Integrative genetic analysis of shared genetic architecture of stroke and coronary artery disease: implications for pharmacist-led precision medicine
摘要
Coronary artery disease (CAD) and stroke are leading causes of global morbidity and mortality. Their frequent comorbidities and overlapping risk profiles highlight the importance of understanding shared genetic mechanisms, particularly in identifying therapeutic targets relevant to personalized pharmacotherapy.
AimThis study aimed to explore the shared genetic architecture between stroke and CAD, identify common therapeutic targets, and provide implications for clinical pharmacy practice.
MethodWe integrated multi-ancestry genome-wide association study (GWAS) summary statistics (stroke: 110,182 cases; CAD: 210,842 cases) and employed linkage disequilibrium score regression to assess genetic correlations. Bidirectional two-sample Mendelian randomization (MR) was employed to infer causal inference. Shared genetic variants were identified through cross-trait meta-analyses (MTAG and CPASSOC) and validated using Bayesian colocalization. Pharmacogenomic pathways associated with shared genes were linked to approved drugs using a pathway-pairing score to assess the therapeutic alignment. A score of ≥ 0.5 indicated a strong alignment between a drug’s pharmacological mechanism and the disease’s genetic pathophysiology.
ResultsA significant genetic correlation was observed between stroke and CAD (rg = 0.48, P = 3.38 × 10−34). Eight pleiotropic SNPs and five colocalized causal variants were identified, implicating ten disease-shared genes. Drug-target analyses prioritized the 19 approved cardiovascular agents. Beta-blockers (e.g., bisoprolol, esmolol) and antihypertensives (e.g., fenoldopam bromide/mesylate) demonstrated strong therapeutic potential (pathway score ≥ 0.5).
ConclusionThis study provides genomic evidence to support integrated therapeutic strategies for stroke and CAD. Pharmacogenomic insights into shared genetic determinants can assist clinical pharmacists in optimizing treatment selection, mitigating polypharmacy risks, and guiding precision medicine in patients with dual cardiocerebrovascular risks.