Genetic variants shape single-cell chromatin accessibility and disease risk

Key numbers

• 60% of them cell-type specific
• 22.6% more signals than eQTLs
• 16% to approximately 65%

Understanding how genetic variation shapes gene regulation at single-cell resolution is crucial for elucidating complex disease mechanisms1, but has been limited by the lack of large-scale single-cell multi-omics data2-4. Here we present single-cell assay for transposase-accessible chromatin with sequencing of 3.5 million peripheral blood mononuclear cells from 1,042 donors with matched whole-genome sequencing. Across 28 immune cell types, we tested 440,996 chromatin accessibility peaks and identified 243,225 chromatin accessibility quantitative trait loci (caQTLs) at 100,483 peaks, 60% of them cell-type specific. Rare variant analysis identified 38,161 associated peaks, with predicted transcription factor-binding disruption implicated at individual loci. Colocalization with expression QTLs (eQTLs) from single-cell RNA sequencing data (5.4 million peripheral blood mononuclear cells)5 defined 28,834 candidate cis-regulatory elements. Across genome-wide association studies for 16 diseases and 44 blood traits, caQTLs colocalized 4.5-22.6% more signals than eQTLs alone. Cell-type-specific and tissue-specific context, distal effects and multi-causal-variant models raised genome-wide association study-eQTL concordance from approximately 16% to approximately 65%. Cell state further modulates genetic effects, with 27% of over 200,000 caQTL-cell-state interactions lacking a main effect. At the inflammatory bowel disease risk gene ADCY7, accessibility effects varied with genotype and G protein-mediated pathway activity in CD4+ T cells. A graph neural network trained on both eQTL and caQTL layers inferred peak-gene links in unpaired multiome data with up to 17.2% higher accuracy than QTL-agnostic data, uncovering 128 additional transcription factor-target pairs (22% increase). Together, these results show that genetic variants shape chromatin accessibility in a cell-type-specific and cell-state-specific manner, influencing immune function and disease risk.

Angli Xue, Jianan Fan, Oscar A Dong, Hao Lawrence Huang, Ling Chen, Peter C Allen, Eleanor Spenceley, Eszter Sagi-Zsigmond, Blake Bowen, Albert Henry, Anna S E Cuomo, Hope A Tanudisastro, Zhen Qiao, Elizabeth Dorans, Eyal Ben-David, Kyle Kai-How Farh, Linfeng Hu, Yijia Christiana Liu, Drew R Neavin, Arthur S Lee, Anne Senabouth, Caitlin Bartie, Rachael A McCloy, Venessa Chin, Wei Zhou, Alkes L Price, Katrina M de Lange, Gemma A Figtree, Alex W Hewitt, Daniel G MacArthur, et al.

Leverage3 / 5

A method or resource many groups across the field will adopt within a year.

24% of this desk’s rubric

Magnitude3 / 5

Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.

16% of this desk’s rubric

Evidence3 / 5

Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.

20% of this desk’s rubric

Novelty3 / 5

A genuinely new approach to an open problem.

20% of this desk’s rubric

Trajectory3 / 5

A clear path to scale.

10% of this desk’s rubric

Stakes3 / 5

Meaningful benefit to many people within a few years.

10% of this desk’s rubric

• Citations3 (reference 20, via openalex, Oct 30, 2026, 05:48 UTC)

• Reviewed by heuristic-v10 on Oct 30, 2026, 05:48 UTC. Paper type: method.
• Categories: Journal Article
• TOP, No.3 in the Biology edition of October 30, 2026.