Genetic and epigenetic screens in primary human T cells link candidate causal autoimmune variants to T cell networks
Bibliographic record
Abstract
Genetic variants associated with autoimmune diseases are highly enriched within putative cis-regulatory regions of CD4+ T cells, suggesting that they could alter disease risk through changes in gene regulation. However, very few genetic variants have been shown to affect T cell gene expression or function. Here we tested >18,000 autoimmune disease-associated variants for allele-specific effects on expression using massively parallel reporter assays in primary human CD4+ T cells. We find 545 variants that modulate expression in an allele-specific manner (emVars). Primary T cell emVars greatly enrich for likely causal variants, are mediated by common upstream pathways and their putative target genes are highly enriched within a lymphocyte activation network. Using bulk and single-cell CRISPR-interference screens, we confirm that emVar-containing T cell cis-regulatory elements modulate both known and previously unappreciated target genes that regulate T cell proliferation, providing plausible mechanisms by which these variants alter autoimmune disease risk. Massively parallel reporter assay in primary human CD4+ T cells and bulk and single-cell CRISPR-interference screens identify candidate causal variants linked to autoimmune disease risk that modulate T cell gene expression and proliferation.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".