Identification of potentially common loci between childhood obesity and coronary artery disease using pleiotropic approaches

Scientific reports(2022)

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摘要
Childhood obesity remains one of the most important issues in global health, which is implicated in many chronic diseases. Converging evidence suggests that a higher body mass index during childhood (CBMI) is significantly associated with increased coronary artery disease (CAD) susceptibility in adulthood, which may partly arise from the shared genetic determination. Despite genome-wide association studies (GWASs) have successfully identified some loci associated with CBMI and CAD individually, the genetic overlap and common biological mechanism between them remains largely unexplored. Here, relying on the results from the two large-scale GWASs ( n = 35,668 for CBMI and n = 547,261 for CAD), linkage disequilibrium score regression (LDSC) was used to estimate the genetic correlation of CBMI and CAD in the first step. Then, we applied different pleiotropy-informed methods including conditional false discovery rate ( cFDR ) and genetic analysis incorporating pleiotropy and annotation (GPA) to detect potentially common loci for childhood obesity and CAD. By integrating the genetic information from the existing GWASs summary statistics, we found a significant positive genetic correlation ( r_g = 0.127, p = 2E−4) and strong pleiotropic enrichment between CBMI and CAD ( LRT = 79.352, p = 5.2E−19). Importantly, 28 loci were simultaneously discovered to be associated with CBMI, and 13 of them were identified as potentially pleiotropic loci by cFDR and GPA. Those corresponding pleiotropic genes were enriched in trait-associated gene ontology (GO) terms “amino sugar catabolic process”, “regulation of fat cell differentiation” and “synaptic transmission”. Overall, the findings of the pleiotropic loci will help to further elucidate the common molecular mechanisms underlying the association of childhood obesity and CAD, and provide a theoretical direction for early disease prevention and potential therapeutic targets.
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Cardiology,Computational biology and bioinformatics,Data integration,Genetics,Genome informatics,Science,Humanities and Social Sciences,multidisciplinary
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