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Abstract: PO2432

Rare Variant Analyses in 171,172 UK Biobank Participants Reveals Novel Genetic Associations with Renal Function and Kidney Diseases

Session Information

Category: CKD (Non-Dialysis)

  • 2101 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention

Authors

  • Li, Shuwei, Janssen Research and Development LLC, Spring House, Pennsylvania, United States
  • Mautz, Brian, Janssen Research and Development LLC, Spring House, Pennsylvania, United States
  • Park, Jonghanne, Janssen Research and Development LLC, Spring House, Pennsylvania, United States
  • Makimura, Hideo, Janssen Research and Development LLC, Spring House, Pennsylvania, United States
  • Breyer, Matthew Douglas, Janssen Research and Development LLC, Spring House, Pennsylvania, United States
  • Reilly, Dermot F., Janssen Research and Development LLC, Spring House, Pennsylvania, United States
  • Black, Mary Helen, Janssen Research and Development LLC, Spring House, Pennsylvania, United States
Background

GWASs have identified hundreds of common genetic variants associated with chronic kidney disease (CKD), but the burden of rare loss-of-function (LoF) or pathogenic/likely pathogenic (P/LP) variants has not been systematically examined.

Methods

We tested gene-based and variant-level association for 5 renal biomarkers (Glomerular Filtration Rate estimated from serum creatinine and/or cystatin-C, Blood Urea Nitrogen, Urine Albumin-to-Creatinine Ratio) measured at enrollment and kidney-related diseases (e.g. End-Stage Renal Disease and stage4/5 CKD, CKD defined by biomarker and/or diagnosis from NHS data, Cystic kidney disease and Renal calculi) in 171,172 UK Biobank participants of genetically assessed European ancestry and with whole exome sequencing (WES). For each trait, we fit a genome-wide regression model and tested for association using REGENIE V2.0, adjusting for age, sex, 10 ancestry PCs, assessment center, and BMI where appropriate. For gene-based analyses, we generated 15 models to collapse ClinVar-classified P/LP, putative LoF and deleterious variants predicted by 16 in silico scores (SIFT, Polyphen, BayesDel, etc.) from dbNSFP 4.1c.

Results

We identified 33 and 18 genes associated with ≥2 biomarkers and ≥1 kidney diseases across collapsing models (FDR<0.05), respectively. PKD1/2, COL4A3/4, CUBN, IFT140 were associated with both biomarkers and kidney diseases. Association analyses also highlighted genes including: COL4A1, CST3, LAMC1, LRP2, SLC22A2, SLC34A3 and SH2B3. Variant-level analyses further informed impact on protein, e.g. the SLC22A2 association signal was mainly driven by a frameshift (rs8177505) with lowering effects on eGFR (p=1e-27, beta=-6.2, MAF=0.12%). The exome-wide variant analyses revealed 29 genes (eg. UMOD) with variant associations (p<5e-8) with >3 biomarkers or ≥1 endpoint, including 2 that were also implicated from gene-based analyses (COL4A4 and CUBN).

Conclusion

This large-scale study elucidates the genetic landscape of kidney diseases. Our findings validate established genes and reveal novel genetic associations with renal function and kidney diseases.

Funding

  • Commercial Support –