Kidney-compartment genomic prioritization across diabetic and population kidney traits: an exploratory Mendelian randomization and colocalization study

Introduction Kidney-trait regulatory signals are difficult to prioritize because diabetes-specific and population-wide outcomes, kidney‐compartment eQTL resources, progression phenotypes, disease-state expression data, and locus-level colocalization are usually evaluated separately. Methods We assembled an exploratory public-data framework integrating CKDGen, KP4CD, GTEx v10, Susztak/NephQTL, Human Protein Atlas, INTERVAL, Open Targets, and kidney disease single‐cell resources across estimated glomerular filtration rate (eGFR), urinary albumin‐to‐creatinine ratio (UACR), and progression phenotypes. Single-SNP Wald ratios were treated as screening estimates, with primary within-outcome FDR and a study‐wide BH-FDR sensitivity analysis across 597,099 screening rows. Single‐cell, external expression, protein, and tractability resources were used as biological context rather than validation. Results GTEx exposure quality control retained 209,031 kidney‐cortex and 2,744,871 whole‐blood significant variant‐gene pairs; outcome‐specific overlap and harmonization yielded 31‐2,232 screening pairs per displayed status outcome. Only UMOD had FDR-level support from diabetes-specific outcomes and general kidney traits. PRKCI and WIPF3 showed robust single-signal approximate Bayes factor colocalization with overall UACR, whereas ITIH4, LINC02166, and A3GALT2 were downgraded because of prior sensitivity or diffuse local patterns. Discussion Therefore, the framework prioritizes loci for mechanistic follow‐up while avoiding claims of definitive causal Mendelian randomization, diabetic kidney disease specificity for general-trait signals, or therapeutic readiness.