWAS) have madeenormous strides in identifying common genetic variants that contribute to common diseases 1,2 and diseaseassociated traits.3 Most of these genetic variants occur in noncoding sequences within introns of genes or in regions of the genome between genes, so called gene deserts, suggesting that they affect gene expression and regulation rather than the sequence of the expressed proteins.Genetic variants do not just affect gene expression in their vicinity but can affect gene expression on different chromosomes.This reflects the finite geometry of the genome within the nucleus, where transcription factor complexes serve multiple active templates.Thus, a variant that disrupts gene expression by affecting the recruitment of transcription factors on 1 chromosome can also have a strong effect at genes on other chromosomes.
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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 | 0.055 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.067 | 0.085 |
| Insufficient payload (model declined to judge) | 0.004 | 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".