Principles for the post-GWAS functional characterisation of risk loci
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Several challenges lie ahead in assigning functionality to susceptibility SNPs. For example, most effect sizes are small relative to effects seen in monogenic diseases, with per allele odds ratios usually ranging from 1.15 to 1.3. It is unclear whether current molecular biology methods have enough resolution to differentiate such small effects. Our objective here is therefore to provide a set of recommendations to optimize the allocation of effort and resources in order to maximize the chances of elucidating the functional contribution of specific loci to the disease phenotype. It has been estimated that 88% of currently identified disease-associated SNPs are intronic or intergenic. Thus, in this paper we will focus our attention on the analysis of non-coding variants and outline a hierarchical approach for post-GWAS functional studies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it