medRxiv : the preprint server for health sciences · 2026
Atlas of glomerular disease-specific genetic effects on blood transcriptome.
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Liu Lili, Wang Chen, Kravets Oleksandr, Fermin Damian, Eichinger Felix, Zanoni Francesca, Khan Atlas, Zhang Jun Y, Ouyang Yan, Li Qin, Hamilton Patrick, Kalra Philip A, Chinnadurai Rajkumar, Reidy Kimberly, Kopp Jeffrey, Mucha Krzysztof, Smith Cathy, Smith Abigail R, Mcnulty Michelle, Eddy Sean, Nair Viji, Helmuth Margaret E, Vasylyeva Tetyana L, Smoyer William E, Berthier Celine, Parekh Rulan, Wenderfer Scott E, Onugha Elizabeth, Martin Tess, Sokolova Ksenia, Sealfon Rachel S G, Theesfeld Chandra L, Parsa Afshin, Gbadegesin Rasheed, Sampson Matthew, Sanna-Cherchi Simone, Troyanskaya Olga G, Paul Dirk S, Petrovski Slave, Goldstein David, Mariani Laura Heyns, Gharavi Ali, Columbia Genomics Consortium, NEPTUNE Consortium, CureGN Consortium, Kretzler Matthias, Kiryluk Krzysztof
Аннотация
IgA nephropathy (IgAN), IgA vasculitis (IgAV), focal segmental glomerulosclerosis (FSGS), membranous nephropathy (MN), and minimal change disease (MCD) account for the majority of idiopathic glomerulo-nephropathies (GN). 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<0.05 in at least one GN type. Approximately 5-10% of the QTL signals were unique to a specific GN type, while ~90% were shared between at least two conditions. Colocalization analysis demonstrated that ~80% of shared eGenes between traits also shared the same causal variants, whereas ~2% 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.