Abstract: Chronic kidney disease (CKD) impacts around 9% of the global population and is a leading contributor to end-stage kidney disease, cardiovascular issues, and premature death. While traditional factors like diabetes and hypertension are well-known culprits, genetics plays a crucial and complex role. Genomic biobanks, large repositories of genetic and health data, have become instrumental in identifying both rare and common genetic variants linked to CKD. These resources power genome-wide association studies (GWAS) and enable the use of polygenic risk scores to predict disease susceptibility even in individuals who show no outward symptoms.
As biobanks expand globally and grow more inclusive of diverse populations, they are bridging gaps in our understanding of monogenic disorders like Alport syndrome and polycystic kidney disease. By integrating multi-omics technologies such as metabolomics and transcriptomics, and leveraging advanced AI tools, researchers are finding promising drug targets and enhancing personalized care. While challenges like data bias and privacy remain, the momentum toward precision nephrology is unmistakable, offering hope for early detection and tailored therapies.
Full article available here: Using Large Genomic Biobanks to Generate Insights into Genetic Kidney Disease – ScienceDirect