GEM-Finder: dissecting GWAS variants via long-range interacting cis-regulatory elements with differentiation-specific genes
Bibliographic record
Abstract
Abstract Interpreting the functional significance of non-coding GWAS variants remains challenging. While co-localizing variants with cell-type specific cis-regulatory elements (CREs) has improved our understanding, many variants remain unassociated. In this study, we propose GEM-Finder (Genomic Element Mapping for Fine Discovery of Promoter-Linked Variants), a novel analytical framework that integrates transcriptomic, epigenomic (H3K27ac ChIP-seq), and chromatin interaction data. GEM-Finder utilizes long-range chromatin interactions to identify CREs that connect differentially expressed genes of specific cell types. When we apply GEM-Finder to endothelial differentiation, unlike conventional methods primarily focused on cell-type specific CREs, GEM-Finder identifies 7.6 times more disease/trait associations. Specifically, by integrating transcriptome, epigenome (particularly H3K27ac ChIP-seq), and long-range chromatin interactions during endothelial differentiation, we identified CREs linked to differentiation-specific genes. Our enrichment analyses revealed both shared and unique associations for 53 human diseases/traits. Notably, the majority of these (68%) exhibited unique associations in a differentiation-specific manner. Hematological traits and neuropsychiatric disorders were primarily linked to the final stage of endothelial differentiation, while several complex diseases, such as colorectal cancer (CRC), were unexpectedly associated with the late stage. Our findings underscore the importance of leveraging long-range chromatin interactions to accurately identify disease-associated CREs in the functional characterization of non-coding GWAS variants.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".