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Abstract: FR-PO111

Kidney Genome Atlas: A Whole-Genome Landscape of More Than 2,000 Kidney Disease Patients

Session Information

Category: CKD (Non-Dialysis)

  • 1903 CKD (Non-Dialysis): Mechanisms

Authors

  • Zhang, Wei, Goldfinch Bio, Cambridge, Massachusetts, United States
  • Soare, Thomas, Goldfinch Bio, Cambridge, Massachusetts, United States
  • Tebbe, Adam, Goldfinch Bio, Cambridge, Massachusetts, United States
  • Walsh, Liron, Goldfinch Bio, Cambridge, Massachusetts, United States
  • Kretzler, Matthias, U.Michigan, Ann Arbor, Michigan, United States
  • Nadkarni, Girish N., Ichan School of Medicine, New York, New York, United States
  • Gbadegesin, Rasheed A., Duke University Medical Center, Durham, North Carolina, United States
  • Wenke, Jamie L., Nashville Biosciences, Nashville, Tennessee, United States
  • Mundel, Peter H., Goldfinch Bio, Cambridge, Massachusetts, United States
  • Tibbitts, Thomas T., Goldfinch Bio, Cambridge, Massachusetts, United States
Background

Chronic kidney disease (CKD) is a heterogeneous disease affecting more than 30 million people in the US, which is about 10% of the US population. Despite the urgent need for targeted therapeutics, the understanding of the mechanistic basis of CKD including the genetic variants that potentially drive it has lagged other diseases (e.g. cancer) for decades.

Methods

Here we report the generation and initial analysis of the Kidney Genome Atlas (KGA), the world’s largest whole-genome landscape of individuals with molecularly defined kidney diseases. Focusing initially on focal segmental glomerulosclerosis (FSGS), related disorders, and diabetic kidney disease (DKD), KGA contains whole genome sequences (>30X coverage) from more than 2,000 patients and 2,500 matched healthy controls. Each genome is linked to longitudinal clinical records and for a subset of 500 patients, the atlas also includes matched transcriptomic data from microdissected glomerular and tubulointerstitial samples.

Results

The KGA has enabled the discovery of genetic variants associated with kidney disease and the integration of genomic and transcriptomic data to identify kidney disease-specific expression quantitative trait loci (eQTLs). The atlas also provides the foundation for establishing the relationships between genetic variants, histologic diagnoses, and quantitative clinical phenotypes of kidney function and disease progression. Computational integration of these datasets will enable the prioritization of candidate variants with putative disease modulating effect. Investigating and validating biological pathways derived from these analyses can also be used to stratify patients into subtypes most likely to respond to specific targeted therapies.

Conclusion

The KGA is a valuable resource fueling our understanding of the molecular mechanisms of kidney diseases at the whole genome scale.

Funding

  • Commercial Support