Abstract: PO1366
Mapping Genomic Regulation of Kidney Diseases and Traits at a Cell Type and Variant Level of Specificity
Session Information
- Genetic Diseases of the Kidneys: Non-Cystic - II
November 04, 2021 | Location: On-Demand, Virtual Only
Abstract Time: 10:00 AM - 12:00 PM
Category: Genetic Diseases of the Kidneys
- 1002 Genetic Diseases of the Kidneys: Non-Cystic
Authors
- Benway, Christopher, Boston Children's Hospital, Boston, Massachusetts, United States
- Mcnulty, Michelle, Boston Children's Hospital, Boston, Massachusetts, United States
- Han, Seong Kyu, Boston Children's Hospital, Boston, Massachusetts, United States
- Lee, Dongwon, Boston Children's Hospital, Boston, Massachusetts, United States
- Sampson, Matt G., Boston Children's Hospital, Boston, Massachusetts, United States
Group or Team Name
- Nephrotic Syndrome Study Network (NEPTUNE) Consortium
Background
Although numerous genetically associated loci for kidney function and disease have been identified by genome-wide association studies (GWAS), determining the causal genes and functional variants remains a major challenge. Integration of GWAS results with other data types (such as expression quantitative trait loci [eQTLs]) can help identify causal and functional variants in a tissue- or cell-type specific manner. Further, analysis of disease tissue may uncover context-specific associations that may otherwise not be detectable.
Methods
We integrated eQTL data from micro-dissected glomerular (n = 240) and tubulointerstitial (TI) (n = 311) transcriptomes from individuals with nephrotic syndrome and summary statistics from two large trans-ethnic GWAS meta-analyses for estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (UACR). We applied a Bayesian statistical framework for eQTL discovery and multi-SNP fine-mapping (TORUS/DAP). eQTL signals from each renal compartment were integrated with summary statistics to perform a gene-level probabilistic transcriptome-wide association study (PTWAS) and SNP-level co-localization (fastENLOC).
Results
We identified 5,526 glomerular and 9,742 TI eQTLs at < 5% FDR level. For eGFR, we identified 971 gene-trait pairs in the glomerulus and 1,816 gene-trait pairs in TI tissue that were significant (FDR < 5%). For UACR, we identified 194 and 340 significant gene-trait pairs in the glomerulus and TI tissue, respectively. In the SNP-level co-localization, we identified 46 TI and 3 glomerular co-localization signals (regional co-localization probability [RCP] > 50%) for eGFR, including known associations with UMOD and FGF5 expression, as well as novel associations to LARP4B and RRAGD which can be attributed to single variants. We identified 7 TI and 16 glomerular co-localization signals (RCP > 50%) for UACR. In addition to replicating co-localization signals at PRKCI and TGFB1 in glomerular tissue, we refined the co-localization signal at PTH1R to a single variant, rs6787229, which also co-localized with expression of MYL3.
Conclusion
Profiling and integrating renal compartment-specific eQTLs with kidney trait GWAS results in a probabalistic framework identified novel gene-trait associations and refined many known associations to a single variant.
Funding
- NIDDK Support