A clustering approach to improve our understanding of the genetic and phenotypic complexity of chronic kidney disease.

ABSTRACT: 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 sign

SUBMITTER: Eoli A 

PROVIDER: S-EPMC10593036 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature