3D chromatin-based variant-to-gene maps across 57 human cell types reveal the cellular and genetic architecture of autoimmune disease susceptibility
Bibliographic record
Abstract
ABSTRACT Genome-wide association studies (GWAS) have identified genetic links to autoimmune disorders, but lack detail on causal elements. We generated 3D genomic datasets of promoter-focused Capture-C, Hi-C, ATAC-seq, and RNA-seq across 57 human cell types integrated with GWAS of 16 autoimmune traits. These data allowed us to map disease-associated variants to their effector genes and identify impacted cell types more effectively than using 1D genomic features or eQTL approaches. Most variants implicated by 3D cis-regulatory architectures are trait-specific, while half the target genes are shared across multiple disorders and cell types, leading to enrichment of similar biological networks. This indicates complex genetic diversity converges on shared targets, yet unique pathways were identified offering avenues for targeted therapies. We pharmacologically validated squalene synthase, a cholesterol biosynthetic enzyme encoded by the FDFT1 gene implicated by our approach and eQTL in multiple sclerosis and systemic lupus erythematosus, as a novel immunomodulatory drug target controlling T cell inflammatory cytokine production and aiding B cell antibody production in a human lymphoid organoid model. These data offer a comprehensive resource for understanding gene cis-regulatory mechanisms, and the analyses shed light on how autoimmune-associated variants regulate gene expression, function, and pathology across diverse tissues and cell types.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".