Integrating polygenic signals and single-cell multiomics identifies cell-type-specific regulomes critical for immune- and aging-related diseases.

Abstract

Single-cell multiomics provides critical insights into how disease-associated variants identified through genome-wide association studies (GWASs) influence transcription factor eRegulons within a specific cellular context; however, the regulatory roles of genetic variants in aging and disease remain unclear. Here, we present scMORE, a method that integrates single-cell transcriptomes and chromatin accessibility with GWAS summary statistics to identify cell-type-specific eRegulons associated with diseases. scMORE effectively captures trait-relevant cellular features and demonstrates robust performance across simulated and real single-cell datasets, and GWASs for 31 immune- and aging-related traits, including Parkinson's disease (PD). In the human midbrain, scMORE identifies 77 aging-relevant eRegulons implicated in PD across seven brain cell types and reveals sex-dependent dysregulation of these eRegulons in PD neurons compared to both young and aged groups. By linking genetic variation to cell type-resolved eRegulon activity, scMORE illuminates how variants shape trait-relevant regulatory networks and provides a practical framework for mechanistic interpretation of GWAS signals.