Identification of multi-omic pleiotropy factors for peripheral artery disease

Abstract Background: Peripheral artery disease (PAD) is prevalent and frequently co-occurs with type 2 diabetes (T2D) and coronary artery disease (CAD). Although shared genetic factors may contribute to these comorbidities, few studies have examined pleiotropy at the transcriptomic and proteomic levels. Methods: We generated summary statistics for transcriptome-wide (TWAS) and proteome-wide (PWAS) association analyses across PAD, T2D, and CAD. Joint tests for pleiotropy at the variant, transcript, and protein levels were performed using PLACO, and the contributions of these pleiotropic factors to genetic correlations were quantified. Furthermore, we developed pleiotropy models integrating variants, transcripts, and proteins. Results: In the PAD-T2D analysis, we identified 5 SNPs, 2 predicted transcripts, and 2 predicted proteins that together contributed 2.8% to the genome-wide genetic covariance. In the PAD-CAD analysis, 33 SNPs, 2 predicted transcripts, and 2 predicted proteins accounted for 9.15% of the genetic covariance. Overall, eight pleiotropy models were established, providing a multi-layered framework for categorizing our findings. Conclusion: Employing a multi-omic framework, this study elucidates the shared genetic architecture of PAD with T2D and CAD. Our findings enhance the understanding of genetic risk factors underlying PAD and its comorbidities, with potential implications for future therapeutic strategies.