Genetic regulation of cell type-specific chromatin accessibility shapes immune function and disease risk

Abstract

Understanding how genetic variation influences gene regulation at the single-cell level is crucial for elucidating the mechanisms underlying complex diseases. However, limited large-scale single-cell multi-omics data have constrained our understanding of the regulatory pathways linking variants to cell type-specific gene expression. Here we present chromatin accessibility profiles from 3.5 million peripheral blood mononuclear cells (PBMCs) across 1,042 donors, generated using single-cell ATAC-seq, including [~]100,000 PBMCs from 90 donors using multiome (RNA+ATAC) sequencing, with matched whole-genome sequencing. We identified 440,996 chromatin peaks across 28 immune cell types and mapped 243,225 chromatin accessibility quantitative trait loci (caQTLs), of which 60% were cell type-specific. Rare variant analysis revealed associations with 27,927 peaks, recapitulating the same cell type-specific regulatory architecture. Colocalization with the eQTLs from scRNA-seq data (5.4 million PBMCs) identified 31,688 candidate cis-regulatory elements; around half (44.40%) show evidence of putative mediating effects via chromatin accessibility. Integrating caQTLs with GWAS summary statistics for 16 diseases and 44 blood traits uncovered 4.5% - 22.6% more colocalized signals compared with using eQTLs alone, many of which have not been reported in prior studies. We show that the limited concordance between GWAS and eQTL signals can be substantially improved by incorporating cell type- and tissue-specific context, distal regulatory effects, models allowing for multiple causal variants, and promoter/enhancer priming, together increasing overlap from [~]20% to [~]60%. In addition, using a graph neural network, we inferred peak-to-gene relationships from unpaired multiome data by integrating caQTL and eQTL signals, achieving up to 80% higher accuracy than with paired multiome data lacking QTL information. This gain translated into improved gene regulatory network inference, enabling the identification of 128 additional transcription factors (TF)-target gene pairs (a 22% increase). Together, these results provide a comprehensive single-cell map of chromatin accessibility and genetic variation in human circulating immune cells, establishing a powerful resource for dissecting cell type-specific regulation and advancing our understanding of genetic risk for complex diseases.