Integration of Genome‐Wide Association Studies With Single‐Cell and Bulk Expression Quantitative Trait Locus to Identify Stroke Susceptibility Genes

Journal of the American Heart Association, Volume 15, Issue 9 , May 5, 2026. BackgroundPrevious studies have integrated genome‐wide association studies with expression quantitative trait locus (eQTL) data from bulk tissues to identify stroke susceptibility genes.

However, eQTL data exhibit high cell‐type specificity, and genetic variants may have distinct effects across stroke subtypes.MethodsWe applied the summary‐data‐based Mendelian randomization (MR) method to integrate eQTL data from 7 brain cell types with genome‐wide association studies data for 5 stroke phenotypes (stroke, ischemic stroke, cardioembolic stroke, large artery stroke, and small vessel stroke). Results were compared with summary‐data‐based MR using eQTL data from 49 tissues in the Genotype‐Tissue Expression project.

Robustness of significant single‐cell summary‐data‐based MR associations was assessed via MR and colocalization analyses. Further evaluations included single‐cell RNA‐seq differential expression, protein–protein interaction, druggability, and phenome‐wide association studies.ResultsSingle‐cell summary‐data‐based MR identified many novel significant genes not detected using bulk tissue eQTL data.

Validated associations revealed 2 stroke risk genes (LRCH1,ICA1L), 3 stroke protective genes (AHI1,LYRM9,CENPQ), 2 large artery stroke risk genes (LIPA,ELL), and 1 ischemic stroke protective gene (CENPQ). Single‐cell RNA‐seq showed significantly increasedLIPAexpression in mouse stroke samples compared with controls.

Protein–protein interaction and druggability analyses, along with phenome‐wide association studies, prioritizedLIPAandLRCH1as potential therapeutic targets for stroke while indicating possible adverse effects.ConclusionsIntegrating single‐cell eQTL with stroke‐subtype genome‐wide association studies uncovers novel cell‐type‐specific causal genes and highlights promising therapeutic targets, advancing understanding of stroke pathogenesis.