Single-cell transcriptome-wide Mendelian randomization and colocalization analyses uncover cell-specific mechanisms in atherosclerotic cardiovascular disease

Ray A.; Alabarse P.; Malik R.; Sargurupremraj M.; Bernhagen J.; Dichgans M.; Baumeister S.E.; Georgakis M.K.

Zusammenfassung

Genome-wide association studies (GWASs) have identified numerous genetic loci influencing human disease risk; however, linking these to causal genes remains challenging, limiting opportunities for drug target discovery. Transcriptome-wide association studies (TWASs) address this by linking variants to gene expression but typically rely on bulk RNA sequencing, limiting cell-specific resolution. Here, we present a single-cell TWAS pipeline combining cis-Mendelian randomization (MR) with colocalization analyses at the single-cell level. As a case study, we examined how genetically proxied gene expression in immune cells influences atherosclerotic cardiovascular disease (ASCVD) risk. We integrated single-cell expression quantitative trait loci (sc-eQTLs) for 14 immune cell types with GWASs for coronary artery disease, large artery atherosclerotic stroke, and peripheral artery disease. sc-cis-MR revealed 440 gene-outcome associations across cell types, 88% of which were missed by bulk TWASs, despite the considerably smaller sample size of the sc-eQTL dataset. Of these associations, 21 were replicated with external cis-eQTLs and colocalized with ASCVD GWAS signals. Expanding on previous evidence linking genetically proxied LIPA expression in whole blood to coronary artery disease, we found genetic variants influencing LIPA expression, particularly in monocytes, to drive associations with coronary artery disease, large artery atherosclerotic stroke, and subclinical atherosclerosis traits. A phenome-wide association study confirmed these findings without evidence of associations with unexpected clinical outcomes. scRNA sequencing and immunohistochemistry of human carotid plaques revealed high LIPA expression in plaque macrophages. Our pipeline enables the discovery of cell-specific expression patterns that drive genetic predisposition to human disease, potentially impacting target selection for cell-tailored therapeutics.