Related Experiment Video
Updated: Apr 21, 2026
Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
Published on: March 12, 2021
Integration of Genome-Wide Association Studies With Single-Cell and Bulk Expression Quantitative Trait Locus to
Yijie He1, Tao Zhang2, Ping Zhu1
1Laboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Ministry of Science and Technology, National Engineering Center of Internet Medical Diagnosis and Treatment Technology, Xuanwu Hospital, Laboratory of Cerebral Small Vessel Disease and Osteoporosis (Abnormal Bone Metabolism) Capital Medical University Beijing China.
Background:
Previous 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.
Methods:
We 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.
Results:
Single-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 increased LIPA expression in mouse stroke samples compared with controls. Protein-protein interaction and druggability analyses, along with phenome-wide association studies, prioritized LIPA and LRCH1 as potential therapeutic targets for stroke while indicating possible adverse effects.
Conclusions:
Integrating 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.
