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

• Objective: This study introduced a novel single-cell transcriptome-wide Mendelian randomization (scTWMR) framework to leverage cell-type-specific gene expression data from atherosclerotic plaques and identify causal genes for Atherosclerotic Cardiovascular Disease (ASCVD) risk.
• Key Findings: The approach prioritized 23 causal genes by resolving signals to specific cell types, finding the strongest evidence in immune cells; for example, genetically predicted increased expression of HSH2D in macrophages was causally associated with increased ASCVD risk.
• Significance: scTWMR provides a level of resolution superior to traditional bulk-tissue MR, enabling the identification of cell-specific drug targets and offering a scalable strategy for uncovering mechanistic details of complex diseases.

PubMed: 40555237 DOI: 10.1016/j.ajhg.2025.06.001 Overview generated by: Gemini 2.5 Flash, 28/11/2025

Key Findings: Cell-Specific Causal Genes in Atherosclerosis

This study introduced a novel, stringent single-cell transcriptome-wide Mendelian randomization (scTWMR) framework to leverage cell-type-specific gene expression data and identify causal genes for Atherosclerotic Cardiovascular Disease (ASCVD) risk, a level of resolution previously inaccessible with bulk-tissue MR.

• Cell-Specific Mechanisms Uncovered: The scTWMR approach successfully resolved the genetic signals influencing ASCVD risk to specific cell types within the atherosclerotic plaque, leading to the prioritization of 23 causal genes.
• Prioritization of Immune Cell Genes: The strongest and most frequent causal signals were found in immune cells, particularly macrophages and T cells, which are central to the inflammatory process of atherosclerosis:
  • Macrophages: Genetically predicted increased expression of HSH2D in macrophages was causally associated with an increased risk of ASCVD.
  • T Cells: Genetically predicted increased expression of PRRC2A and ARHGAP26 in T cells was causally associated with increased ASCVD risk.
• Vascular Smooth Muscle Cell (VSMC) Genes: Genes with causal effects were also identified in VSMCs, highlighting their role in plaque stability:
  • SCARB1 and CD33 expression in VSMCs were causally associated with ASCVD risk.
• Improved Gene Prioritization: The scTWMR approach significantly improved the precision of causal gene prioritization compared to traditional bulk-tissue MR. Many signals that appeared ambiguous or lacked a confirmed causal gene in bulk tissue analysis were successfully linked to a specific cell type and gene using the single-cell resolution framework.

Study Design and Methods

Study Design

This was a two-sample single-cell transcriptome-wide Mendelian randomization (scTWMR) study. The framework integrated publicly available GWAS summary statistics for ASCVD with cell-type-specific gene expression data derived from single-cell RNA sequencing (scRNA-seq) of human atherosclerotic plaques.

Data Sources and Instrumental Variables

1. Outcome GWAS Data (ASCVD): Summary statistics for Atherosclerotic Cardiovascular Disease (ASCVD), Coronary Artery Disease (CAD), and Stroke were sourced from large-scale consortia (e.g., CARDIOGRAMplusC4D, ISGC).
2. Exposure Data (Cell-Specific Expression): Expression quantitative trait loci (eQTLs) were derived from an analysis of human atherosclerotic plaque scRNA-seq data, providing a high-resolution map of genetic effects on gene expression within specific cell types, including macrophages, T cells, endothelial cells, and smooth muscle cells.
3. Instrument Selection: Genetic variants (SNPs) acting as cell-type-specific eQTLs were used as instrumental variables (IVs). This ensured that the instruments reflected the genetic regulation of gene expression specifically in the cell type of interest.

Statistical Analysis

1. Mendelian Randomization (MR): The Inverse-Variance Weighted (IVW) method was used as the primary MR approach to estimate the causal effect (Odds Ratio, OR) of genetically predicted cell-type-specific gene expression on ASCVD risk.
2. Sensitivity Analyses: Robust sensitivity methods were performed, including:
   • MR-Egger regression.
   • Weighted Median and Weighted Mode estimators.
   • MR-PRESSO (to detect and remove pleiotropic outliers).
3. Colocalization Analysis: Bayesian colocalization (moloc) was used to provide statistical evidence that the same underlying genetic variant was responsible for both the change in cell-specific gene expression and the change in ASCVD risk, distinguishing true causal links from spurious associations due to linkage disequilibrium.
4. Novelty of Framework: The study designed its framework to be stringent and scalable, providing a powerful resource for future studies aimed at dissecting complex disease mechanisms at cellular resolution.

Conclusions and Recommendations

The study concludes that integrating single-cell transcriptomic data with MR provides a necessary leap forward in genetic epidemiology, enabling the dissection of complex disease risk at a cellular level.

• High-Resolution Drug Targets: By resolving causal genes to specific cell types (e.g., HSH2D in macrophages), the scTWMR framework directly identifies highly specific molecular targets for therapeutic development. This could lead to the design of drugs that modulate ASCVD risk by specifically affecting the function of key immune cells in the plaque, potentially minimizing off-target effects.
• Mechanistic Understanding: The prioritization of genes in immune cells reinforces the central role of inflammation in ASCVD aetiology and provides a refined list of genes for functional validation studies.
• Future Work: The authors advocate for the widespread application of this scTWMR framework to other complex diseases, such as neurological disorders and cancer, where cell-type heterogeneity is known to play a crucial role.