KidneyNetwork: Using kidney-derived gene expression data to predict and prioritize novel genes involved in kidney disease

• DOI: 10.1101/2021.03.10.21253054
• Publication Date: 2021-03-17T12:25:13Z
• Authors: Floranne Boulogne; Laura R. Claus; Henry Wiersma; Roy Oelen; Floor Schukking; Niek de Klein; Shuang Li; Harm-Jan Westra; Bert van der Zwaag; Franka van Reekum; Dana Sierks; Ria Schönauer; Jan Halbritter; Nine V.A.M. Knoers; ; Patrick Deelen; Lude Franke; Albertien M. van ...
• Keywords: Candidate gene

Abstract Background Genetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for disorder as potentially pathogenic variants can reside genes that are yet known to be involved disease. To help identify these genes, we have developed KidneyNetwork, utilizes tissue-specific expression predict kidney-specific gene functions. Methods KidneyNetwork is a co-expression network built upon combination of 878 RNA-sequencing samples and multi-tissue dataset 31,499 samples. It uses patterns which kidney-related function (disease) phenotypes might result from genes. We applied prioritize rare exome sequencing data 13 without diagnosis. Results accurately functions (kidney disease) disease-associated Applying it allowed us promising candidate liver cysts: ALG6 . Conclusion present predicts show added value by applying molecular diagnosis consequently, propose one patients. clinically unsolved cases, but also used researchers gain insight into individual order better understand physiology pathophysiology. Significance statement patient’s disorder. Potentially disease, making difficult interpret relevance variants. This reveals clear need methods phenotypic consequences variation an unbiased manner. Here describe tool group undiagnosed cases identified cystic In summary, aid interpretation therefore translational nephrogenetics improve diagnostic yield