Genomic structural equation modeling reveals novel genetic loci and pathways in panvascular disease
TL;DR
This study presents for the first time a comprehensive genetic landscape of panvascular disease, through GWAS of a phenotype not directly measured, by leveraging genomic structural equation modeling and a variety of post-GWAS methods.
Abstract
The genetic architecture underlying panvascular disease-related traits remains poorly understood. By leveraging genomic structural equation modeling (Genomic SEM) and a variety of post-GWAS methods, we estimated causal single-nucleotide polymorphisms (SNPs) that are independent of panvascular disease variants and identified a total of 2799 genome-wide significant loci. We applied multiple transcriptome-wide association methods to identify highly correlated susceptibility gene signals and associated elements with panvascular disease from tissues, cells, and genomic elements. Subsequently, we assessed the genetic correlations across phenotypes to evaluate panvascular disease-related susceptibility factors. Additionally, polygenic scores based on summary data have been utilized to analyze evidence of panvascular disease risk across different chromosomes. Our study, through GWAS of a phenotype not directly measured, presents for the first time a comprehensive genetic landscape of panvascular disease.