Kidney Week

Abstract: FR-OR45

Combined Genome and Transcriptome Sequencing of the CureGN Study Generates Comprehensive Maps of Glomerulonephritis-Specific Blood Expression Quantitative Trait Loci (eQTL) Effects

Session Information

• Glomerular Diseases: Mechanisms and More
  October 25, 2024 | Location: Room 1, Convention Center
  Abstract Time: 05:50 PM - 06:00 PM

Category: Glomerular Diseases

• 1401 Glomerular Diseases: Mechanisms, including Podocyte Biology

Authors

• Liu, Lili, Columbia University, New York, New York, United States

Lili Liu, PhD

• Wang, Chen, Columbia University, New York, New York, United States

Chen Wang, PhD

• Eichinger, Felix H., University of Michigan, Ann Arbor, Michigan, United States

Felix H. Eichinger

• Fermin, Damian, University of Michigan, Ann Arbor, Michigan, United States

Damian Fermin, PhD

• Sanna-Cherchi, Simone, Columbia University, New York, New York, United States

Simone Sanna-Cherchi, MD

• Sampson, Matt G., Harvard Medical School, Boston, Massachusetts, United States

Matt G. Sampson, MD

• Gbadegesin, Rasheed A., Duke University, Durham, North Carolina, United States

Rasheed A. Gbadegesin, MD, MBBS, FASN

• Gharavi, Ali G., Columbia University, New York, New York, United States

Ali G. Gharavi, MD

• Kretzler, Matthias, University of Michigan, Ann Arbor, Michigan, United States

Matthias Kretzler, MD

• Kiryluk, Krzysztof, Columbia University, New York, New York, United States

Krzysztof Kiryluk, MD

Background

IgA nephropathy (IgAN), focal segmental glomerulosclerosis (FSGS), membranous nephropathy (MN), and minimal change disease (MCD) account for the majority of idiopathic glomerulopathies (GN). GWASes started to delineate their genetic mechanisms, but there are no adequately powered transcriptomic datasets coupled to genetic data to investigate functional consequences of identified risk alleles in different GN forms.

Methods

We performed 30x whole genome sequencing (WGS) coupled with blood RNA-seq on the CureGN cohort. In total, 1826 participants had high quality WGS and RNA-seq data for analysis. The bulk gene expression profiles were deconvolved into 6 major immune cell types using CIBERSOFTx. We then generated transcriptome-wide maps of eQTL effects for FSGS (N=450), IgAN (N=403), IgA vasculitis (N=123), MCD (N=408) and MN (N=442). Cis- and trans-eQTL signals for each condition were defined using linear regression in tensorQTL, adjusting for age, sex, genetic PCs, PEER factors. The analyses were performed with and without additional adjustment for immune cell fractions. We then performed interaction eQTL mapping for each cell type as well as GN type.

Results

We identified 13,115 unique cis-eGenes (N_FSGS=10,004, N_IgAN=9,589, N_IgAV=3,757, N_MCD=9,686, and N_MN=9,899). About 5% of eGenes were unique to one phenotype, and 95% were shared between at least two conditions. For trans-eQTLs, 645 trans-eGenes were detected (N_FSGS=298, N_IgAN=136, N_IgAV=47, N_MCD=229, and N_MN=215). Interestingly, a larger proportion of trans-eGenes was disease-specific compared to cis-signals. After additionally adjusting for cell fractions, we gained extra 279 cis-eGenes and 70 trans-eGenes, but lost 362 cis-eGenes and 114 trans-eGenes. By interaction eQTL mapping, we further refined a set of cis-signals specific to each cell type and each GN form. These data were then used to prioritize candidate causal genes at the GN risk loci identified in the latest GWASes.

Conclusion

Using the CureGN dataset, we identified immune cell type-specific and GN-context-specific genomic regulators for gene expression by blood eQTL mapping. Our comprehensive eQTL maps provide a powerful resource for integrative gene discovery studies for primary GN.