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

Abstract

Abstract Genetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the disorder as potentially pathogenic variants can reside in genes that are not yet known to be involved in kidney disease. To help identify these genes, we have developed KidneyNetwork, that utilizes tissue-specific expression to predict kidney-specific gene functions.KidneyNetwork is a novel method that we used to enrich a kidney RNA-sequencing co-expression network of 878 samples with a multi-tissue network of 31,499 samples. It then uses expression patterns to predict which genes have a kidney-related function and which (disease) phenotypes might result from variants in these genes, based on established gene-phenotype associations. We applied KidneyNetwork to prioritize rare variants in exome sequencing data from 13 kidney disease patients without a genetic diagnosis.KidneyNetwork can accurately predict kidney-specific gene functions and (kidney disease) phenotypes for disease-associated genes. Applying it to exome sequencing data of kidney disease patients allowed us to highlight a convincing candidate gene for kidney and liver cysts: ALG6.We present KidneyNetwork, a kidney-specific co-expression network that accurately predicts which genes have kidney-specific functions and can result in kidney disease. We show the added value of KidneyNetwork by applying it to kidney disease patients without a molecular diagnosis and consequently, we propose ALG6 as candidate gene in one of these patients. We designed an easy-to-use online interface that allows clinicians and researchers to use gene expression and co-regulation data and gene-phenotype connections to accelerate advances in hereditary kidney disease diagnosis and research.