Combinatorial effects of multiple enhancer variants in linkage disequilibrium dictate levels of gene expression to confer susceptibility to common traits
Why this work is in the frame
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Bibliographic record
Abstract
DNA variants (SNPs) that predispose to common traits often localize within noncoding regulatory elements such as enhancers. Moreover, loci identified by genome-wide association studies (GWAS) often contain multiple SNPs in linkage disequilibrium (LD), any of which may be causal. Thus, determining the effect of these multiple variant SNPs on target transcript levels has been a major challenge. Here, we provide evidence that for six common autoimmune disorders (rheumatoid arthritis, Crohn's disease, celiac disease, multiple sclerosis, lupus, and ulcerative colitis), the GWAS association arises from multiple polymorphisms in LD that map to clusters of enhancer elements active in the same cell type. This finding suggests a "multiple enhancer variant" hypothesis for common traits, where several variants in LD impact multiple enhancers and cooperatively affect gene expression. Using a novel method to delineate enhancer-gene interactions, we show that multiple enhancer variants within a given locus typically target the same gene. Using available data from HapMap and B lymphoblasts as a model system, we provide evidence at numerous loci that multiple enhancer variants cooperatively contribute to altered expression of their gene targets. The effects on target transcript levels tend to be modest and can be either gain- or loss-of-function. Additionally, the genes associated with multiple enhancer variants encode proteins that are often functionally related and enriched in common pathways. Overall, the multiple enhancer variant hypothesis offers a new paradigm by which noncoding variants can confer susceptibility to common traits.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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