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
BACKGROUND: Insight into the genetic basis for many common autoimmune disorders has been uncovered by genome-wide association studies (GWAS), but this alone does not reveal causal variants, effector genes, or the cell types impacted by disease-associated variation. RESULTS: Here, we generate 3D genomic datasets consisting of promoter-focused Capture-C, Hi-C, ATAC-seq, and RNA-seq and integrate this data with GWAS of 16 autoimmune traits to physically map disease-associated variants to the effector genes they likely regulate in 57 human cell types. The majority of variants implicated by these cis-regulatory architectures are trait-specific, but nearly half of the target genes connected to these variants are shared across multiple autoimmune disorders in multiple cell types, leading to enrichment of similar biological networks. While this suggests a high level of genetic diversity and complexity that converges at the level of target gene and cell type, some trait-specific pathways representing potential areas for disease-specific intervention were identified. We pharmacologically validate squalene synthase, a cholesterol biosynthetic enzyme encoded by the FDFT1 gene implicated by our approach and supported by prior eQTL data 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. CONCLUSIONS: These data represent a comprehensive resource for basic discovery of gene cis-regulatory mechanisms, and the analyses reported reveal mechanisms by which autoimmune-associated variants act to regulate gene expression, function, and pathology across multiple, distinct 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".