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Abstract: FR-PO041

Characterizing the Genetic Architecture of Rare Glomerulonephropathy Disease: The Landscape of RNA Binding Protein Dysregulation

Session Information

Category: Augmented Intelligence, Digital Health, and Data Science

  • 300 Augmented Intelligence, Digital Health, and Data Science

Authors

  • Marvin, Tess A., Princeton University Princeton Center for Quantitative Biology Lewis-Sigler Institute for Integrative Genomics, Princeton, New Jersey, United States
  • Litman, Aviya, Princeton University Princeton Center for Quantitative Biology Lewis-Sigler Institute for Integrative Genomics, Princeton, New Jersey, United States
  • Wang, Chen, Columbia University Vagelos College of Physicians and Surgeons, New York, New York, United States
  • Theesfeld, Chandra L., Princeton University Princeton Center for Quantitative Biology Lewis-Sigler Institute for Integrative Genomics, Princeton, New Jersey, United States
  • Mariani, Laura H., University of Michigan Department of Internal Medicine, Ann Arbor, Michigan, United States
  • Kiryluk, Krzysztof, Columbia University Vagelos College of Physicians and Surgeons, New York, New York, United States
  • Kretzler, Matthias, University of Michigan Department of Internal Medicine, Ann Arbor, Michigan, United States
  • Troyanskaya, Olga, Princeton University, Princeton, New Jersey, United States

Group or Team Name

  • CureGN Genetics.
Background

The majority of primary glomerular nephropathy (GN) cases present with either immunoglobulin A nephropathy, focal segmental glomerulosclerosis, membranous nephropathy, and minimal change disease. Despite overlapping presentations, each diagnosis may represent many distinct disease subtypes with unique etiologies. Even as most disease risk is concentrated in a small number of genes, most patients do not carry syndromic mutations in GN-associated genes. Regulatory mutations that alter the magnitude or spatiotemporal aspect of expression may contribute to the genetic architecture of disease. RNA binding proteins (RBPs) regulate the life cycle of the RNA molecule (e.g. alternative splicing). Here we employ a genome-wide analysis of >67 million mutations in WGS of GN patients from the Cure Glomerulonephropathy (CureGN) cohort using a set of RBPs and their target sites to characterize the role RBPs play in the GN disease.

Methods

To assess the pathogenic contribution of RBP dysregulation to GN, we identified the functional impact of GN patient variants on RBP binding profiles using Seqweaver, a deep learning framework for predicting variant dysregulation for over 200 RNA-binding protein models with single nucleotide sensitivity. To determine if there are global signals of RBP dysregulation across the CureGN cohort, we identified trends in RBP disease impact scores across the patient variants. A pipeline was designed to select high-impact variants that are disease specific to identify mechanisms of disease. Beyond looking at individual genes and variants in isolation, we utilized a network-based approach to nominate active disease processes at a pathway level.

Results

We observed genes enriched for predicted high-impact variants from different patients (e.g. VAV3), and together, the rare variants associated with GN GWAS genes had significantly higher average disease impact than control variants associated with all other genes. Importantly, we also observed significant enrichment of RBP impact in kidney cell types. Seqweaver variant predictions provide hypotheses for biochemical mechanisms that explain GWAS association signals.

Conclusion

These findings suggest that RBP regulatory mutations of known kidney disease genes may harbor substantial disease risk.

Funding

  • NIDDK Support