Shared genetic etiology of vessel diseases: A genome-wide multi-traits association analysis
Jiangwei Song a), Ning Gao a) b), Zhe Chen c), Guocong Xu a), Minjian Kong a), Dongdong Wei a), Qi Sun d) e), Aiqiang Dong a)
a-Department of Cardiovascular Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
b-Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
c-Department of Thoracic Surgery, The Second Xiangya Hospital, Central South University, Changsha 410011, Hunan, China
d-Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Qingchun Road 79, Hangzhou 310003, China
e-Key Laboratory of Combined Multi-organ Transplantation, Ministry of Public Health, First Affiliated Hospital, School of Medicine, Zhejiang University, Qingchun Road 79, Hangzhou 310003, China
Abstract
Background
The comorbidity among vascular diseases has been widely reported, however, the contribution of shared genetic components remains ambiguous.
Methods
Based on genome-wide association study summary statistics, we employed statistical genetics methodologies to explore the shared genetic basis of eight vascular diseases: coronary artery disease, abdominal aortic aneurysm, ischemic stroke, peripheral artery disease, thoracic aortic aneurysm, phlebitis, varicose veins, and venous thromboembolism. We assessed global and local genetic correlations among these disorders by linkage disequilibrium score regression, high-definition likelihood, and local analysis of variant association. Cross-trait analyses conducted with CPASSOC identified pleiotropic variants and loci. Further, biological pathways at the multi-omics level were explored using multimarker analysis of genomic annotation, transcriptome-wide and proteome-wide association studies. Causal associations among the vascular diseases were evaluated by mendelian randomization and latent causal variable to assess vertical pleiotropic effects.
Results
We found significant global genetic associations in 18 pairs of vascular diseases. Additionally, we discovered 317 unique genomic regions where at least one pair of traits demonstrated significant correlation. Multi-trait association analysis identified 19,361 significant potential pleiotropic variants in 274 independent pleiotropic loci. Multi-trait colocalization analysis revealed 56 colocalized loci in specific disease sets. Gene-based analysis identified 700 potential pleiotropic genes, which were subsequently validated at both transcriptome and protein levels. Gene-set enrichment analysis supports the role of biological pathways such as vessel wall structure, coagulation and lipid transport in vascular disease. Additionally, 7 pairs of vascular diseases have a causal relationship.
Conclusions
Our study indicates a shared genetic basis and the presence of common risk genes among vascular diseases. These findings offer novel insights into potential mechanisms underlying the association between vascular diseases, as well as provide guidance for interventions and treatments of multi-vascular conditions.