Advancing CKD Risk Prediction: Exome-Wide Studies and Rare Variant Analysis Across Big Biobanks

Columbia University Health Sciences, New York NY

Investigators

Abstract

Project Summary/Abstract Chronic kidney disease (CKD) affects 10-13% of the global population, contributing to mortality, morbidity, and high public health costs. The polygenic architecture of CKD has been investigated using genetic data generated in large population-based cohorts. Genome-wide association studies (GWAS) of cross-sectional eGFR (estimated glomerular filtration rate) have identified hundreds of variants associated with renal function. One way that GWAS results are clinically translated involves the development of genome-wide polygenic scores (GPS), which aggregate the effects of multiple GWAS variants to assess an individual's disease risk. However, a major limitation of this approach is that the impact of rare protein-coding variants, which tend to have much larger effects on biology and disease risk, is not detectable by GWAS. Therefore, exome- wide rare variant association studies are needed to improve the predictive performance of GPS and better understand the impact of protein-coding genetic variants on kidney function. This proposal aims to perform large-scale rare variant analyses involving 1 million participants by leveraging phenotypic and genetic data from major biobanks, including the UK Biobank, All of Us (AoU), Genomics England (GE), and the Pakistan Genome Resource (PGR). In Aim 1, we will conduct EXWAS for kidney function across these biobanks, identifying rare variants and genes associated with CKD while uncovering genetic loci missed by traditional GWAS. In Aim 2, we will develop a rare variant burden score and integrate it with the existing CKD GPS based on GWAS. We will test whether the GPS modifies the effect of the rare variant burden score to improve the overall predictive performance of GPS in CKD. After completion, this project will provide deeper insights into kidney function's genetic architecture, uncover novel mechanisms underlying CKD, and enhance the risk prediction of CKD.