Disentangling shared genetic etiologies for kidney function and cardiovascular diseases

Abstract

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, with chronic kidney disease (CKD) identified as a significant risk factor. CKD is primarily monitored through the estimated glomerular filtration rate (eGFR), calculated using the CKD-EPI equation. Although epidemiological and clinical studies have consistently demonstrated strong associations between eGFR and CVDs, the genetic underpinnings of this relationship remain elusive. Recent genome-wide association studies (GWAS) have highlighted the polygenic nature of these conditions and identified several risk loci correlating with their cross-phenotypes. Nonetheless, the extent and pattern of their pleiotropic effects have yet to be fully elucidated. We analyzed the most comprehensive GWAS summary statistics, involving around 7.5 million individuals, to investigate the shared genetic architectures and the underlying mechanisms between eGFR and CVDs, focusing on single nucleotide polymorphisms (SNPs), genes, biological pathways, and proteins exhibiting pleiotropic effects. Our study identified 508 distinct genomic locations associated with pleiotropic effects across multiple traits, involving 379 unique genes, notably L3MBTL3 (6q23.1), MMP24 (20q11.22), and ABO (9q34.2). Additionally, pathways such as stem cell population maintenance and the glutathione metabolism pathway were pivotal in mediating the relationships between these traits. From the perspective of vertical pleiotropy, our findings suggest a causal relationship between eGFR and conditions such as atrial fibrillation and venous thromboembolism. These insights significantly enhance our understanding of the genetic links between eGFR and CVDs, potentially guiding the development of novel therapeutic strategies and improving the clinical management of these conditions.