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These disorders involve immune system dysregulation and have a complex genetic architecture. Currently, there are no adequately powered blood transcriptomic datasets coupled to genetic data from patients with GN that can delineate disease-context specific genetic effects on the blood immune cell transcriptome. We performed whole genome sequencing coupled with bulk blood transcriptome sequencing on 1,822 participants from the CureGN study, a prospective cohort of participants with a kidney biopsy diagnosis of primary GN. We generated disease-context specific transcriptome-wide maps of gene expression QTL (eQTL), splicing QTL (sQTL), and double strand RNA-editing QTL (edQTL) for FSGS (N=447), IgAN (N=403), IgAV (N=123), MCD (N=408), and MN (N=441), as well as cross-disease maps for all 1,822 participants. Our QTL mapping identified 16,068 eGenes, 4,644 sGenes and 4,611 edQTLs with an FDR&lt;0.05 in at least one GN type. Approximately 5-10% of the QTL signals were unique to a specific GN type, while \u223c90% were shared between at least two conditions. Colocalization analysis demonstrated that \u223c80% of shared eGenes between traits also shared the same causal variants, whereas \u223c2% had distinct causal variants, suggesting context-specific regulatory effects. Cross-phenotype QTL mapping uncovered 6,466 eGenes, 2,705 sGenes and 5,321 edQTLs not previously detected in GTEx. Age, eGFR, and proteinuria-interaction QTL analyses identified hundreds of loci modified by age or disease severity. Lastly, integrative analyses with GWAS nominated new candidate genes for each of the five GN types under study. In summary, we generated comprehensive maps of GN-context-specific genetic effects on the blood transcriptome, providing a powerful new resource for integrative gene discovery studies of primary GN.<\/jats:p>","DOI":"10.64898\/2026.06.22.26356281","type":"posted-content","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T02:40:12Z","timestamp":1782355212000},"source":"Crossref","is-referenced-by-count":0,"title":["Atlas of glomerular disease-specific genetic effects on blood transcriptome"],"prefix":"10.64898","author":[{"given":"Lili","family":"Liu","sequence":"first","affiliation":[{"name":"Division of Nephrology, Department of Medicine, Columbia University, College of Physicians & Surgeons, New York, NY, USA"},{"name":"Division of Nephrology, Department of Medicine, University of Michigan, Ann Arbor, MI, USA"},{"name":"Department of Computational medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 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