Kidney Week

Abstract: PO1200

KidneyNetwork Uses Kidney-Derived Gene Expression Data to Predict and Prioritize Novel Genes Involved in Kidney Disease

Session Information

• Cystic Kidney Disease - I
  November 04, 2021 | Location: On-Demand, Virtual Only
  Abstract Time: 10:00 AM - 12:00 PM

Category: Genetic Diseases of the Kidneys

• 1001 Genetic Diseases of the Kidneys: Cystic

Authors

• Claus, Laura R., Department of Genetics, University Medical Center Utrecht, Utrecht, Utrecht, Netherlands

Laura R. Claus, MD, PhD

• Boulogne, Floranne, Department of Genetics, University Medical Center Groningen, Groningen, Groningen, Netherlands

Floranne Boulogne,

• Wiersma, Henry, Department of Genetics, University Medical Center Groningen, Groningen, Groningen, Netherlands

Henry Wiersma,

• Oelen, Roy, Department of Genetics, University Medical Center Groningen, Groningen, Groningen, Netherlands

Roy Oelen,

• Schukking, Floor, Department of Genetics, University Medical Center Groningen, Groningen, Groningen, Netherlands

Floor Schukking,

• de Klein, Niek, Department of Genetics, University Medical Center Groningen, Groningen, Groningen, Netherlands

Niek de Klein,

• Li, Shuang, Department of Genetics, University Medical Center Groningen, Groningen, Netherlands

Shuang Li,

• Westra, Harm-Jan, Department of Genetics, University Medical Center Groningen, Groningen, Groningen, Netherlands

Harm-Jan Westra,

• Van der zwaag, Bert, Department of Genetics, University Medical Center Utrecht, Utrecht, Utrecht, Netherlands

Bert Van der zwaag,

• Sierks, Dana, Medical Department III - Endocrinology, Nephrology, Rheumatology Department of Internal Medicine, Division of Nephrology, University of Hospital Leipzig Medical Center, Leipzig, Germany

Dana Sierks,

• Schönauer, Ria, Medical Department III - Endocrinology, Nephrology, Rheumatology Department of Internal Medicine, Division of Nephrology, University of Hospital Leipzig Medical Center, Leipzig, Germany

Ria Schönauer,

• Halbritter, Jan, Medical Department III - Endocrinology, Nephrology, Rheumatology Department of Internal Medicine, Division of Nephrology, University of Hospital Leipzig Medical Center, Leipzig, Germany

Jan Halbritter,

• Knoers, Nine V., Department of Genetics, University Medical Center Groningen, Groningen, Groningen, Netherlands

Nine V. Knoers,

• Deelen, Patrick, Department of Genetics, University Medical Center Utrecht, Utrecht, Utrecht, Netherlands

Patrick Deelen,

• Franke, Lude, Department of Genetics, University Medical Center Groningen, Groningen, Groningen, Netherlands

Lude Franke,

• van Eerde, Albertien M., Department of Genetics, University Medical Center Utrecht, Utrecht, Utrecht, Netherlands

Albertien M. van Eerde,

Background

Genetic testing in patients with suspected hereditary kidney disease does not always reveal the genetic cause for the patient's disorder. Pathogenic variants can reside in genes that are not yet known to be involved in kidney disease. To help identify candidate genes for kidney disease we have developed KidneyNetwork, in which tissue-specific expression is utilized to predict kidney-specific gene functions.

Methods

KidneyNetwork is a co-expression network built upon 878 kidney RNA-sequencing samples and a multi-tissue dataset of 31,499 samples. It uses expression patterns to predict which genes have kidney-related functions and which phenotypes might result from variants in these genes. As proof of principle, we applied KidneyNetwork to prioritize rare variants in exome-sequencing data from 13 kidney disease patients.

Results

We assessed the prediction performance of KidneyNetwork by comparing it to GeneNetwork, our previously developed multi-tissue co-expression network. In KidneyNetwork, we observe significantly improved prediction accuracy of kidney-related HPO-terms and an increase in the total number of significantly predicted kidney-related HPO-terms (figure 1). Applying KidneyNetwork to exome-sequencing data allowed us to identify ALG6 as candidate gene for kidney and liver cysts.

Conclusion

KidneyNetwork is a kidney-specific co-expression network that predicts which genes have kidney-specific functions that can result in kidney disease. Gene-phenotype associations of genes unknown for kidney-related phenotypes can be predicted. We show its added value by applying it to kidney disease patients without a molecular diagnosis. KidneyNetwork can be applied to clinically unsolved cases, but it can also be used by researchers to better understand kidney physiology and pathophysiology.