Chronic kidney disease (CKD) is a complex disorder that causes a gradual loss of kidney function, affecting approximately 9.1% of the world's population. Here, we use a soft-clustering algorithm to deconstruct its genetic heterogeneity. First, we selected 322 CKD-associated independent genetic variants from published genome-wide association studies (GWAS) and added association results for 229 traits from the GWAS catalog. We then applied nonnegative matrix factorization (NMF) to discover overlapping clusters of related traits and variants. We computed cluster-specific polygenic scores and validated each cluster with a phenome-wide association study (PheWAS) on the BioMe biobank (n=31,701). NMF identified nine clusters that reflect different aspects of CKD, with the top-weighted traits signifying areas such as kidney function, type 2 diabetes (T2D), and body weight. For most clusters, the top-weighted traits were confirmed in the PheWAS analysis. Results were found to be more significant in the cross-ancestry analysis, although significant ancestry-specific associations were also identified. While all alleles were associated with a decreased kidney function, associations with CKD-related diseases (e.g., T2D) were found only for a smaller subset of variants and differed across genetic ancestry groups. Our findings leverage genetics to gain insights into the underlying biology of CKD and investigate population-specific associations.

Claudia Schurmann is a paid employee of Bayer AG, Pharmaceuticals. All other authors do not have any competing interest.

This work was supported in part through the computational and data resources and staff expertise provided by Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai and supported by the Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. Additionally, this work was supported by the Office of Research Infrastructure of the National Institutes of Health under award number S10OD026880, which allowed us to use Mount Sinai Data Warehouse (MSDW) data. Regarding HPI.MS resources, funding was provided by the Hasso Plattner Foundation (HPF). Additionally, the research leading to these results has received funding from the Horizon 2020 Programme of the European Commission under Grant Agreement No. 826117 (Smart4Health). The Mount Sinai BioMe Biobank has been supported by The Andrea and Charles Bronfman Philanthropies and in part by Federal funds from the NHLBI and NHGRI (U01HG00638001; U01HG007417; X01HL134588).

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The IRB of the Mount Sinai Health System (NYC, NY, USA) gave ethical approval for this work.

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All publicly available data (input variants, trait-variant associations) used to support the findings of this study are included in this published article (and its Supplementary Information files) and are also available from the cited publications and GWAS Catalog. Additional data generated for the analysis steps, including source code and intermediate results, are available from the corresponding author upon reasonable request. The data used to validate the findings of this study are available from BioMe biobank (https://icahn.mssm.edu/research/ipm/programs/biome-biobank), but restrictions apply to their availability. To access the data, please reach out to biomebiobank@mssm.edu.