Nature· 2026Q1
Genetic variants shape single-cell chromatin accessibility and disease risk
- 3citations
- Q1SCImago
- 2026year
Short summary
A graph neural network accurately predicted gene-peak links from unpaired multiome data, identifying 128 new transcription factor-target pairs and showing genetic variants influence cell-type-specific chromatin accessibility.
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Key points
- A graph neural network improved gene-peak link prediction accuracy by up to 17.2% using multiome data and QTL information.
- The model identified 128 novel transcription factor-target pairs, a 22% increase over existing knowledge.
- Genetic variants were shown to influence chromatin accessibility in a cell-type and cell-state-specific manner.
- These genetic influences on chromatin accessibility impact immune function and disease risk.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. Nature, 2026 · DOI ↗
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Field: Molecular Biology
Molecular BiologyBiochemistry, Genetics and Molecular Biology