cellSTAAR: incorporating single-cell-sequencing-based functional data to boost power in rare variant association testing of noncoding regions

Abstract

Understanding how rare genetic variants influence complex traits remains a major challenge, particularly when these variants lie in noncoding regions of the genome. The effects of variants within candidate cis-regulatory elements (cCREs) often depend on the cell type, making interpretation difficult. Here we introduce cellSTAAR, which integrates whole-genome sequencing data with single-cell assay for transposase-accessible chromatin using sequencing data to capture variability in chromatin accessibility across cell types via the construction of cell-type-specific functional annotations and regulatory elements. To reflect the uncertainty in cCRE–gene linking, cellSTAAR uses a comprehensive strategy to link cCREs to their target genes. We applied cellSTAAR to data from the Trans-Omics for Precision Medicine consortium (n ≈ 60,000) and replicated our findings using the UK Biobank (n ≈ 190,000). Across four lipid traits, cellSTAAR improved the detection of biologically meaningful associations and enhanced biological interpretability. These results demonstrate the potential of cell-type-aware approaches to boost discovery in rare variant whole-genome sequencing association studies. cellSTAAR advances noncoding rare variant association analysis by integrating single-cell genomics data.