Integrative analysis prioritizes the relevant genes and risk factors for chronic venous disease.

Objective: Chronic venous disease (CVD) refers to a range of symptoms resulting from long-term morphological and functional abnormalities of the venous system. However, the mechanism of CVD development remains largely unknown. Here, we aim to provide more information on CVD pathogenesis, prevention strategies, and therapy development through the integrative analysis of large-scale genetic data.

Methods: Genetic data were obtained from publicly accessible databases. We used different approaches, including Functional Mapping and Annotation, DEPICT, Sherlock, SMR/HEIDIS, DEPICT, and NetWAS to identify possible causal genes for CVD. Candidate genes were prioritized to further literature-based review. The differential expression of prioritized genes was validated by microarray from the Gene Expression Omnibus, a public genomics data repository and real-time quantitative polymerase chain reaction of varicose vein specimens. The causal relationships between risk factors and CVD were assessed using the two-sample Mendelian randomization approach.

Results: We identified 46 lead single-nucleotide polymorphisms and 26 plausible causal genes for CVD. Microarray data indicated differential expression of possible causal genes in CVD when compared with controls. The expression levels of WDR92, RSPO3, LIMA, ABCB10, DNAJC7, C1S, and CXCL1 were significantly downregulated (P < .05). PHLDA1 and SERPINE1 were significantly upregulated (P < .05). Dysregulated expression of WDR92, RSPO3, and CASZ1 was also found in varicose vein specimens by quantitative polymerase chain reaction. Two-sample Mendelian randomization suggested causative effects of body mass index (odds ratio [OR], 1.008; 95% confidence interval [CI], 1.005-1.010), standing height (OR, 1.009; 95% CI, 1.007-1.011), college degree (OR, 0.983; 95% CI, 0.991-0.976), insulin (OR, 0.858; 95% CI, 0.794-0.928), and metformin (OR, 0.944; 95% CI, 0.904-0.985) on CVD.

Conclusions: Our study integrates genetic and gene expression data to make an effective risk gene prediction and etiological inferences for CVD. Prioritized candidate genes provide more insights into CVD pathogenesis, and the causative effects of risk factors on CVD that deserve further investigation.